Multi-Robot Path Planning for Mobile Sensing through Deep Reinforcement Learning

Mobile sensing is an effective way to collect environmental data such as air quality, humidity and temperature at low costs. However, mobile robots are typically battery powered and have limited travel distances. To accelerate data collection in large geographical areas, it is beneficial to deploy multiple robots to perform tasks in parallel. In this paper, we investigate the Multi-Robot Informative Path Planning (MIPP) problem, namely, to plan the most informative paths in a target area subject to the budget constraints of multiple robots. We develop two deep reinforcement learning (RL) based cooperative strategies: independent learning through credit assignment and sequential rollout based learning for MIPP. Both strategies are highly scalable with the number of robots. Extensive experiments are conducted to evaluate the performance of the proposed and baseline approaches using real-world WiFi Received Signal Strength (RSS) data. In most cases, the RL based solutions achieve superior or similar performance as a baseline genetic algorithm (GA)-based solution but at only a fraction of running time during inference. Furthermore, when the budgets and initial positions of the robots change, the pre-trained policies can be applied directly.