AGV发展历史和协同搬运的背景

随着科技的迅猛发展，传统雷弹技术保障系统在贮存运输、人员要求、协调难度以及应对信息化作战需求等方面暴露出诸多问题。本研究提出一种基于智能仓储AGV技术的雷弹信息化技术保障系统，详细阐述其优势，包括商业化应用成熟、保障标准稳定且安全等特点。深入分析该系统的构建，涵盖传统保障系统模型与基于AGV技术的新型保障系统模型对比。最后对未来发展方向进行展望，指出无人化保障将实现智能化升级、保障结构扁平化以及具备更高鲁棒性特质，为雷弹技术保障在信息化作战条件下的发展提供了重要的理论与实践参考。

通过对工业革命发展类人比较分析，得出了前四次工业革命的每一次都是以机器(广义机器)衍生出类人某种重要器官肌能的机器机能为标志，使得各种机器不断转型升级和广泛应用，从而形成工业X.0的发展规律。采用工业发展的这一规律，推论出类人认知学习能力的机器学习机能将引发第五次工业革命，诞生各种学习机能机器和广泛应用，即为工业5.0，并研究定义了工业5.0机器机能的定义。工业发展的这一规律为未来工业发展重点乃至科学研究方向提供了战略性的理论依据。依据工业5.0机器机能的定义，我们研制成功可体现工业5.0特征的一种群机器人智动化作业系统模型，为工业5.0智动化系统的关键技术研究奠定了试验基础和模型示范。

本文综述了自动化的过去、现在和未来，叙述了手动化、自动化和智动化的发展过程，给出了三者的内涵比较说明，给出了智动化生产线的基本特征以及与自动化的对照比较，简介了智动化生产线涉及的理论技术，介绍了智动化生产线仿真教学实验平台特点及实现的智动化功能，展望了以国内外品牌工业机器人及其智能化离线编程软件集成实现基于工业机器人智动化生产线仿真系统及前景。

信息技术迅猛发展，企业信息化与智能化融合成为推动企业转型升级的核心动力。本研究以京东物流为案例，综合运用文献研究法与案例分析法展开探究。京东物流的信息化历经启蒙探索、局部数字化、全面贯通与协同、深度集成与拓展四个阶段，成功实现向智能化的跨越。在智能化实践中，智能仓储、运输、配送及客服售后等方面成果突出，有效提升运营效率、降低成本、优化服务。其融合路径在技术层面，多种前沿技术深度嵌入；业务上实现各环节智能化升级；产业生态方面推动多方协同发展。京东物流的实践为物流行业发展提供了重要借鉴，但随着技术和市场变化，仍需在新兴技术应用、全球市场拓展等方面持续努力，以更好地应对挑战、把握机遇。

随着国内经济迅猛发展，越来越多的人开始转向机器人行业的研究中来。各个国家逐渐将搬运机器人应用到工业活动当中，这样不仅能够大幅度的提升产品的质量，还能够减少劳动力的投入，进而节约总体成本。由此可见，机器人技术的发展对社会而言至关重要。因此，如果一个国家想要提升综合创新能力，那么必须要重视机器人技术的发展。该篇文章主要阐述了搬运机器人国内外的发展现状和相应的路径规划算法。同时，还就现阶段搬运机器人研究中所存在的问题和不足，提出几点具体可行的方法措施。

为解决传统短距无线技术在智能汽车、工业控制等场景下面临的高时延与低可靠性问题，即“确定性鸿沟”，本文对新一代星闪(NearLink)无线通信技术进行了系统性分析。文章首先剖析了星闪为实现确定性通信所采用的核心技术，包括其集中式调度机制、SLB (SparkLink Basic)/SLE (SparkLink Low Energy)双模接入架构以及集成了Polar码和HARQ (Hybrid Automatic Repeat reQuest)机制的物理层设计。分析结果表明，星闪通过上述设计，在关键性能上取得了显著提升，其空口时延低于20 µs，传输可靠性高于99.999%，峰值速率可达920 Mbps。目前，星闪技术已在消费电子、汽车电子、工业控制等领域得到初步商业应用，相关产业链与生态系统正在构建中。本文认为，星闪技术为需要精密同步和高可靠性交互的应用场景提供了有效的解决方案，标志着短距无线通信正从“尽力而为”的模式向着提供可预测服务的方向发展，具备较好的应用前景。

目前在对于协同使用多种机器人达到生产目的工厂中，多数同时存在多种系统，数据在多系统之间存在信息壁垒，生产效率也因为信息流通迟滞而比较不足。针对这种现状并在骏通自卸车间实践项目基础上提出一种基于OPC工业标准的数据采集以及Socket通信技术的多机器人协同自动化智能调度系统的解决方案。使用Kepware工具对PLC的数据IO点进行读取和写入等交互操作，数据采集对象包括CLOOS焊接机器人，SINSUN的AGV以及工装库，实现对现场数据接收、流转、回收的实时监控，使得各机器人之间的信息得到汇总，并对各种机器人的状态进行实时监控与管理。同时利用智能调度策略来对生产中箱板材料种类进行控制并进行生产路径优化并实现柔性制造，该方案可以使箱板材料产量增加，并对材料种类进行控制，防止出现单一材料过剩，同时提高整体生产效率以及时间成本。

随着工业自动化和物流行业的迅速发展，自动引导车辆(Automated Guided Vehicle, AGV)在物流仓库中的路径规划已成为确保运输效率和准确性的关键环节。尽管近年来已经有很多策略被提出，但多AGV系统在复杂的物流环境中仍然频繁地出现碰撞、路径冲突以及控制迟延等问题。鉴于此，本研究提出了一种基于多智能体深度强化学习(Multi Agent Deep Reinforcement Learning, MADRL)的路径规划方法，以期解决多AGV之间的相互协调问题并提高其路径规划效率。为验证所提方法的有效性，我们采用了与遗传算法(Genetic Algorithm, GA)的比较实验。结果显示，基于MADRL的策略在整体运输效率上实现了28%的提升，并在碰撞事件上有了明显的减少。

针对智能制造车间多自动导引小车的调度和路径规划问题，需要同时考虑每台AGV的工作是装卸和运输工件，提出了AGV分步任务调度及路径优化模式，首先基于最小化最大运送完工时间为优化目标建立数学模型算法和基于工件加工紧急程度的物料运输任务分配算法，将货架和加工设备间的所有工件运送任务序列分配给相应的AGV，生成每台AGV初始可行路径。然后设计AGV冲突检测及防碰撞算法，规划多台AGV在车间工作场所全局无碰撞行走路径，并可以根据运送任务动态调整路径。最后通过算例验证方法的有效性，能有效解决多台AGV任务分配和基于运送任务序列避免冲突碰撞的路径规划，提高AGV工作效率。

本文研究了一种基于AGVs的智能仓储批量分拣模型及两阶段改进批量分拣优化方法。分析了受能量约束的AGV行驶路径问题，考虑以最小化AGV行驶距离为优化目标，构建多AGVs批量分拣数学模型。在迭代改进分拣优化的第一阶段利用待分拣订单位置，订单特征生成高质量初始解。第二阶段考虑使用智能优化算法，分别利用模拟退火算法和遗传算法对第一阶段生成的优质初始解进一步改进，并对所适用的智能仓储规模进行分析与验证。本文构建的分拣模型充分考虑了货架布局的多样性、订单构成的通用性、以及AGV行驶路径的规范性，更加适用于实际智能仓储分拣需求。提出的方法对于具有不同规模、不同分布的货架及订单都能够高效地进行分拣，在提升AGV利用率的同时降低智能仓储总能耗，为实际智能仓储应用提供可适配的柔性支撑。

