Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots

• URL: http://arxiv.org/abs/2604.24518v1
• Date: Mon, 27 Apr 2026 14:20:10 GMT
• Title: Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots
• Abstract summary: This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance.<n>The proposed architecture guarantees tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints.<n>The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments.
• Score: 1.7044368816068716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile robots. By formulating a generalized kinematic transformation, we convert diverse vehicle dynamics into a strict feedback form, facilitating the design of a Sliding Mode Control (SMC) strategy for precise and robust reference tracking. To ensure operational safety in dynamic environments, the tracking controller is integrated with a Collision Cone Control Barrier Function (C3BF) based safety filter. The proposed architecture guarantees asymptotic tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints. The novelty of this work lies in designing a sliding mode controller for ground robots like the Ackermann drive, which has not been done before. The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments on three distinct platforms: an Ackermann-steered vehicle, a differential drive robot, and a quadrotor drone. Video of the experiments are available at https://youtu.be/dWcxwum96vk

Related papers

• Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions [45.68209588239261]
  This work proposes a safe local motion planning and control method that guarantees that a polytopic robot footprint stays inside a convex free-space region.<n>The proposed free-space formulation does not need any obstacle detection or segmentation.<n>The approach is validated in simulation with an autonomous surface vehicle and on hardware with a non-holonomic mobile robot.
  arXiv  Detail & Related papers  (2026-06-08T16:44:28Z)
• Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments [14.321190258774358]
  We present a real-time control architecture that integrates a 3D LIDAR perception-based composite control barrier function.<n>We validate the system through multiple field experiments in underground environments.
  arXiv  Detail & Related papers  (2026-05-15T09:42:13Z)
• EMMa: End-Effector Stability-Oriented Mobile Manipulation for Tracked Rescue Robots [14.473975238280538]
  This paper presents a motion generation framework for tracked mobile manipulators to achieve stable end-effector operation in complex rescue scenarios.<n>The proposed framework consistently outperforms SOTA methods across key metrics, including task success rate and end-effector motion stability.
  arXiv  Detail & Related papers  (2026-04-09T14:28:41Z)
• A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance [45.63674336069651]
  Soft continuum manipulators provide passive safety through mechanical compliance.<n>Existing approaches, such as sampling-based planning, are often computationally expensive and lack formal safety guarantees.<n>This paper presents a closed-form Control Lyapunov Function--Control Barrier Function (CLF--CBF) controller for real-time 3D obstacle avoidance.
  arXiv  Detail & Related papers  (2026-03-19T19:34:49Z)
• OmniTrack: General Motion Tracking via Physics-Consistent Reference [54.256312586493415]
  We introduce OmniTrack, a general tracking framework that explicitly decouples physical feasibility from general motion tracking.<n>Experiments show that OmniTrack improves tracking accuracy and demonstrates strong generalization to unseen motions.<n>In real-world tests, OmniTrack achieves hour-long, consistent, and stable tracking, including complex acrobatic motions such as flips and cartwheels.
  arXiv  Detail & Related papers  (2026-02-27T09:12:15Z)
• Finite-Time Control Based on Differential Flatness for Wheeled Mobile Robots with Experimental Validation [0.0]
  A robust tracking control strategy is designed to empower wheeled mobile robots (WMRs) to track predetermined routes while operating in diverse fields.<n>A novel integral nonlinear hyperplane-based sliding mode control (INH-SMC) technique is proposed for WMR under disturbances.<n>Its practical viability is demonstrated through a comparative real-world indoor experiment on a TurtleBot3 WMR subjected to disturbances.
  arXiv  Detail & Related papers  (2025-12-23T10:41:04Z)
• CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
  hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-16T18:56:04Z)
• Disturbance Compensation for Safe Kinematic Control of Robotic Systems with Closed Architecture [5.705899925573079]
  In commercial robotic systems, it is common to encounter a closed inner-loop torque controller that is not user-modifiable.<n>In this work, we focus on the development of an easily integrated add-on at the outer-loop layer.<n>Our solution demonstrates superior performance in terms of simplicity of implementation, robustness, tracking precision, and safety.
  arXiv  Detail & Related papers  (2025-12-04T22:29:12Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• 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)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
  We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
  arXiv  Detail & Related papers  (2021-03-26T07:14:01Z)

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.