V-RVO: Decentralized Multi-Agent Collision Avoidance using Voronoi Diagrams and Reciprocal Velocity Obstacles

Arul, Senthil Hariharan; Manocha, Dinesh

doi:10.48550/arxiv.2102.13281

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…According to different ways of graph representation, global path planning can be divided into A* algorithm 2 and the combination of A* with data-driven approach, 3 probabilistic roadmaps, 4 navigation meshes, 5 navigation graphs, 6 and Voronoi diagrams. 7 Local collision avoidance steers the preferred velocity away from collisions with other agents in which the environment is unknown or known partially. Local collision avoidance methods have been proposed including rule-based methods, 8 force-based methods, 9 optimization-based methods, 10 and extended methods taking psychological factors into consideration.…”

Section: Related Workmentioning

confidence: 99%

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

Sun

Qin

2022

Computer Animation & Virtual

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We propose a virtual crowd navigation approach based on deep reinforcement learning to improve the adaptability of virtual crowds in an unknown and complex environment. To address the problem of local optimum or slow iteration or even failure to converge due to sparse rewards in complex environments, we integrate the curiosity-driven mechanism, the key navigation points acquisition and the failure path penalty method in addition to combining long short-term memory networks, dynamic obstacle collision prediction with proximal policy optimization algorithms, which realizes the crowd navigation in a complex environment. The experimental results show that the proposed approach can simulate the motions of virtual crowds in various dynamic and complex scenarios without the environment modeling. The use of continuous action space also ensures that the movement trajectories of the virtual crowds are more realistic and natural. Furthermore, our approach can provide analysis and demonstration tools for a variety of independent collaborations such as competition and cooperation of group intelligence in an open and dynamic environment.

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Section: Related Workmentioning

confidence: 99%