搬运机器人在无人仓的“货到人”拣选系统中得到了广泛应用，然而多个搬运机器人协同作业还存在着路径寻优规划不合理、路径冲突等问题。考虑路径的负载平衡及搬运机器人转弯耗费的时间，建立了带有时间窗的多搬运机器人路径规划模型；结合调整优先级的避障策略，提出了一种改进的“离线–在线”两阶段动态路径规划算法，实现全局离线路径规划和在线冲突规避，提升了无人仓系统的运行效率。最后，设计三组仿真实验，通过对比搬运机器人的总运行时间，验证了改进算法的有效性。

汽车制造柔性化、定制化转型背景下，传统厂内物流存在动态生产适配不足、人工分拣依赖、数据协同低效等痛点。本文聚焦汽车入厂物流智能化升级需求与分拣效率瓶颈问题，研究厂内物流模式的技术优化空间与智能技术融合路径，提出“智能仓储–精准分拣–全链路协同”三位一体智能化升级路径及技术方案。通过优化仓储、提升分拣精准度、强化数据协同，破解物流低效僵化问题，推动行业向“低延迟响应、低差执行”智能生态演进，为数字化转型提供参考建议。

随着移动机器人在工业自动化、特种作业及智能服务领域的广泛应用，其路径规划能力越来越依赖机器人操作系统ROS2的通信性能。ROS2通过去中心化架构与数据分发服务中间件显著提升了系统可靠性，但动态复杂环境中路径规划对通信延迟、带宽及稳定性的严苛要求，使中间件性能成为影响实时规划精度的关键瓶颈。近年来针对DDS、Zenoh等中间件的优化研究大量涌现，但仍缺乏对多协议多场景性能指标的跨维度系统性总结。本文综述了近年来核心研究成果，深入剖析了ROS2中间件从通用架构向场景定制化设计的转型趋势；论证了路径规划与通信服务质量的深度耦合机制；总结了嵌入式协同与边缘智能的系统级优化机制。文末提出了目前研究还存在的一些问题。

针对复合机器人在机床上下料中的定位方法，本文主要关注识别机床卡盘和末端作业面的定位。首先分析了复合机器人在机床上下料过程中的工作原理及其基本结构，包括AGV (自动导引车)和协作机器人的功能及特点。然后，详细阐述了如何使用视觉系统和纠偏算法来识别机床卡盘和末端作业面，进而实现了复合机器人在机床上下料作业过程中的精确定位。接着提出了一种基于视觉系统和纠偏算法的复合机器人定位方法，该方法可以有效地识别机床卡盘和末端作业面。最后，对文中所提出的定位方法进行了实验验证，并与现有的定位方法进行了比较。实验结果表明，本文提出的定位方法能够有效地提高复合机器人在机床上下料过程中的定位精度和工作效率。总之，本研究为复合机器人在机床上下料中的应用提供了一种有效的定位方法，具有较高的实用价值。

伴随着机器人服务场景的复杂化、多样化，机器人工作过程中采集的数量种类繁多的数据处理与分析问题也亟待解决。为此，设计一个基于云平台的多用户多机器人的协同工作系统。该系统通过http协议进行数据传输，用户通过Android语音识别将文本命令发送到服务器，机器人从服务器获取用户的命令，执行并将结果返还。将云技术和多机器人系统的集成使得多机器人系统具有改进能源效率、实时性高、成本低的特点。

近年来，多智能体的协同控制在机器人等领域已经引起了很多学者的广泛关注，并得到了迅速的发展。本文针对无人机在飞行过程当中只有一个固定编队的问题展开研究，通过对算法进行改进从而实现无人机群的动态编队，以达到解决在多种情况下的不能实现自适应问题的目的。本文基于传统的多智能体编队方法，通过引入新变量的方法进行改进，首先引入了一个时间变量，在状态量后分别加入与时间相关的函数X(t)、Y(t)，通过改变参数对编队进行仿真，使编队中心随时间的改变而不断变化，编队形式也随之改变，从而实现动态编队。最后仿真结果表明改进的算法能够实现无人机群的动态编队控制，且改进后的动态编队与原编队相比，应用范围更加广泛，更加具有普遍性。

本文设计开发了一套基于机械臂和小车的物料分拣搬运的桌面级智能仓储系统，该系统基于AT Mega2560控制芯片，使用dobot机械臂、传送带、AI-Starter小车、传感器、Xbee无线通信器为执行部件，通过鼓励同学们开发机器视觉图像处理算法、机械臂小车同步作业协同控制算法，实现了物料的高效分拣、灵活装载、安全运输和自动入库，本系统为培养高素质复合型“新工科”人才提供了重要的教学实践平台，为学生运用人工智能知识分析解决问题提供了切实可行的实践手段。

随着科技的发展，机器人发展日新月异，在当今时代，在工业、物流、医疗以及服务领域，都能看到机器人的身影，因为单个机器人功能的局限性，多机器人系统开始发展，多机器人编队避障问题成为研究热点。采用的研究方法主要是是实验法和文献研究法，主要通过查阅文献来获得思路，然后在MATLAB中仿真得到实验结果。研究的主要内容有人工势场法、领航跟随法、人工势场法改进领航跟随法实现过程中遇到的问题、在静态环境中运用人工势场法和领航跟随法使多个机器人协同合作完成编队和避障。实验结果在MATLAB仿真中实现，利用栅格法，对障碍物进行膨化处理，4个机器人从初始位置，初始队形，到达目标位置，在行进过程中遇到障碍物，遇到障碍物时队形发生变换，躲避障碍物后，队形再一次改变，整个过程中，领航者运动轨迹和跟随者不同，在领航者到达位置后，跟随者根据情况改变位置信息从而达到目标位置。

近年来，移动机器人在各个领域的应用日益广泛，与之相关的技术已在国内外机器人领域掀起一股研究热。对环境的认识和定位从而实现自主导航是移动机器人智能化的重要标志和特征。在完全未知环境下的即时定位与地图构建(SLAM)也一直是移动机器人领域的研究重点。由于激光SLAM具有不易受环境影响、测量距离远且测量精度高、成本低的优点，因此本文采用改进的Hector_SLAM算法来完成室内环境下地图的创建。本文中所用系统主要包括由硬件系统和软件系统两部分。首先通过硬件系统对环境进行感知，然后提取周围环境特征信息，最后通过软件系统完成二维环境地图的实时创建。在得到环境地图的基础上，采用改进的卡尔曼滤波法对初始数据进行优化处理，以获得更为精确的环境地图。实验结果表明，本文所用系统成本低但性能好，能较准确的构建二维环境地图，并能成功使用在小型室内移动机器人的视觉导航中。

狭隘环境下多机器人路径规划使用共享资源时，极易产生冲突，优先顺序化是解决共享资源冲突的一个重要技术。本文提出了一种基于学习分类器的动态分配优先权的方法，提高机器人团队的性能。首先机器人通过XCS优化各自的行为，然后引入和训练高水平的机器人管理者来分配优先权解决冲突。本方法适用于部分可知的Markov环境，仿真实验结果表明本文所提方法用于解决多机器人的路径规划冲突是有效的，提高了多机器人系统解决路径规划冲突的能力。

未知环境下的多机器人区域覆盖是指多个机器人遍历环境中每个无障碍物的区域。机器人区域覆盖作为多机器人系统研究的重要组成部分，在灾后救援、野外勘测、森林防火等众多领域有着广泛的应用，具有十分重要的研究意义。传统的多机器人覆盖方法需要考虑区域分割、任务分配等问题，且没有协同策略的覆盖方法只是单个机器人方法的简单叠加。而在强化学习中机器人可以通过自主学习的方式求得问题可行解。本文将多机器人区域覆盖问题转换为多机器人强化学习中团队奖励值最大化的求解问题，搭建了基于Actor-Critic结构的多机器人强化学习网络，考虑到机器人个体行为对环境造成的不平稳问题，选择考虑了全局信息的QMIX网络作为多机器人行为的评价网络。最后设计了强化学习与仿真环境端到端的数据交互接口，简化了训练数据交互过程。算法训练结果表明本文提出的算法能达到较高的覆盖率，验证了该算法解决区域覆盖任务问题的有效性和可行性。

