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

TL;DR

V-RVO is a decentralized collision avoidance method for dense multi-agent environments that combines Voronoi diagrams with reciprocal velocity obstacles to ensure safe, efficient, and passive-friendly navigation.

Contribution

It introduces a novel decentralized approach integrating buffered Voronoi cells with RVO, enabling collision avoidance with deadlock resolution in dense scenarios.

Findings

Performs comparably to prior velocity-obstacle methods

Less conservative than ORCA in collision avoidance

Effective in scenarios with tens of agents in close proximity

Abstract

We present a decentralized collision avoidance method for dense environments that is based on buffered Voronoi cells (BVC) and reciprocal velocity obstacles (RVO). Our approach is designed for scenarios with large number of close proximity agents and provides passive-friendly collision avoidance guarantees. The Voronoi cells are superimposed with RVO cones to compute a suitable direction for each agent and we use that direction for computing a local collision-free path. Our approach can satisfy double-integrator dynamics constraints and we use the properties of the BVC to formulate a simple, decentralized deadlock resolution strategy. We demonstrate the benefits of V-RVO in complex scenarios with tens of agents in close proximity. In practice, V-RVO's performance is comparable to prior velocity-obstacle methods and the collision avoidance behavior is significantly less conservative than ORCA.