深層強化学習による新型閉ループ5節リンクアクティブサスペンションの自律制御
Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning
惑星探査ローバーの車体安定化と障害物走破のため、閉ループ5節リンクのアクティブサスペンションをSACとPIDで制御する手法を提案し、Gazeboシミュレーションで検証した。
著者: Nishesh Singh, Sidharth Ramesh, Abhishek Shankar, Jyotishka Duttagupta, Leander Stephen D'Souza, Sanjay Singh
分類: cs.RO, cs.AI
原文アブストラクト
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.