研究聚焦于智能仓储中AGV的路径规划问题，构建了基于强化学习的多智能体寻路算法。每个AGV能从环境和以往经验中学习，利用不同行为产生的奖励机制，训练智能体自主选择更高效策略，以达到预定目标。本研究在ACTOR-CRITIC算法基础上加入经验回放机制并采用了中心化训练和去中心化决策的方法，以提高智能体的路径规划效率。同时，将ACTOR-CRITIC算法在智能仓的环境下进行模拟训练，验证AGV的路径规划效果。

针对三维环境中多无人机路径规划面临着样本效率低、长时程决策困难和鲁棒性不足等挑战，本文提出一种基于分层策略与世界模型增强的多智能体深度确定性策略梯度算法框架(HWC-MADDPG)。首先，引入对比学习机制，从高维观测中提取时序一致性的鲁棒状态表征，增强了状态表征的区分度；其次，设计多智能体层次化策略网络架构，通过高层策略网络规划宏观意图，低层策略网络执行具体动作的方式，将路径规划任务分解，提升决策能力；最后，集成共享的世界模型，通过其内在的前瞻性推演生成想象奖励，优化Critic网络的价值评估，提升了决策前瞻性和收敛速度。实验结果表明，本文提出的算法在学习速度、策略稳定性和飞行安全性上均优于传统的多智能体深度确定性策略梯度算法(MADDPG)。该研究为解决三维环境下的多智能体路径规划问题提供了一种更高效的解决方案，具有一定的理论价值与应用前景。

商场、写字楼等室内场所经常存在异物堆放和发生火灾烟雾的情况，这些室内场所作为重要的活动区域，为了消除存在的安全隐患。需要经常对商场、写字楼等室内场所进行巡检，然而人工巡检效率低下，且不能全天候巡检。智能巡检机器人能够高效、全天候对巡检区域进行巡检。设计研究基于多传感器融合的ROS巡检机器人，该巡检机器人包括对激光雷达数据的预处理，基于粒子滤波的Gmapping算法实现建图，AMCL用于室内巡检机器人的重定位。结合A*全局路径规划算法和DWA局部路径规划算法共同实现室内巡检机器人的导航避障。深度相机结合改进YOLOv7检测算法对室内是否存在火灾烟雾与行人依靠电梯等安全隐患进行巡检。实验表明：该室内巡检机器人具有良好的建图、导航避障、视觉检测的功能。可见研究成果对室内巡检机器人的研究具有一定参考价值。

本文提出了一种LCS和LS-SVM相结合的多机器人强化学习方法，LS-SVM获得的最优学习策略作为LCS的初始规则集。LCS通过与环境的交互，能更快发现指导多机器人强化学习的规则，为强化学习系统的动作选择提供实时、动态的反馈，使多机器人自主地学习到相互协作的最优策略。算法的分析和仿真表明多机器人学习空间大、学习速度收敛慢、学习效果不确定等问题得到很大的改善。

This paper proposes an improved differential evolution (DE) algorithm to solve the multi-AGV cooperative path planning problem under complex multimodal environments. Based on the original DE, a dynamic weight adjustment mechanism is introduced to enhance the global exploration capability of Cuckoo Search (CS), and reduce the risk of local optima. A novel CS-DE fusion strategy dynamically adjusts the contribution ratio of the two algorithms to balance global exploration and local exploitation. Experiments on benchmark test functions and real-world multi-AGV trajectory planning scenarios demonstrate that the proposed fusion algorithm outperforms traditional methods in convergence speed, solution quality, and stability, providing a more efficient and reliable optimization solution.

With the rapid development of intelligent manufacturing, Automated Guided Vehicles (AGV) have become an important technology for enhancing production efficiency, reducing costs and optimizing resource allocation. Through analysis, combined with CiteSpace and vosviewer software, this paper discusses the evolution and development trend of AGV technology in intelligent manufacturing. Based on 150 effective literatures in the web of science database, this paper analyzes the research hotspots of AGV technology, such as path planning, scheduling optimization, Internet of things (IOT) and the application of 5g technology. Research shows that AGV technology is evolving towards greater intelligence and integration, and is deeply integrated with technologies such as artificial intelligence and the Internet of Things, promoting the further development of intelligent manufacturing and Industry 4.0.

Automated guided vehicles (AGV) have applications in various fields ranging from the process industry to many more. AGV has its history from the early 50s to till date. However, it’s been gone through several modifications in structure, design, and techniques. In its simple form, it completes its task using navigation. This paper aims to give a review of the various technological advancements in the field of Automated Guided Vehicle in the past few years. In this review, various navigational techniques and structural designs have been addressed. The various techniques of navigation have been studied and are used by various manufacturers in the world. The review includes the various structure of AGV which is currently in use in the market. In addition to this, the voice recognition technique has also been addressed.

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The increasing complexity of real-world engineering problems, ranging from manufacturing scheduling to resource optimization in smart grids, has driven demand for adaptive and high-performing heuristic methods. Automatic Heuristic Design (AHD) and neural-enhanced metaheuristics have shown promise in automating strategy development, but often suffer from limited flexibility and scalability due to static operator libraries or high retraining costs. Recently, Large Language Models (LLMs) have emerged as a powerful alternative for exploring and evolving heuristics through natural language and program synthesis. This paper proposes a novel LLM-based memetic framework that synergizes LLM-driven exploration with domain-specific local refinement and memory-aware reflection, enabling a dynamic balance between heuristic creativity and effectiveness. In the experiments, the developed framework outperforms other LLM-based state-of-the-art approaches across the designed AGV-drone scheduling scenario and two benchmark combinatorial problems. The findings suggest that LLMs can serve not only as general-purpose optimizers but also as interpretable heuristic generators that adapt efficiently to complex and heterogeneous domains.

Since its inception in the 1960s, light detection and ranging (LiDAR) technology has demonstrated great potential in various fields such as autonomous driving, robot navigation, and environmental monitoring due to its high precision, high resolution, and strong anti-interference capability. This paper reviews the development history, technical principles, application fields, and future development trends of LiDAR technology. It introduces the technical applications of LiDAR technology in autonomous driving, robot navigation, and environmental monitoring, and explores the development direction of SLAM algorithms in multi-sensor fusion and real-time map construction, providing a reference basis for the development and research of LiDAR.

Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.

In order to improve the efficiency and stability of multi-robot handling, inspired by stretcher handling, this paper is mainly aimed at obstacle avoidance and trajectory tracking in multi-robot handling of deformable sheet materials. When facing the obstacle avoidance problem in multi-robot handling, under the premise of ensuring stability, obstacle avoidance is achieved by improving the geometric obstacle avoidance control method based on the leader-follower formation control method while taking advantage of the characteristics of the deformable material. By improving the sliding mode variable structure control, the tracking of the respective target trajectory by each mobile robot is achieved, and the tracking error converges to zero. The simulation results show that the designed multi-robot cooperative handling algorithm can effectively achieve formation handline and obstacle avoidance.

With the increasing use of multi-robot systems in emergency scenarios, collaborative path planning for robots has attracted greater attention. The multi-robot path-planning problem was modeled as a bilevel cooperative path planning model and solved using a memetic algorithm with a dynamic window approach and a parking scheduling strategy (MA-DWAPSS). The bilevel path planning model has divided the problem into two parts: global (static) path planning to find a near-optimal route and dynamic path planning to avoid path conflicts. Corresponding to the proposed MA-DWAPSS method, an improved memetic algorithm was developed based on genetic algorithm to find an optimal global path and a cubic Bézier curve to smooth the path and avoid sharp turns. The dynamic window approach (DWA) and parking scheduling strategy (PSS) obtain real-time sensor data and coordinate the docking and movement of robots in dynamic environments, handling obstacles in real time and preventing conflicts or unnecessary stops to improve efficiency. DWA further accounts for the dynamic characteristics of robot motion, making the path planning flexible and adaptive to rapid environmental changes. Simulation results show that the proposed method outperforms three other algorithms in path distance, time, obstacle avoidance, and smoothness.

