Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning
Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Singh, Nishesh, Ramesh, Sidharth, Shankar, Abhishek, Duttagupta, Jyotishka, D'Souza, Leander Stephen, Singh, Sanjay
Institution: Manipal Academy of Higher Education
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4400141765