Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning

Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this letter, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D environment while operating under resource constraints (such as path length). Here, each robot aims to maximize discovered targets, avoid unknown static obstacles, and prevent inter-robot collisions while operating under communication and resource constraints. We utilize the centralized training and decentralized execution paradigm to train a single policy neural network. A key aspect of our approach is our coordination graph that prioritizes visiting regions not yet explored by other robots. Our learned policy can be copied onto any number of robots for deployment in more complex environments not seen during training. Our approach outperforms state-of-the-art approaches by at least <inline-formula><tex-math notation="LaTeX">$\mathbf {26.2\%}$</tex-math></inline-formula> in terms of the number of discovered targets while requiring a planning time of less than <inline-formula><tex-math notation="LaTeX">$\mathbf {2}$</tex-math></inline-formula> sec per step. We present results for more complex environments with up to <inline-formula><tex-math notation="LaTeX">$\mathbf {64}$</tex-math></inline-formula> robots and compare success rates against baseline planners.