Cooperative Mobile Multi-Robot Systems (CMMRS) are supposed to enable more flexible handling systems but face challenges in scalability due to kinematic overdetermination. This paper presents a scalable control architecture using admittance control to mitigate said overdetermination. A Temporal Convolutional Network (TCN) for real-time force estimation serves to mitigate instabilities in the admittance controller that occur in rigid surface contact. Experimental validation with up to eight industrial robots demonstrates high tracking accuracy, with position errors below 2 mm and orientation errors around 10 mrad.

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Multi-robot systems have gained significant attention in recent years due to their potential for collaborative handling and operations. Designing an effective simulation system for multi-robot environments is crucial for enabling research and development in this field, while minimizing risks and costs associated with real robot testing. This paper presents a Multi-Robot Collaborative Transport (MRCT) simulation system that integrates the Robot Operating System (ROSI) with the Gazebo software environment. The system employs the FastDDS communication framework for task allocation and control, enabling efficient distribution of tasks among robots. A dual-robot model is developed for collaborative handling, with two robots equipped with manipulators working together to accomplish transport tasks. The performance of the simulation system is evaluated by comparing the trajectory of transported objects with that of the dual-robot system. The results demonstrate the exceptional performance and accuracy of the MRCT system.

Modeling and solving multi-robot task allocation with definite path-conflict-free handling is an important research, especially in real working environments. Some of the research lines are unable to obtain definite path-conflict-free solutions for multi-robot task allocations, such as using the penalty-term method in the fitness function to restrict the survival probabilities of the solutions with path conflicts. In some cases, these solutions are only able to satisfy the objective of minimizing task time. We formulate this problem based on grid maps, while focusing on the frequently used cooperative task allocation. In our model, two subtasks of each cooperative task must be executed by two robots, simultaneously. We propose vitality-driven genetic task allocation algorithm (VGTA), which is able to simultaneously minimize task time and realize definite conflict-free path planning. VGTA consists of local operators, such as random mutations, greedy crossovers, and vitality selection. Meanwhile, VGTA includes schedule conflict and path conflict handling strategies. In path conflict handling strategy, we not only consider the common path conflicts in a grid cell, but also focus on the path conflicts between robots when exchanging positions in the adjacent grid cells. Besides, we construct our benchmarks based on real working environments, such as factory, powerhouse, and airport environments. Experimental results indicate that VGTA’s search capability and computation cost are satisfactory. Meanwhile, its solutions are able to be really executed.

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In order to improve the safety of the multi-vehicle collaborative handling process and the flexibility of mobile robot scheduling, this paper optimizes the design of target formations for different types of mobile robots to carry heavy objects well. Rolling moment, combined with the handling strategy, a multi-vehicle intact heavy object handling model was established. At the same time, the energy-based static stability description method ESM (Energy Stability Margin) and the spacing between each short-circuit mobile robot are introduced to evaluate the team's search for the optimal form through an improved genetic algorithm, and the initial population generation method and selection methods, crossover and mutation methods; finally, the advantages and disadvantages of the form proposed in this article are proved through example verification and comparison with traditional genetic algorithms. The algorithm for solving multi-vehicle collaborative handling teams optimizes suction and perception problems.

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Formation control in multi-robot systems (MRS) is essential for collaborative transport, environmental surveillance, material handling, and distributed monitoring. A major challenge in MRS is maintaining predefined formations or cooperative task execution when individual robots experience operational faults, potentially isolating them from the group. In mission-critical scenarios, preserving the number of operational robots is crucial for task success. To address this, we propose a Robot Replacement approach framework for differential wheeled mobile robots. This approach isolates faulty robots and dynamically replaces them with pre-deployed spares, ensuring uninterrupted formation tasks. A graph theory-based framework models inter-robot communication and formation topology, enabling decentralized coordination. The proposed techniques were implemented in a MATLAB/Simulink simulation environment. The simulated robots are equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders for navigation. Simulation results demonstrate that the framework successfully maintains the target formation and task continuity during robot failures by dynamically integrating replacements with minimal disruption.

In safety-critical applications, where several mobile robots and autonomous agents are being utilized for a mission, a fault-resilience behavior of the system is necessary. The fault resilience mechanism mostly uses the robot’s redundancy and tasks reassignment to recover malfunctioning and increase operating efficiency. The E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model designed for the Role-Based Collaboration (RBC) approach has been used successfully on cooperative Multi-Robot Systems (MRSs). Role-based characteristics of E-CARGO will facilitate cooperative decision-making and simplify handling failure. This paper develops an extended E-CARGO model for a fault resilience role engine. Agents use factor graphs to update the process role and manage the potential failure in each time step. We apply hybrid control in this paper. By “hybrid” we mean that evaluating and assigning initial roles are centralized, and role-playing is decentralized based on the local observations. The RBC life cycle and a Bayesian consensus will maintain fault resilience behaviors. Potential failure can be identified in a Bayesian way by updating agents’ reliability and calling the central unit to assign new process roles to guarantee robustness. Simulation experiments show that the proposed role engine can increase performance and tolerate failures in multi-robot path planning scenarios.

In this paper, we present a solution for simultaneous handling of large components with industrial robots performing synchronized motions with an AGV in flexible flow assembly. For this purpose, we implement an Extended Kalman Filter with a global localization system to track an AGV and multiple manipulators. We propose a model-predictive controller for force compliance and trajectory tracking in multi-robot cooperative, decentralized, and fast manipulation tasks. In order to show the effectiveness of our system, we assemble a truck windshield using two industrial robots and an AGV in motion. In our experiments, we reliably achieve assembly tolerances of 1.5mm at AGV velocities up to $400\frac{{{\text{mm}}}}{{\text{s}}}$. The presented system makes flexible assembly systems with AGVs and freely reconfigurable manipulators possible. It enables the automation of high variant, low volume, large size assembly tasks such as aircraft, truck or steel beam assembly, which are mostly manual processes at present.

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In recent years, the demand for multi-robot cooperative operations in the warehousing and logistics industry has been growing rapidly, and how to move goods efficiently has become an important challenge. To cope with the challenge, this paper aims to deeply study the selection of robot types and formation strategies in warehouse environment to improve the efficiency of accomplishing multi-robot cooperative handling tasks. The article realizes the cooperative work of formations to accomplish handling tasks by designing two common robot models and two main formation strategies. A detailed comparative analysis is carried out in terms of obstacle collision risk, maintenance of formation shape and formation passability, and the experimental results comprehensively show that the combination of two-wheeled differential drive robots using virtual structure formation is more suitable for the warehouse environment, which is a reference and guidance for the practical application of multi-robots in warehousing.

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The use of free-flyer robots in space applications has been increasing in recent years. Several space agencies, such as NASA, DLR and JAXA, have at this moment, or intend to have, projects involving this type of vehicles. The increasing interest in these robots is largely motivated by the expansion of human presence beyond low earth orbit, to cislunar space and beyond. In particular, the envisioned Deep Space Gateway (DSG) will require autonomous robots performing logistics operations. This work presents a nonlinear model predictive control based method targeting a free-flyer robot in microgravity to perform 3 different tasks: waypoint navigation, single robot transportation, and multi-robot transportation. This controller is coupled with a trajectory generation method that provides feasible trajectory references to the controller, and is capable of handling confined spaces, composed by walls and obstacles, as well as control actuation limitations and system dynamics. All tests conducted in this work were obtained through realistic simulation based in ROS and Gazebo/RotorS.

