Focus Bug: Learning Environmental Awareness for Efficient Mapless Navigation

Abstract

Tiny robots such as nano quadcopters or micro rovers are highly beneficial for various applications as they are inexpensive, agile, and safe for humans. However, their extreme size, weight, and power (SWAP) constraints lead to extremely limited compute, making autonomous navigation challenging. Existing approaches have enabled navigation within these tight constraints, but struggle in dynamic and cluttered scenes.To this end, we present Focus Bug, a novel and robust mapless navigation algorithm that can run on extremely limited hardware. Focus Bug reduces the amount of processed sensory data using a tiny reinforcement learning policy, only processing the inputs necessary for navigation. We use deep reinforcement learning (DRL) to identify critical parts of the robot’s range data and combine it with classical mapless navigation methods to benefit from their robustness and established performance. We implement and evaluate Focus Bug both on a drone in simulation and a micro-rover in the real world to show it can be applied across embodiments. Our hybrid approach outperforms the state-of-the-art in DRL navigation (57% less collisions in dynamic environments) while reducing the amount of range data processed by 87%, and achieving a 2.6X improvement in processing time compared to classical methods. Focus bug is the first method to achieve the high success rate of robust methods (97%) within such a tight compute budget.