Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics

Yu, Jingjin; LaValle, Steven M.

doi:10.1109/tro.2016.2593448

Cited by 276 publications

(177 citation statements)

References 50 publications

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“…Another approach to multi-agent planning under uncertainty over a discretized state domain is to employ a decentralized partially observable Markov decision process (POMDP) [88], [89]. Optimality in the multi-robot path planning problem (7) may be with respect to any number of different objectives, including integrated control effort, maximum single-robot travel distance, last arrival time, and total distance or time [90]. Although solving for the exact optimal solutions is NPhard, approximate sub-optimal solutions can be computed efficiently using well-chosen heuristics [90].…”

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

“…Optimality in the multi-robot path planning problem (7) may be with respect to any number of different objectives, including integrated control effort, maximum single-robot travel distance, last arrival time, and total distance or time [90]. Although solving for the exact optimal solutions is NPhard, approximate sub-optimal solutions can be computed efficiently using well-chosen heuristics [90]. One must ensure that the resulting paths are kinematically or dynamically feasible for the robots to follow [91], [92].…”

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…To further verify the synchronous path following capability of MICROMVP, we integrated the optimal graph-based multi-robot path planning algorithm [1] into MICROMVP. Specifically, we tested the algorithm MINMAXDIST that minimizes the maximum distance traveled by any vehicle over a hexagonal grid.…”

Section: B Optimal Multi-robot Path Planningmentioning

confidence: 99%

“…In this paper, we introduce an affordable, portable, and open source multi-vehicle hardware and software platform, MICROMVP 1 ( Fig. 1 (a)), for research and education efforts requiring single or multiple mobile robots.…”

Section: Introductionmentioning

confidence: 99%

A portable, 3D-printing enabled multi-vehicle platform for robotics research and education

Han

Tang

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-MICROMVP is an affordable, portable, and open source micro-scale mobile robot platform designed for robotics research and education. As a complete and unique multi-vehicle platform enabled by 3D printing and the maker culture, MI-CROMVP can be easily reproduced and requires little maintenance: a set of six micro vehicles, each measuring 8 × 5 × 6 cubic centimeters and weighing under 100 grams, and the accompanying tracking platform can be fully assembled in under two hours, all from readily available components. In this paper, we describe MICROMVP's hardware and software architecture, and the design thoughts that go into the making of the platform. The capabilities of MICROMVP APIs are then demonstrated with several single-and multi-robot path and motion planning algorithms. MICROMVP supports all common operation systems.

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“…Centralized methods formulate the motion planning problem as an optimization problem in which information about position, velocity and goal location of all agents are available and the goal is to guide all agents toward their desired locations avoiding collision with each other and minimizing objectives such as energy or time. Yuin et al in [2] present a centralized algorithm based on linear programming (LP) to minimize last agent's arrival time, the maximum (single-agent) traveled distance, the total arrival time, and the total distance. Tang et al in [3] decompose the problem into two phases: the motion planning step and the trajectory generation step.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

Semnani

Liu

Everett

et al. 2020

IEEE Robot. Autom. Lett.

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This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50% more successful scenarios than deep RL and up to 75% less extra time to reach goal than FMP.

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Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics

Cited by 276 publications

References 50 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

A portable, 3D-printing enabled multi-vehicle platform for robotics research and education

Multi-Agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

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