This paper presents a communication framework for connected robots in a platoon formation, designed as a platform for real-time testing and validation of control algorithms. The framework utilizes ZigBee-based wireless communication, ensuring reliable, low-power, and efficient data exchange between robots. It supports scalable and adaptable communication, allowing efficient integration into various multi-robot applications while maintaining robustness in dynamic environments. The proposed system facilitates real-time monitoring and coordination, providing a robust environment for evaluating different strategies for platoon formation and cooperative decision-making. This framework provides a versatile solution for developing and validating control strategies in connected robotic systems by ensuring structured message handling, efficient network management, and optimized data processing.

The objective of the paper is to devise a general framework for handling the human safety in a multi-robot work-cell controlled within a decentralized framework. The paper is motivated by the increasing demand coming from new production paradigms for strict cooperation between humans and robots and for flexibility and robustness provided by decentralized control frameworks. The cell foresees several robots with different assigned roles. In particular, it is supposed that there are worker agents, that are in charge of performing the cooperative manipulation task, and watcher robots, that are in charge of supervising the cell with particular attention to the human safety. The latter is guaranteed by properly modifying the workers' task trajectory according to a state transition strategy that tries to preserve the task path as much as possible. The overall solution is tested via simulations in order to show the effectiveness of results.

Multi-robot cooperative transportation systems (MRCTSs) hold great potential in industrial material transport, where they need to be omnidirectional, highly flexible, adaptable to various terrains, and cost-effective. This letter presents an industrial MRCTS that employs a novel composite connector, consisting of a passive revolute joint and a gasbag, to link the robots and the payload. An angular sensor is used in the revolute joint to provide feedback control on the robot's orientation. We also introduce a simplified and cost-effective cooperative strategy that treats the robots as steering wheels (SWs) and the entire transportation system as a single robot with multiple SWs. The cooperative strategy for transporting payloads only necessitates handling robots to measure their heading, and the information provided to handling robots solely consists of the control velocities of the payload. Besides, we provide kinematics controllers for the entire system and individual robots and successfully perform three kinds of physical handling experiments. Experimental results demonstrate that the system can smoothly shuttle between flat and sloping roads without any midway stopping and possess highly flexible configurations. Besides, the system shows omnidirectional mobility, despite using nonholonomic mobile robots.

To solve the problem that the single robot task execution capability is not enough to meet the whole handling task demand under complex conditions, the hybrid path planning models such as multi-robot path planning and formation cooperative control considering obstacle avoidance are studied. Firstly, for the robot global path finding problem, on the basis of the construction for a robot working environment model based on the geometric map model building method, an improved particle swarm algorithm-based global path planning model is proposed to solve the problems of low robot path planning solution efficiency and easy to fall into local optimal solutions. Secondly, for the multi-robot cooperative formation control and obstacle avoidance and inter-robot collision avoidance problems, a multi-robot formation local path planning model based on the improved artificial potential field method is constructed, a simulated annealing algorithm is introduced to optimize the traditional artificial potential field method, and a multi-robot formation control strategy, robot obstacle avoidance, and inter-robot collision avoidance methods are designed in combination with the pilot-following method to improve the robot formation path exploration The proposed method can improve the path exploration capability and handling efficiency of robot formation. Finally, the global path planning model of the robot based on the improved particle swarm algorithm is simulated and analyzed using Matlab 7.0 to verify the outstanding performance of the model in pathfinding capability. Then the local path planning model of multi-robot formation based on the improved artificial potential field is simulated and analyzed to verify the improved algorithm has good path planning as well as obstacle avoidance performance. The hybrid path planning model is applied to a real case and simulated, and the results show that the improved algorithm improves the exploration capability of the robot formation, effectively avoids obstacles, and verifies its reliability and superiority in the hybrid path planning process.

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This paper addresses the challenge of Autonomous Mobile Robot (AMR) navigation in unknown environments using Simultaneous Localization and Mapping (SLAM). We propose improving the accuracy of AMR pose estimation within Unscented Kalman Filter (UKF)- and Cubature Kalman Filter (CKF)-based SLAM frameworks by integrating the Iterative Closest Point (ICP) algorithm immediately after the state prediction step. Our approach leverages re-observed landmarks from consecutive environmental scans, aligning and matching them to refine AMR position estimates. Simulation results demonstrate that incorporating ICP into the UKF-SLAM and CKF-SLAM methods significantly enhances state estimation accuracy and system stability.

In recent years, wheeled mobile robotics (WMR) for small and large-scale Industry 4.0 applications are being implemented in warehouses, factories, and smart cities. Mobile robots must navigate constantly changing dynamic environments, which present significant challenges due to the difficulty of real-time mapping, collision avoidance, and path planning optimization. This research develops an autonomous mobile robot (AMR) system capable of navigating independently through unfamiliar and uncharted indoor environments. To achieve this, a sensor system tailored to the environment is used to perform specific tasks. The data collected by these sensors is processed by an enhanced SLAM (Simultaneous Localization and Mapping) algorithm, which extends SLAM's capabilities and generates pathways to unexplored regions. A simulation environment is created in Gazebo for mobile robot mapping, integrating lidar and odometry data throughout the process. The slip rate of the four-wheel robot's steering is measured in position, leading to improved chassis pose accuracy. Currently, ROS and STM32 communicate, with the ROS chassis node packaged to receive speed commands, provide feedback from odometer data, and process transformations. Based on experiments and simulations, the system accurately maps the environment and performs precise navigation tasks.

Automation has come a long way thanks to the introduction and development of technologies that are now indispensable to industrial operations. As autonomous systems have developed, they have made it feasible to enhance human labour by cooperating with them and taking over the most taxing jobs. In this work, an autonomous mobile robot (AMR) system is created that can navigate on its own throughout an unfamiliar and uncharted interior area. An environment-appropriate sensoristics system is implemented to carry out the specified mission. The data provided by the sensors is utilized by an active SLAM algorithm that expands the capabilities of SLAM and creates pathways toward unknown regions. An environment in Gazebo was created for mobile robot mapping, and lidar and odometer data were integrated throughout the simulation. It was successful to measure the slip rate of the four-wheel robot's steering in-situ, resulting in improved chassis pose accuracy. Currently, ROS and STM32 are communicating, the ROS chassis node is packaged, and it can receive speed commands, feedback odometer data, and TF transformations. Based on experiments and simulations, the system can map the environment accurately and perform precise navigation tasks.

The ability to move from one point to the destination point autonomously is very necessary in AMR robots, to be able to meet this, the robot must be able to detect the surrounding environment and know its location to the environment, the Hector SLAM algorithm is added using the LIDAR sensor, and to find out the ability of the LIDAR sensor with the Hector SLAM and computer specifications in order to process properly, a simulation of the HECTOR SLAM with the LIDAR sensor was made. Simulation is carried out by creating an environment map on the Gazebo. Then explore environmental mapping using Hokuyo LIDAR which has been added to the turtlebot3 model waffle_pi to the simulated environment map. In this study, a model of the second floor lobby environment and Brail of the Batam State Polytechnic was used which was made in the form of a simulation on the Gazebo, where robots that have used LIDAR will be controlled with a keyboard around the simulation environment, where simultaneously the mapping and localization process runs and the process can be seen on the Rviz in real-time, where LIDAR will send data in the form of distance readings that will be received by Hector SLAM. The results of this study are expected that Hector SLAM using LIDAR sensor simulation can produce environmental mapping and localization in the simulation environment and obtain a minimum computer specification to process data from the SLAM Hector process using LIDAR sensors.

