V-RVO：基于沃罗诺伊图和倒速度障碍的分散式多智能体冲突避免
V-RVO: Decentralized Multi-Agent Collision Avoidance using Voronoi Diagrams and Reciprocal Velocity Obstacles
摘要：
提出了一种基于缓冲Voronoi单元（BVC）和倒速度障碍（RVO）的密集环境下的分散式避碰方法。我们的方法是专为场景与大量的近距离代理，并提供被动友好的碰撞避免保证。Voronoi细胞与RVO锥叠加，以计算一个合适的方向为每个代理，我们使用该方向计算一个本地的无碰撞路径。我们的方法可以满足双积分动力学约束，我们使用的性质BVC制定一个简单的，分散的死锁解决策略。我们展示了V-RVO在复杂场景中的优势，其中有数十个代理人非常接近。在实践中，V-RVO的性能与以前的速度障碍物方法相当，避碰行为明显不如ORCA保守。
英文摘要：
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.
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