Multi-agent Gaussian Process Motion Planning via Probabilistic Inference

TL;DR

This paper introduces a probabilistic inference-based method for multi-agent motion planning using Gaussian processes, enabling simultaneous trajectory optimization that improves success rates and computational efficiency.

Contribution

It presents a novel approach that jointly optimizes multiple agents' trajectories via probabilistic inference on Gaussian processes, outperforming traditional individual planning methods.

Findings

Significant improvement in success rate over individual planning.

Enhanced computational efficiency in multi-agent trajectory optimization.

Effective joint planning that accounts for future states of all agents.

Abstract

This paper deals with motion planning for multiple agents by representing the problem as a simultaneous optimization of every agent's trajectory. Each trajectory is considered as a sample from a one-dimensional continuous-time Gaussian process (GP) generated by a linear time-varying stochastic differential equation driven by white noise. By formulating the planning problem as probabilistic inference on a factor graph, the structure of the pertaining GP can be exploited to find the solution efficiently using numerical optimization. In contrast to planning each agent's trajectory individually, where only the current poses of other agents are taken into account, we propose simultaneous planning of multiple trajectories that works in a predictive manner. It takes into account the information about each agent's whereabouts at every future time instant, since full trajectories of each agent are found jointly during a single optimization procedure. We compare the proposed method to an individual trajectory planning approach, demonstrating significant improvement in both success rate and computational efficiency.