Current Automated Guided Vehicle (AGV) systems in warehouses often lack flexibility in navigating around obstacles due to limitation of line and marker-based navigation methods. This study presents an intelligent navigation system for an autonomous mobile robot (AMR) designed to improve efficiency of item retrieval in warehouse environment. The proposed system employs a Simultaneous Localization and Mapping (SLAM) algorithm to enable real-time mapping, obstacle avoidance, and efficient path generation. The global planner is a critical component in the navigation framework, as it determines the optimal and collision-free route through the warehouse, providing an efficient high-level path for the local planner to refine during real time navigation. Extensive evaluations were conducted to determine the optimal combination of global path planners between 1) RRT and Hybrid A* with local planners DWA and 2) TEB for path optimization, based on minimizing path length and travel time to the goal. Experimental results showed that the Hybrid A* with TEB planner achieved the fastest navigation time of 1.25 minutes with a 90% success rate, outperforming the RRT with DWA combination in both speed and reliability. This advancement offers significant potential to enhance warehouse operations by enabling fully autonomous mobile robots capable of performing item retrieval without human intervention.

This study aimed to develop a real-time localization system for an AMR (autonomous mobile robot), which utilizes the Robot Operating System (ROS) Noetic version in the Ubuntu 20.04 operating system. RTAB-MAP (Real-Time Appearance-Based Mapping) is employed for localization, integrating with an RGB-D camera and a 2D LiDAR for real-time localization and mapping. The navigation was performed using the A* algorithm for global path planning, combined with the Dynamic Window Approach (DWA) for local path planning. It enables the AMR to receive velocity control commands and complete the navigation task. RTAB-MAP is a graph-based visual SLAM method that combines closed-loop detection and the graph optimization algorithm. The maps built using these three methods were evaluated with RTAB-MAP localization and AMCL (Adaptive Monte Carlo Localization) in a high-similarity long corridor environment. For RTAB-MAP and AMCL methods, three map optimization methods, i.e., TORO (Tree-based Network Optimizer), g2o (General Graph Optimization), and GTSAM (Georgia Tech Smoothing and Mapping), were used for the graph optimization of the RTAB-MAP and AMCL methods. Finally, the TORO, g2o, and GTSAM methods were compared to test the accuracy of localization for a long corridor according to the RGB-D camera and the 2D LiDAR.

The project focuses on the design, development, and implementation of an Autonomous Mobile Robot (AMR) system specifically tailored for warehouse environments. The escalating demand for efficient warehouse management, driven by the e-commerce boom, necessitates the adoption of automated solutions to overcome challenges like labor shortages and operational inefficiencies. The proposed AMR system integrates advanced sensing technologies, including 2D LiDAR and IMU, with robust navigation and control algorithms to enable the robot to autonomously navigate through dynamic warehouse environments. The core of the system is built upon the ROS 2 framework, providing a robust and flexible platform for development and deployment. The project explores the implementation of Simultaneous Localization and Mapping (SLAM) techniques, utilizing the SLAM Toolbox, to enable the robot to build and update maps of its environment while simultaneously determining its position. Furthermore, the system incorporates advanced path planning algorithms and obstacle avoidance mechanisms to ensure safe and efficient navigation. The project utilizes Gazebo for realistic simulation and validation, allowing for thorough testing and optimization of the AMR system before real-world deployment. This research aims to contribute to the advancement of warehouse automation by developing a cost-effective and efficient AMR system that can significantly improve operational efficiency and productivity.

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The widespread application of Autonomous Mobile Robots (AMRs) in dynamic indoor environments has exposed the shortcomings of traditional navigation systems. For example, they cannot solve problems, like moving obstacles, layout changes, and multi-robot collaboration. The development of artificial intelligence and sensor technologies speeds up the changes in AMR navigation systems. This paper contains the development of AMR navigation systems. It analyses the technical challenges brought by dynamic indoor environments, including problems such as interference from mobile obstacles, bottlenecks in location and map updates, and efficiency conflicts in multi-robot collaboration. Additionally, it explores the key technologies used to solve these challenges, including dynamic object detection and removal in dynamic SLAM, multi-sensor fusion, semantic enhancement of dynamic map construction, and distributed task allocation based on a market auction mechanism, conflict search, and spatiotemporal path coordination in multi-robot collaborative navigation. According to these studies, this paper guides the development of more robust and adaptive AMR navigation frameworks to meet the application needs of modern indoor robots.

Autonomous Mobile Robots (AMRs) are utilized for handling goods and workpieces in factories and warehouses. The advantages of AMRs are completing automated tasks, reducing labour costs, improving efficiency and increasing convenience. In order to enable AMRs to navigate and move faster in all directions, this study focuses on the implementation of an AMR equipped with four Mecanum wheels to execute path planning tasks for a human-robot collaboration work. To achieve the path planning tasks, the AMR employs a Simultaneous Localization and Mapping (SLAM) algorithm based on the Robot Operating System (ROS) and light detection and ranging (LIDAR) sensors are used for mapping. With omnidirectional mobility of the AMR, a global path planning method is crucial to ensure the AMR’s navigation in the environment. This study adopts the A* algorithm and Voronoi diagram as the global path planning method; it can find the shortest path for navigation of the AMR. To improve the efficiency of AMR path planning, the Voronoi diagram is used to describe the map and the shortest path is built by Dijkstra algorithm. The collision-free A* algorithm will avoid collisions effectively and find the optimal path according to the shortest path tree. Voronoi algorithm is more suitable for use in factory environments with obstacles. Data fusion technology significantly improves positioning accuracy through the integration of visual odometers, IMUs, and traditional odometers.

Accurate position estimation is critical for the reliable navigation of autonomous mobile robots (AMRs) in indoor construction environments. Conventional methods, including landmark detection using rangefinders, visionbased optical sensors, and global positioning system (GPS)- based localization, encounter limitations in large, clutterfree indoor industrial spaces. Similarly, light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) requires premap construction, which is time-consuming and impractical for dynamic construction sites. To address these challenges, this article proposes a planar marker-based position estimation system that enables immediate deployment without premapping, optimized for multirobot collaboration in indoor construction environments. The proposed system employs 3-D marker recognition with minimal setup, using markers placed on both the robot and a designated home position. The interrobot communication enables relative position estimation and coordinate sharing, while accumulated odometry errors are periodically reset using the home marker to minimize positional drift. The experimental validation demonstrates position errors below 100 mm over a 20-m travel distance, with standard deviations of ±2.5 and ±2.0 mm in the X- and Y-axes, respectively, and an angular error of ±0.1° during docking. These results confirm that the proposed method achieves accurate trajectory tracking and rapid environmental adaptability, significantly enhancing the efficiency and robustness of collaborative AMR operations in large-scale indoor construction sites.

This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.

Autonomous Mobile Robots (AMRs) are increasingly utilized across various industrial sectors, particularly in manufacturing. This study focuses on the design and implementation of an AMR equipped with four Mecanum wheels, programmed to execute diverse path planning tasks within a simulated manufacturing environment. The primary objectives are to determine the AMR's position, plan its movements, and navigate around obstacles. To achieve these goals, the AMR employs a Simultaneous Localization and Mapping (SLAM) algorithm based on the Robot Operating System (ROS), installed on a Raspberry Pi. A Light Detection and Ranging (LIDAR) sensor is used for mapping. The path planning algorithm integrates the Dijkstra algorithm for global planning and the Dynamic Window Approach (DWA) for local planning, allowing the AMR to navigate autonomously based on a predefined map. User-defined target movements are executed via optimal paths determined by the AMR's navigation plan. The proposed algorithm and the AMR's navigation system were tested both in real-world path planning scenarios and in simulated environments using RViz, demonstrating their effectiveness and reliability.

