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

Arul, Senthil Hariharan; Manocha, Dinesh

doi:10.1109/iros51168.2021.9636618

Cited by 25 publications

(11 citation statements)

References 25 publications

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“…Similarly, waypoints W 11 and W 12 are the centres of the Delaunay triangles of waypoints W 6 to W 9, which made the final layer (layer 3) waypoints. This is similar to the Voronoi tessellation methods ( Arul and Manocha, 2021 ; Chowdhury and De, 2021 ; Inoue et al, 2021 ) in which waypoints are the centers of the Delaunay triangulation circumcircles instead of the centers of the triangles. The AMASE simulator calls the Delaunay triangulation methods and generates the waypoints.…”

Section: The Proposed Solutionmentioning

confidence: 94%

“…( Chawla and Duhan, 2018 ; Sutantyo et al, 2011 ). There are other forms of hybrid methods that use geometric processes, e.g., Voronoi tessellation in pure form or augmented with order processes, e.g., buffering ( Arul and Manocha, 2021 ), k-means algorithm ( Chowdhury and De, 2021 ), gradient descent algorithm ( Inoue et al, 2021 ), area prioritization ( Zarei and Mozafar, 2021 ), and particle swarm optimization ( Zaimen et al, 2021 ). In these methods, the plan generation is controlled by theorems, propositions, lemmas, and protocols of the geometric process.…”

Section: Related Workmentioning

confidence: 99%

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DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Yusuf

Baber²

2022

Front. Robot. AI

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This article describes an approach for multiagent search planning for a team of agents. A team of UAVs tasked to conduct a forest fire search was selected as the use case, although solutions are applicable to other domains. Fixed-path (e.g., parallel track) methods for multiagent search can produce predictable and structured paths, with the main limitation being poor management of agents’ resources and limited adaptability (i.e., based on predefined geometric paths, e.g., parallel track, expanding square, etc.). On the other hand, pseudorandom methods allow agents to generate well-separated paths; but methods can be computationally expensive and can result in a lack of coordination of agents’ activities. We present a hybrid solution that exploits the complementary strengths of fixed-pattern and pseudorandom methods, i.e., an approach that is resource-efficient, predictable, adaptable, and scalable. Our approach evolved from the Delaunay triangulation of systematically selected waypoints to allocate agents to explore a specific region while optimizing a given set of mission constraints. We implement our approach in a simulation environment, comparing the performance of the proposed algorithm with fixed-path and pseudorandom baselines. Results proved agents’ resource utilization, predictability, scalability, and adaptability of the developed path. We also demonstrate the proposed algorithm’s application on real UAVs.

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Section: The Proposed Solutionmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Yusuf

Baber²

2022

Front. Robot. AI

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“…Xu 17 used dueling double deep Q-learning (D3QN) and set a sub-goal in the agent's field of view to help plan a feasible and safe path. Arul 18 proposed a decentralized collision avoidance based on buffered Voronoi cells and reciprocal velocity obstacle. Long 19 presented a multi-scenario multi-stage framework to learn a policy, which can achieve a sensor-level collision avoidance.…”

Section: Related Workmentioning

confidence: 99%

Path planning for multiple agents in an unknown environment using soft actor critic and curriculum learning

Sun

Yan

Qin

2022

Computer Animation & Virtual

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Path planning can guarantee that agents reach their goals without colliding with obstacles and other agents in an optimal way and it is a very important component in the research of crowd simulation. In this article, we propose a novel path planning approach for multiple agents which combines soft actor critic (SAC) algorithm and curriculum learning to solve the problems of single policy, slow convergence of the policy in an unknown environment with sparse rewards. The path planning task is set as lessons from easy to difficult, and the neural network of the SAC algorithm is arranged to learn in sequence, and finally the neural network can be fully competent for the path planning task. We also stack the state information to address the problems caused by limited observation for policy learning, and design a comprehensive reward function to make agents reach their goals successfully and avoid collisions with static obstacles and other agents. The experimental results demonstrate that our approach can plan smooth and natural paths for multiple agents, and furthermore, our model has a certain generalization ability and a better adaptability to the changes in a dynamic environment.

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“…In contrast to VO-based formulations, BVC only requires the agents' position information to compute collision-free paths. RVO and BVC is used for multi-agent navigation in [26].…”

Section: B Decentralized Plannersmentioning

confidence: 99%

Dense Multi-Agent Navigation Using Voronoi Cells and Congestion Metric-based Replanning

Arul¹,

Manocha²

2022

Preprint

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We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in scenarios with multiple corridors. To handle dense scenes with narrow passages, our approach computes the initial path for each agent to its assigned goal using a lattice planner. Based on neighbors' information, each agent performs online replanning using a congestion metric that tends to reduce the collisions and improves the navigation performance. Furthermore, we use the Voronoi cells of each agent to plan the local motion as well as a corridor selection strategy to limit the congestion in narrow passages. We evaluate the performance of our approach in complex warehouse-like scenes with hundreds of agents and demonstrate improved performance and efficiency over prior methods. In addition, our approach results in a higher success rate in terms of collision-free navigation to the goals.

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

Cited by 25 publications

References 25 publications

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Path planning for multiple agents in an unknown environment using soft actor critic and curriculum learning

Dense Multi-Agent Navigation Using Voronoi Cells and Congestion Metric-based Replanning

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