AdaptiveON: Adaptive Outdoor Local Navigation Method for Stable and Reliable Actions

Abstract

We present a novel outdoor navigation algorithm to generate stable and efficient actions to navigate a robot to reach a goal. We use a multi-stage training pipeline and show that our approach produces policies that result in stable and reliable robot navigation on complex terrains. Based on the Proximal Policy Optimization (PPO) algorithm, we developed a novel method to achieve multiple capabilities for outdoor local navigation tasks, namely alleviating the robot's drifting, keeping the robot stable on bumpy terrains, avoiding climbing on hills with steep elevation changes, and avoiding collisions. Our training process mitigates the reality (sim-to-real) gap by introducing generalized environmental and robotic parameters and training with rich features captured from light detection and ranging (Lidar) sensor in a high-fidelity Unity simulator. We evaluate our method in both simulation and real-world environments using Clearpath Husky and Jackal robots. Further, we compare our method against the state-of-the-art approaches and observe that, in the real world, our method improves stability by at least 30.7% on uneven terrains, reduces drifting by 8.08%, and decreases the elevation changes by 14.75%.