Probabilistic Multi-Robot Planning with Temporal Tasks and Communication Constraints

Multi-robot systems are broadly used in applications such as search and rescue, environmental monitoring, and mapping of unknown environments. Effective coordination among these robots often relies on distributed information and local decision-making. However, maintaining constant communication links between robots can be challenging due to environmental and task constraints. Robots can move around to seek temporal communication links that over time jointly establish the intermittent connectivity of the network. This paper aims to incorporate temporal communication constraints into the path planning for multi-robot teams with stochastic motion and handling complex tasks specified in a temporal order. We use formal methods to model the temporal specification of tasks. Task assignments and high-level communication requirements are provided to individual robots on a multi-robot team as independent temporal logic expressions. Robots update their plans for future communication events according to their local decision-making algorithms and jointly synthesize a bottom-up policy to meet the communication requirements. We provide a strategyto maintain intermittent connectivity while satisfying a risk constraint.In addition, we systematically analyze the impact of different rendezvous selection strategies, comparing cost functions that minimize the total traveled distance, balance distances among robots, or incorporate risk awareness. Our simulation results suggest that the proposed method effectively accommodates diverse operational preferences, enhancing flexibility, robustness, and overall mission performance.