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

TL;DR

AdaptiveON is a novel outdoor navigation algorithm that enhances robot stability, reliability, and efficiency on complex terrains by leveraging a multi-stage training pipeline and rich perception features, bridging the sim-to-real gap.

Contribution

The paper introduces a new outdoor navigation method based on PPO that improves stability, reduces drifting, and handles complex terrains more effectively than existing approaches.

Findings

Improves stability by at least 30.7% on uneven terrains

Reduces drifting by 8.08%

Decreases elevation changes by 14.75%

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 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 of Lidar perception 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 it improves stability by at least 30.7% on uneven terrains, reduces drifting by 8.08% and decreases the elevation changes by 14.75%.