mlsanityData to 14 Oct 2026

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

arXiv 2406.18899 ↗First posted 27 Jun 2024cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 27 Jun 2024.

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9 of 30 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  6. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  7. 2017Equivalence Between Policy Gradients and Soft Q-Learningcited by 173
  8. 2018Benchmarking Reinforcement Learning Algorithms on Real-World Robotscited by 65
  9. 2016Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebocited by 19

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  5. 2026Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control
  6. 2024Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control
  7. 2025Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control
  8. 2024Autonomous Wheel Loader Navigation Using Goal-Conditioned Actor-Critic MPC