Autonomous Mobile Robots Lab

Mobile robots deployed for persistent operations in partially known environments need to be able to recover and adapt against unforeseen changes in dynamics, e.g., due to failures, or external disturbances. This paper presents a novel hierarchical framework capable of zero-shot adaptation to environmental and dynamic changes. At the high level, an abstract planner generates a collision-free global path, adapting to degraded mobility by inflating a dynamic safety buffer around obstacles to ensure the route remains navigable. At the low level, a concrete planner employs a conditional Denoising Diffusion Probabilistic Model (DDPM) to refine the abstract path into a smooth, executable trajectory. The key to our approach is conditioning the diffusion model's generation process on the robot's online-estimated dynamic limits. Our framework's effectiveness and robustness are validated in both complex simulations and real-world hardware experiments, demonstrating its ability to ensure mission success under unstructured and unexpected fault situations.