Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning-based control system for a novel five-bar active suspension in planetary rovers, enhancing stability and obstacle traversal in rugged terrains through simulation validation.

Contribution

It introduces a new suspension control method combining SAC and PID for improved chassis stabilization and obstacle navigation in planetary exploration robots.

Findings

Effective chassis stabilization demonstrated in Gazebo simulations.

Enhanced obstacle traversal capability at low speeds.

Integration of SAC with traditional PID control improves suspension performance.

Abstract

Planetary exploration requires traversal in environments with rugged terrains. In addition, Mars rovers and other planetary exploration robots often carry sensitive scientific experiments and components onboard, which must be protected from mechanical harm. This paper deals with an active suspension system focused on chassis stabilisation and an efficient traversal method while encountering unavoidable obstacles. Soft Actor-Critic (SAC) was applied along with Proportional Integral Derivative (PID) control to stabilise the chassis and traverse large obstacles at low speeds. The model uses the rover's distance from surrounding obstacles, the height of the obstacle, and the chassis' orientation to actuate the control links of the suspension accurately. Simulations carried out in the Gazebo environment are used to validate the proposed active system.