Learning fault-tolerant navigation with self-reconfiguring modular robots

Navigating dynamic environments is a fundamental challenge in distributed robotic systems, particularly when faults occur within the system itself, resulting in a changing connectivity graph. Classical graph search algorithms such as A* provide optimal paths as long as the graph is static. However, faults are a part of real life applications and cannot be ignored; classical approaches scale poorly in such scenarios because updating the graph topology requires extensive inter-robot communication, recombination of local maps, and replanning. This paper proposes a reinforcement learning (RL)-based approach that enables agents to learn navigation policies without requiring global knowledge of the graph. Each agent observes only its immediate neighborhood, making locally reasonable decisions about navigating toward a target location that collectively achieve near-optimal global performance. Through training on randomly chosen faults, our model learns robust traversal behaviors that adapt online to topology changes, reducing communication overhead compared to a more basic A*-based approach in a faulty environment.