Multi-robot Informative Path Planning using a Leader-Follower Architecture

Abstract

We consider typical scenarios where an autonomous multi-robot team is used for surveying a large region. The desired output is a spatial map of the physical values of interest. Accounting for spatial correlation and uncertainty, the map is modeled using a Gaussian Process. Considering real-world constraints such as limited time budget and collision avoidance, we model team’s mission as a joint informative path planning problem which is tackled using a leader-follower architecture balancing centralized and fully distributed computation of plans. The leader first identifies a convex containment region that is to be sampled by the team. Next, through a combination of Bayesian optimization and Monte Carlo simulation, distinct sampling locations are identified and assigned to the followers. Each follower independently solves an orienteering problem to find a collision-free path maximizing information gain. A team-level adaptive replanning criterion is designed to keep redirecting sampling towards the most informative regions. The algorithm has been validated in computational experiments for map estimation. Compared to a baseline reference algorithm, it has shown a significantly higher accuracy. Moreover, the approach has shown good ability to support network connectivity, as well as good scalability in computation.