Robot Navigation in Irregular Environments with Local Elevation Estimation using Deep Reinforcement Learning

We present a novel method for safely navigating a robot in unknown and uneven outdoor terrains. Our approach trains a novel Deep Reinforcement Learning (DRL)-based network with channel and spatial attention modules using a novel reward function to compute an attention map of the environment. The attention map identifies regions in the environment's elevation map with high elevation gradients where the robot could have reduced stability or even flip over. We transform this attention map into a 2D navigation cost-map, which encodes the planarity (level of flatness) of the terrain. Using the cost-map, we formulate a novel method for computing local least-cost waypoints leading to the robot's goal and integrate our approach with DWA-RL, a state-of-the-art navigation method. Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in highly uneven terrains. Our hybrid approach also leads to a low sim-to-real gap, which arises while training DRL networks. We observe an improvement in terms of success rate, the cumulative elevation gradient of the robot's trajectory, and the safety of the robot's velocity. We evaluate our method on a real Husky robot in highly uneven real-world terrains and demonstrate its benefits.