TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning

TL;DR

This paper introduces TERP, a deep reinforcement learning-based navigation system that reliably guides robots through uneven outdoor terrains by identifying stable regions and planning safe, efficient paths.

Contribution

The paper presents a novel DRL approach using elevation maps and attention mechanisms to improve outdoor robot navigation in uneven terrains, with real-world validation.

Findings

35.18% increase in success rate

26.14% reduction in elevation gradient

Effective navigation in high-elevation regions

Abstract

We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a novel fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attention mask is used to identify reduced stability regions in the elevation map and is computed using channel and spatial attention modules and a novel reward function. We continuously compute and update a navigation cost-map that encodes the elevation information or the level-of-flatness of the terrain using the attention mask. We then generate locally least-cost waypoints on the cost-map and compute the final dynamically feasible trajectory using another DRL-based method. Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in uneven terrains. We observe an increase of 35.18% in terms of success rate and, a decrease of 26.14% in the cumulative elevation gradient of the robot's trajectory compared to prior navigation methods in high-elevation regions. We evaluate our method on a Husky robot in real-world uneven terrains (~ 4m of elevation gain) and demonstrate its benefits.