Conflict-based search for optimal multi-agent pathfinding
TLDR
A new search algorithm called Conflict Based Search (CBS), which enables CBS to examine fewer states than A* while still maintaining optimality and shows a speedup of up to a full order of magnitude over previous approaches.About:
This article is published in Artificial Intelligence.The article was published on 2015-02-01 and is currently open access. It has received 433 citations till now. The article focuses on the topics: Pathfinding.read more
Citations
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Journal ArticleDOI
Trajectory Planning for Quadrotor Swarms
TL;DR: The proposed method can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes, and is demonstrated on a quadrotor swarm navigating in a warehouse setting.
Posted Content
Optimal Multi-Robot Path Planning on Graphs: Complete Algorithms and Effective Heuristics
Jingjin Yu,Steven M. LaValle +1 more
TL;DR: The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of 1.x-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds.
Journal ArticleDOI
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios
TL;DR: A decentralized sensor-level collision-avoidance policy for multi-robot systems, which enables a robot to make effective progress in a crowd without getting stuck and has been successfully deployed on different types of physical robot platforms without tedious parameter tuning.
Proceedings Article
ICBS: improved conflict-based search algorithm for multi-agent pathfinding
TL;DR: ICBS is introduced, an improved version of CBS that incorporates three orthogonal improvements to CBS which are systematically described and studied and reduces the runtime over basic CBS by up to 20x in many cases.
Posted Content
A Review of Cooperative Multi-Agent Deep Reinforcement Learning
TL;DR: This review article has mostly focused on recent papers on Multi-Agent Reinforcement Learning (MARL) than the older papers, unless it was necessary, and discussed some new emerging research areas in MARL along with the relevant recent papers.
References
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A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Proceedings ArticleDOI
A performance comparison of multi-hop wireless ad hoc network routing protocols
TL;DR: The results of a derailed packet-levelsimulationcomparing fourmulti-hopwirelessad hoc networkroutingprotocols, which cover a range of designchoices: DSDV,TORA, DSR and AODV are presented.
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Depth-first iterative-deepening: an optimal admissible tree search
TL;DR: This heuristic depth-first iterative-deepening algorithm is the only known algorithm that is capable of finding optimal solutions to randomly generated instances of the Fifteen Puzzle within practical resource limits.
Journal ArticleDOI
A multiagent approach to autonomous intersection management
Kurt Dresner,Peter Stone +1 more
TL;DR: This article suggests an alternative mechanism for coordinating the movement of autonomous vehicles through intersections and demonstrates in simulation that this new mechanism has the potential to significantly outperform current intersection control technology--traffic lights and stop signs.
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Generalized best-first search strategies and the optimality of A*
Rina Dechter,Judea Pearl +1 more
TL;DR: It is shown that several known properties of A* retain their form and it is also shown that no optimal algorithm exists, but if the performance tests are confirmed to cases in which the estimates are also consistent, then A* is indeed optimal.