Multi-Robot Ergodic Trajectory Optimization with Relaxed Periodic Connectivity

Abstract

This paper considers a multi-robot trajectory planning problem with inter-robot connectivity maintenance for information gathering. Given an information map in the form of a distribution over the workspace, ergodic search plans trajectories, along which, the time spent in any region is proportional to the amount of information in that region, and can balance between exploration and exploitation. Existing ergodic search rarely considers the limited communication range among robots or connectivity maintenance, and this paper takes a step to fill this gap. Besides, multi-robot connectivity maintenance was studied a lot, including continual, periodic, intermittent connectivity, etc. Naively combining these methods with ergodic search may prevent the planner from finding high-quality ergodic trajectories or lead to poor connectivity among the robots. To handle the challenge, this paper adapts an intermittent connectivity maintenance strategy to the ergodic search framework, and develops a two-phase trajectory planning approach utilizing the augmented Lagrangian method. Our simulation and real drone experiments show that under the same connectivity maintenance requirement, our approach plans trajectories that are about 10 times better than the baselines in terms of the ergodic metric.