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

Accurate localization is critical for autonomous vehicles (AVs), enabling safe navigation and interaction with their environment. Traditional localization methods, such as Global Navigation Satellite System (GNSS), often face limitations in urban canyons, forests, and remote areas where GNSS signals are unreliable. While other sensor modalities like light detection and ranging (LiDAR) sensors and cameras offer potential solutions, they still fall short in certain conditions. This paper presents a Ground Penetrating Radar (GPR) based vehicle positioning system that addresses these limitations. We propose a rasterized GPR data model that efficiently represents subsurface features by discretizing them into multiple data layers, reducing storage and transmission requirements. This model integrates with existing sensor modalities, such as Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), and odometry, to create maps using a low-latency Simultaneous Localization and Mapping (SLAM) framework optimized for mixed-GPS environments. The GPR-SLAM system, combined with a Vehicle- to-Everything (V2X) data transmission architecture using the Message Queuing Telemetry Transport (MQTT) protocol, enables real-time map generation and sharing among vehicles, ensuring accurate localization even with partial data reception. Our experimental results demonstrate that GPR based maps have a small data footprint, remaining within the bandwidth requirements of standard networks. Furthermore, the system proves effective in mixed-GNSS environments, where GNSS signals are degraded or unavailable, showcasing its potential for off-road and urban applications with limited or disrupted network conditions. This research highlights GPR as a promising technology for robust and scalable localization.

With the advancement of communication technology from 5G to 6G, future communication networks will no longer be limited to land and air, and the ocean will also become the battlefield for 6G networks. The expansion of the network has expanded the scope of Intelligent Autonomous Transport Systems (IATS). As a new type of underwater transport system, Autonomous Underwater Vehicle (AUV) has gained popularity due to their advantages of autonomy, endurance, and concealment. In practical applications, it is necessary to fully consider the impact of uncertain marine environments on AUV’s motion, and also design stable control unit to achieve AUV formation. The core of the control unit is the AUV formation control algorithm, which should enable AUV to complete path planning and obstacle avoidance while ensuring formation control. In order to solve the above problems, an Intelligent Multi-agent path planning and formation control algorithm based on Value-decomposition networks (IMV) is proposed in this paper. Specifically, a three-dimensional high-resolution marine simulation environment located in the Mariana Trench is established, the state transition function and reward function are well designed under uncertain conditions for stable Multi-Agent Reinforcement Learning (MARL) mechanism, a Value-Decomposition Networks (VDN) based training framework is constructed to improve the convergence speed of the proposed method. The experimental results verify the excellent performance of the IMV method proposed in this paper, demonstrating that our method can outperform other methods in the aspect of stability, adaptability, intelligence, and timeliness.

In this paper, a fully distributed control protocol is presented based on adaptive technology to solve the time‐varying formation problem of multi‐unmanned aerial vehicle (UAV) systems, where the coupling weights between neighbouring UAVs can be adjusted through adaptive method. Firstly, the proposed control protocol eliminates the need for global information on the interaction topology of multi‐UAV systems, the calculation of Laplacian matrix eigenvalues is avoided and reduces the computational complexity of the system. Secondly, an algorithm with two steps is proposed to determine the distributed adaptive control gain matrices, where the feasibility condition of time‐varying formation of multi‐UAV systems is given. Then, the closed‐loop stability of multi‐UAV systems is proved through Lyapunov theory. Finally, numerical simulation is conducted to verify the effectiveness of the theoretical results.

Most studies on coupled control in mixed traffic environments have primarily focused on improving the travel efficiency of CAVs, rather than optimizing the entire traffic flow composed of both CAVs and HDVs, and they usually decide traffic signals based on the current states without predictions of CAVs’ responsive actions. Therefore, we propose a hierarchical DRL-based method for the coupled optimization of traffic signals and vehicle trajectories within partially-connected environments. In the first layer, a multi-vehicle formation control method is designed to form CAV/HDV platoons in each direction of the intersection. In the second layer, a framework in which vehicles and the signal controller form an asynchronous decision-making mechanism is established to maximize the operational efficiency of the entire mixed flow. Ultimately, simulation experiments conducted with SUMO software confirm that the proposed method enhances the operational efficiency of both CAVs and HDVs under varying traffic demands and CAV ratios.

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The present work deals with a consensus control for a multi-agent system composed by a mini Vertical Take-off and Landing (VTOL) rotorcrafts by means of a controller based on time-delay parametrization. The VTOL system modeling is presented using the quaternion parametrization to develop the attitude-stabilizing law of the aerial robots. The vehicle position dynamics are extended to the multi-agent case where a time-delayed PID control is designed in order to achieve general consensus in terms of formation control of the system. Finally, a detailed simulation study is presented to validate the effectiveness of the proposed control strategy, where it also considered a collective interaction.

This paper describes the development of an optimization-based multi-stage centralized planning system for the efficient routing and assignment of extended flight formations in commercial airline operations. In an extended formation, where aircraft are longitudinally separated by 5-40 wingspans, a trailing aircraft can attain a reduction in induced drag at fixed lift, and consequently in fuel burn, by flying in the upwash of the leading aircraft’s wake. To organize the assembly of flight formations on a network-wide scale essentially two distinct approaches can be taken, viz., a centralized approach and a decentralized approach. Both approaches have distinct advantages and disadvantages. In this study a novel multi-stage method for flight formation assignment is proposed that combines the advantages of the decentralized approach (fast computation and reduced vulnerability to flight delays) with the main benefit of the centralized approach (a near-global optimum in terms of fuel savings). The multi-stage centralized approach that we propose is validated and subsequently demonstrated in a case study involving a wave of 267 eastbound transatlantic flights. In the case study fuel savings of 6.8% are recorded (relative to flying “solo”), while flying in formations comprising up to 16 aircraft.

In this paper, a unified multi-vehicle formation control framework for Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the one-dimensional scenario, different formation geometries are analyzed and the interlaced structure is mathematically modelized to improve driving safety while making full use of the lane capacity. The assignment problem for vehicles and target positions is solved using Hungarian Algorithm to improve the flexibility of the method in multiple scenarios. In the two-dimensional scenario, an improved virtual platoon method is proposed to transfer the complex two-dimensional passing problem to the one-dimensional formation control problem based on the idea of rotation projection. Besides, the vehicle regrouping method is proposed to connect the two scenarios. Simulation results prove that the proposed multi-vehicle formation control framework can apply to multiple typical scenarios and have better performance than existing methods.

Multi-robot cooperation, unmanned aerial vehicle (UAV) formation control, intelligent transport systems, and distributed sensor networks are just a few domains where multi-agent systems are crucial, as they require coordinated behavior to achieve common goals such as exploration, resource allocation, distributed sensing, and target tracking. This paper investigates various neural network configurations utilized in the NN-MPC framework for consensus control of multi-agent robotic systems. The NN-MPC control is applied to the consensus problem of a leader-follower multi-agent system, where agents coordinate to achieve collective behavior. In this approach, MPC is utilized to predict the future values of the control objective, which is optimized by minimizing a cost function with various neural network architectures. Different neural network configurations based on feed-forward, recurrent neural networks, Fitnet, and cascade networks are explored for the NN-MPC-based multi-agent systems. The analysis is performed through a simulation-based model of a quadrotor fleet system. Results show that the follower agents achieve consensus 60% faster than with RNN-MPC in comparison to the feedforward neural network, whereas the results are more effective when compared with the cascade network configuration-based MPC, where agents reach consensus 90% early if paired with suitable training structures. Overall, the article contributes to the recent topic of research on learning-based MPC of the multi-agent system in achieving consensus for the leader-follower strategy.

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

Aiming at the problem of multi-vehicle formation control, this paper uses the leader-follower method, and designs the control scheme based on the characteristic model theory. Through the multivariable golden section adaptive controller, the data-driven control of the Mecanum wheel vehicle is carried out in the network delay environment. The control scheme is simulated and analyzed. Under the influence of the random delay module, the multi-vehicle is capable of maintaining the desired formation and following the intended trajectory, and the controller debugging is simple and the control performance is excellent.

