A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of which consists of a central agent and neighboring agents. Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems. When solving for the local control actions, the joint optimality equation for each subsystem is cast as a linear partial differential equation and solved using the Feynman-Kac formula. The solution and the optimal control action are then formulated as path integrals and approximated by a Monte-Carlo method. Numerical verification is provided through a simulation example consisting of a team of cooperative UAVs.
We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-ba…
Tokyo Institute of Technology（东京工业大学）；OMRON SINIC X（欧姆龙先尼克斯）
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach is to first construct graphs approximating the spaces, called roadmaps, and then apply multi-agent pathfinding (MAPF) algorithms to derive a set of conflict-free paths. While conventional studies have utilized roadmap construction methods developed for single-agent planning, it remains largely unexplored how we can construct roadmaps that work effectively for multiple agents. To this end, we propose a novel concept of roadmaps called cooperative timed roa…
We investigate the distributed planning of robot trajectories for optimal execution of cooperative tasks with time windows. In this setting, each task has a value and is completed if sufficiently many robots are simultaneously present at the necessary location within the specified time window. Tasks keep arriving periodically over cycles. The task specifications (required number of robots, location, time window, and value) are unknown a priori and the robots try to maximize the value of completed tasks by planning their own trajectories for the upcoming cycle based on their past observations i…
This article addresses the problem of Cooperative Coverage Path Planning (C-CPP) for the inspection of complex infrastructures (offline 3D reconstruction) by utilizing multiple Unmanned Autonomous Vehicles (UAVs). The proposed scheme, based on a priori 3D model of the infrastructure under inspection, is able to generate multiple paths for UAVs in order to achieve a complete cooperative coverage in a short time. Initially the infrastructure under inspection is being sliced by horizontal planes, which has the capability of recognizing the branches of the structure and these branches will be hand…
Motion Planning of Cooperative Nonholonomic Mobile Manipulators
Department of Mechanical Engineering, Indian Institute of Technology Bombay（印度理工学院班加罗尔机械工程系）；Center for Systems and Control, Indian Institute of Technology Bombay（印度理工学院班加罗尔系统控制中心）
Multi-goal Rapidly Exploring Random Tree with Safety and Dynamic Constraints for UAV Cooperative Path Planning
Vietnam National University, Hanoi（越南河内国家大学）；Thai Nguyen University of Technology, Thai Nguyen University（太原大学太原工业学院）；University of Transport and Communications（交通通信大学）；Fulbright University Vietnam（越南富布赖特大学）
Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases
Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
The Hong Kong University of Science and Technology（香港科技大学）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）；College of Computer Science and Engineering, Chongqing Three Gorges University（重庆三峡大学计算机科学与工程学院）；Chinese Academy of Sciences, New Territories, Hong Kong（中国科学院香港新 Territories 分院）；City University of Hong Kong, Kowloon, Hong Kong（香港城市大学）
Path planning is a fundamental and extensively explored problem in robotic control. We present a novel economic perspective on path planning. Specifically, we investigate strategic interactions among path planning agents using a game theoretic path planning framework. Our focus is on economic tension between two important objectives: efficiency in the agents' achieving their goals, and safety in navigating towards these. We begin by developing a novel mathematical formulation for path planning that trades off these objectives, when behavior of other agents is fixed. We then use this formulatio…
Autonomous Systems Lab, ETH Zürich（苏黎世联邦理工学院自主系统实验室）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）；Massachusetts Institute of Technology（麻省理工学院）
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制科学与工程学院 cyber系统研究所）；Polytechnic Institute of Zhejiang University（浙江大学理工学院）；Shanghai AI Laboratory（上海人工智能实验室）
This paper studies the time-optimal path tracking problem for a team of cooperating robotic manipulators carrying an object. Considering the problem for rigidly grasped objects, we show that it can be cast as a convex optimization problem and solved efficiently with a guarantee of optimality. When formulating the problem, we avoid using a particular wrench distribution and exploit the full actuation available to the system. Then, we consider the problem for grasps using frictional forces and show that this problem also, under a force-closure grasp assumption, can be formulated as a convex opti…
This paper addresses the design of an optimization-based cooperative path-following control law for multiple robotic vehicles that optimally balances the transient trade-off between coordination and path-following errors. To this end, we formulate a more general multi-agent framework where each agent is associated with (i) a continuous-time dynamical model, which governs the evolution of its state, and (ii) an output equation that is a function of both the state of the agent and a coordination vector. According to a given network topology, each agent can access its state and coordination vecto…
This paper presents a "cooperative vehicle sorting" strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon. In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in the discrete time-space domain. Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain. The problem is solved by the deterministic Astar algorithm with a stepwise strategy, where only one vehicle can move within a movement step. The resul…
Heriot-Watt University（赫瑞-瓦特大学）；University of Campinas (UNICAMP)（坎皮纳斯大学（UNICAMP））
This work investigates two approaches for the design of autonomous cooperative flight controllers for airship swarms. The first controller is based on formation flight and the second one is based on swarm intelligence strategies. In both cases, the team of airships needs to perform two different tasks: waypoint path following and ground moving target tracking. The UAV platform considered in this work is the NOAMAY airship developed in Brazil. We use a simulated environment to test the proposed approaches. Results show the inherent flexibility of the swarm intelligence approach on both tasks.
