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

• URL: http://arxiv.org/abs/2406.18899v3
• Date: Thu, 4 Jul 2024 04:12:25 GMT
• Title: Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning
• Authors: Nishesh Singh, Sidharth Ramesh, Abhishek Shankar, Jyotishka Duttagupta, Leander Stephen D'Souza, Sanjay Singh,
• Abstract summary: This paper deals with an active suspension system focused on chassis stabilisation. SAC was applied along with Proportional Integral Derivative (PID) control to stabilise the chassis. 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.
• Score: 0.6456676618238324
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
  This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
  arXiv  Detail & Related papers  (2024-10-03T17:50:19Z)
• Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots [50.02055068660255]
  Navigating urban environments poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain.
  arXiv  Detail & Related papers  (2024-05-03T00:29:20Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning [0.0]
  Compensating for slip and skid is crucial for mobile robots navigating outdoor terrains. This paper proposes a novel trajectory tracking technique featuring real-world feasible online slip and skid compensation. Experimental results demonstrate a significant improvement, enhancing the trajectory tracking system's performance by over 27%.
  arXiv  Detail & Related papers  (2023-09-16T03:58:03Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers [11.29285364660789]
  Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation. Previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots. In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing.
  arXiv  Detail & Related papers  (2022-09-22T14:38:10Z)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning [9.891207216312937]
  Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility. We propose a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to guide autonomous UAS to their destinations. Results show that the proposed model can provide accurate and robust guidance and resolve conflict with a success rate of over 99%.
  arXiv  Detail & Related papers  (2021-11-13T04:44:53Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Learning Robust Hybrid Control Barrier Functions for Uncertain Systems [68.30783663518821]
  We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
  arXiv  Detail & Related papers  (2021-01-16T17:53:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.