Inspired by natural swarm collective behaviors such as colonies of bees and schools of fish, coordination strategies in swarm robotics have received significant attention in recent years. In this paper, a mixed formation control design based on edge and bearing measurements is proposed for networked multi-vehicle systems. Although conventional edge-based controllers have been widely used in many formation tasks, the tracking accuracy may not be guaranteed in some extreme environments as it depends on the quality of the sensors and requires the exact position data of each vehicle. To overcome this limitation, we combine the edge-based controller with a bearing-based method where only relative bearings among the vehicles are required. Depending on the sensing-ability of the robotic platform, this mixed control method can provide an efficient solution to maximise the tracking performance. Both leaderless and leader-follower cases are considered in the protocol design. The stability of the networked multi-vehicle systems under the proposed mixed formation approach is ensured by Lyapunov theory. Finally, we present simulation results to verify the effectiveness of the theoretical results.

Abstract This paper presents a compelling combination of the two negative-imaginary (NI) control systems: a consensus-based formation control framework for a hybrid multi-vehicle system and a new dynamic obstacle detection and avoidance algorithm. The incorporation of the two techniques permits robots to self-detect collisions and then self-create a safe path to overcome any unexpected obstacles while traveling to the destination in a given formation. Moreover, owing to the distributed and dynamic architecture, our NI obstacle avoidance method has better adaptability to the change of environment conditions or obstacle/robot dynamics. Also, a rigorous comparative study on the performance of our obstacle avoidance method with respect to that of the artificial potential field function and switching formation-based NI obstacle avoidance algorithms is performed as a benchmark. Finally, simulation and experimental results are then implemented to demonstrate the effectiveness of the proposed theories.

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Formation control is one of critical topics in cooperative control. In this paper, we design a prescribed-time bipartite formation control strategy for vehicle multi-agent systems with external disturbances and internal unknown dynamics. A prescribed-time extended state observer (PTESO) is developed to compensate for total disturbances in a prescribed time, and the convergence time can be set in advance for different initial conditions. In order to make the formation error converge in a prescribed time, two novel time functions are introduced and applied to the control strategy. A prescribed-time control strategy with PTESO is developed for bipartite vehicle formation, and the relationship of the convergence time between PTESO and formation error is illustrated. Stability analysis shows the observation error and bipartite formation error are steered to converge their prescribed time, respectively. Furthermore, simulations are conducted to demonstrate the effectiveness of the prescribed-time bipartite formation control strategy. Note to Practitioners—This paper presents a prescribed-time bipartite formation control strategy for vehicle multi-agent systems with external disturbances and internal unknown dynamics. The total disturbance of each agent is compensated by a prescribed-time extended state observer (PTESO). In this PTESO, the convergence time is adjustable in advance for different initial conditions, and the peak phenomenon, caused by large gains, is avoided owing to adjustable and smaller time-varying gains. Two novel prescribed-time functions are proposed for vehicle MASs. The proposed functions remove the requirement that calculating gains mathematically, and the issue of jumping variation, caused by modifying coefficients in traditional prescribed-time functions arbitrarily, is also addressed. This strategy provides a feasible strategy for industrial applications.

This paper investigates the path planning problem for multiple vehicles formation in multiple obstacles environment. The kinematics model of the intelligent small robot vehicle (ISRV) is proposed by mechanical analysis. In order to remove the deadlock phenomenon during the path planning, a joint grid network and improved particle swarm optimization algorithm for path planning of multiple vehicles is proposed. The long path in the whole working environment with multiple obstacles is divided into multi-segment continuous short paths by the grid network. Then, the improved particle swarm optimization (IPSO) algorithm is used to search for the optimal path in the short paths for the mobile vehicles formation. Finally, some experiments are performed to demonstrate the effectiveness of the proposed path planning algorithm for multi-vehicle formation.

This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of the unmanned vehicles only rely on the neighboring bearing information. This feature can be utilized to reduce the sensing requirements in the hardware implementation. A gradient-descent protocol is first developed to achieve the desired coordination within a prespecified settling time, where the unknown disturbances are considered in the vehicle dynamics, then the bound of formation tracking error is guaranteed by the Lyapunov approach. In case of damage to the actuators (e.g., motors) in some of the vehicles during the task, fault-tolerant analysis of the proposed controller is provided to ensure the success of the task in extreme environments. Furthermore, the proposed bearing-based method is extended to deal with general linear systems, which can be applied to a wider range of robotic platforms. Finally, numerical simulations and lab-based experiments using unmanned ground vehicles are conducted to validate the effectiveness of the proposed strategy.Note to Practitioners—The aim of this paper is to develop and design a practical bearing-only formation control approach for multi-vehicle systems. Many real-world complex tasks can be solved by multiple unmanned aerial and ground vehicles being connected by a communication network. This paper has proposed a formation tracking scheme for networked multi-vehicle systems that only relies on the relative bearing information of the neighboring vehicles. Closed-loop stability of the scheme and finite-time convergence of the tracking error have been established using the Lyapunov stability approach. The proposed method ensures the robustness and fault-tolerance of the multi-vehicle system against hardware faults or exogenous disturbances. A systematic set of guidelines on how to apply the proposed strategy in practice is also provided for the control practitioners in the form of an algorithm. In order to demonstrate the feasibility and usefulness of the proposed coordination scheme, numerical simulations and lab-based hardware experiments were conducted. Potential applications of the proposed scheme include search and rescue, security surveillance and cooperative exploration.

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This paper addresses a path-guided formation-containment control problem by virtue of the aperiodic communication for networked heterogeneous multi-vehicle systems suffering from actuator faults and uncertainties. At the leader layer, a novel three-dimensional (3-D) path-following formation controller endowed with spatial-temporal decoupling and path invariance advantages is presented for multiple quadrotors to accomplish a preassigned formation pattern along implicit paths. At the follower layer, in light of neighboring transmission, a containment controller is proposed that actuates several unmanned vehicles to access the convex hull generated by the leaders. Besides, by incorporating an event-triggered criterion with a state predictor, an aperiodic transmission strategy is established to alleviate the communication burden and evade unnecessary resource wastage. Subsequently, to recognize the actuator faults and uncertainties, guided by invariant manifold principle, unknown system dynamics estimators (USDEs) are elaborated on the strength of straightforward filtering operations. Finally, the stability of entire closed-loop system is proved according to Lyapunov analysis and input-to-state stability (ISS), while the effectiveness and superiority are demonstrated by simulations.

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This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.

Formation control has been studied extensively along with the potential applications of multi-vehicle system in various areas. In this paper, a distributed control scheme based on the relative bearing and distance measurements is proposed. The main difference from the existing works lies in that the proposed control scheme can realize unambiguous formation tracking while considering the task-specification and environment constraints under practical bearing and distance sensing conditions. First, the uniqueness of the formation based on relative bearings and distances is investigated and the sufficient condition is provided. Further, the distributed formation controllers that are able to dynamically track the formation with both leaderless and leader-follower setups are designed under practical sensing conditions. In the end, to validate the effectiveness and practicability of the proposed distributed hybrid measurements based formation control scheme, both simulations based on synthetic scenarios and experiments with quadrotors equipping cameras and Ultra-Wide Band (UWB) transmitters are carried out. The simulation and experiment results show that the proposed controllers are able to track the desired formation pattern based on the bearing and distance measurements.

For the formation navigation, formation keeping and formation obstacle avoidance problems in the formation control of multiple unmanned vehicles (UGVs), the commonly used all-in-one algorithms have the disadvantage of high information interaction costs. In this paper, a distributed formation control method based on the leader-following method is constructed by distinguishing the leader vehicle and the follower vehicle, configuring a high intelligence trajectory tracking algorithm for the leader vehicle and a low intelligence formation control algorithm and obstacle avoidance algorithm for the follower vehicle. In this paper, the dynamic window approach, the error control-based leader-following method and the behavior-based method are used to achieve the work of trajectory tracking, formation control and formation avoidance respectively. With this distributed approach, the information interaction cost of the system is reduced and better navigation results are obtained for formation navigation compared to the old manual potential field method.

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