Proxemic group behaviors using reciprocal multi-agent navigation

We present a decentralized algorithm for group-based coherent and reciprocal multi-agent navigation. In addition to generating collision-free trajectories for each agent, our approach is able to simulate macroscopic group movements and proxemic behaviors that result in coherent navigation. Our approach is general, makes no assumptions about the size or shape of the group, and can generate smooth trajectories for the agents. Furthermore, it can dynamically adapt to obstacles or the behavior of other agents. The additional overhead of generating proxemic group behaviors is relatively small and our approach can simulate hundreds of agents in real-time. We highlight its benefits on different benchmarks.

I. Introduction

Multi-agent navigation algorithms are widely used for motion planning among static and dynamic obstacles [1], for autonomous localization [2], and in the simulation of animated characters or human crowds in games and virtual worlds [3]. A key issue in these applications is collision-free path planning: given a set of agents in a static or a dynamic environment, each with its own initial and goal positions, computing collision-free trajectories for each agent towards its goal. In some scenarios, there are additional constraints imposed on the trajectories corresponding to dynamics, biomechanics, or human-like behaviors.