Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning
This addresses the need to protect sensitive equipment on Mars rovers from mechanical damage during exploration, though it is incremental as it builds on existing control methods.
The paper tackled the problem of planetary rover traversal over rugged terrain by developing an active suspension system using Soft Actor-Critic (SAC) and PID control to stabilize the chassis and navigate obstacles, validated through simulations in Gazebo.
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.