This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflicts. ARC creates subproblems centered around conflicts, and the solutions represent the robot motions required to resolve these conflicts. The use of subproblems enables an inexpensive hybrid exploration of the multi-robot planning space. ARC leverages the hybrid exploration by dynamically adjusting the coupling and decoupling of the multi-robot planning space. This allows ARC to adapt the levels of coordination efficiently by planning in decoupled spaces, where robots can operate independently, and in coupled spaces where coordination is essential. ARC is probabilistically complete, can be used for any robot, and produces efficient cost solutions in reduced planning times. Through extensive evaluation across representative scenarios with different robots requiring various levels of coordination, ARC demonstrates its ability to provide simultaneous scalability and precise coordination. ARC is the only method capable of solving all the scenarios and is competitive with coupled, decoupled, and hybrid baselines.
K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Siebel School of Computing and Data Science（西贝尔计算与数据科学学院）；Texas A&M University（德克萨斯农工大学）；Instituto Tecnológico Autónomo de México (ITAM)（墨西哥自治技术学院）
Multi-Robot Coordination for Planning under Context Uncertainty
Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University（俄勒冈州立大学协作机器人与智能系统研究所）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
Virginia Tech（弗吉尼亚理工大学）；University of Maryland（马里兰大学）
Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks. We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmen…
The hunter and gatherer approach copes with the problem of dynamic multi-robot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multi-robot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multi-robot exploration algorithm for hunters based on innovative notions of "exp…
IMT Lille Douai（里尔-杜埃国立高等矿业电信学院）；Université de Lille（里尔大学）；University Rey Juan Carlos（胡安卡洛斯国王大学）；Valencian Research Institute for AI（瓦伦西亚人工智能研究院）；Universitat Politècnica de València（瓦伦西亚理工大学）
Open and shared manufacturing factories typically dispose of a limited number of robots that should be properly allocated to tasks in time and space for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture and location of robots need to be determined at consecutive periods within a given time horizon and products can be anticipated or backordered related to the demand period. We consider a decentralized multi-agent variant of this problem i…
Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings
School of Computing and Communications, The Open University（奥本大学计算与通讯学院）；School of Electronics and Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）
Multi-Robot Planning and Control from CCTV Camera Networks in a Real Warehouse
Oxford Robotics Institute, University of Oxford, UK（牛津大学机器人研究所，牛津大学，英国）；Robot Systems Group, Technical University of Munich, Germany（机器人系统组，慕尼黑技术大学，德国）
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
In this paper, we describe a robust multi-drone planning framework for high-speed trajectories in large scenes. It uses a free-space-oriented map to free the optimization from cumbersome environment data. A capsule-like safety constraint is designed to avoid reciprocal collisions when vehicles deviate from their nominal flight progress under disturbance. We further show the minimum-singularity differential flatness of our drone dynamics with nonlinear drag effects involved. Leveraging the flatness map, trajectory optimization is efficiently conducted on the flat outputs while still subject to …
Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy（意大利国家研究委员会智能工业技术与先进制造研究所）；Politecnico di Milano（米兰理工大学）；Politecnico di Torino（都灵理工大学）
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais（计算机视觉与机器人实验室（VeRLab）、计算机科学系、巴西联邦大学矿务格里斯大学）
Rutgers, the State University of New Jersey（罗格斯大学（新泽西州立大学））
For enabling efficient, large-scale coordination of unmanned aerial vehicles (UAVs) under the labeled setting, in this work, we develop the first polynomial time algorithm for the reconfiguration of many moving bodies in three-dimensional spaces, with provable $1.x$ asymptotic makespan optimality guarantee under high robot density. More precisely, on an $m_1\times m_2 \times m_3$ grid, $m_1\ge m_2\ge m_3$, our method computes solutions for routing up to $\frac{m_1m_2m_3}{3}$ uniquely labeled robots with uniformly randomly distributed start and goal configurations within a makespan of $m_1 + 2m…
DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning
Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学全球未来技术学院）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Department of Electrical Engineering, The University of Texas at Arlington（德克萨斯大学阿灵顿分校电气工程系）
Rice University（莱斯大学）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；University of Oxford（牛津大学）
High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan) and their continuous geometric realization (i.e., motion plans). This joint approach solves more difficult problems than approaches that address the task and motion subproblems independently. TMP algorithms combine and extend results from both task and motion planning. TMP has mainly focused on computa…
V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；State Key Laboratory of CNS/ATM, Beijing Institute of Technology（北京理工大学国家空管委/民航委重点实验室）；Beijing Key Laboratory of Lightweight Intelligent System, Beijing Institute of Technology（北京理工大学轻量化智能系统重点实验室）；School of Automation, Southeast University（东南大学自动化学院）；Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology（深圳先进技术大学计算机科学与控制工程学院）
MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm
AI Institute of Seoul National University（首尔国立大学人工智能研究所）；Carnegie Mellon University（卡内基梅隆大学）；Department of Mechanical System Design Engineering, Seoul National University of Science and Technology (SEOULTECH)（首尔科学技术大学机械系统设计工程系）
Generative Multi-Robot Motion Planning via Diffusion Modeling with Multi-Agent Reinforcement Learning Guidance
School of Manufacturing Systems and Networks, Arizona State University, Mesa, AZ（1制造系统与网络学院，亚利桑那州立大学，梅萨，AZ）；Michael W. Hall School of Mechanical Engineering, Mississippi State University, Starkville, MS（2迈克尔·W·霍尔机械工程学院，密西西比州立大学，斯塔克维尔，MS）
