Reactive Model Predictive Contouring Control for Robot Manipulators

2025-08-13

AI Summary

Robot path tracking in dynamic environments struggles to simultaneously ensure obstacle avoidance, singularity avoidance, and self-collision prevention, while suffering from large trajectory deviations during evasion maneuvers. Method: This paper proposes a reactive model predictive contour control framework based on real-time path parameterization. It encodes safety constraints via control barrier functions (CBFs), employs Jacobian linearization and Gauss–Newton Hessian approximation for efficient nonlinear optimization, and achieves high-fidelity trajectory tracking through online path parameterization and receding-horizon optimization at 100 Hz. Contribution/Results: Experiments demonstrate that the framework significantly reduces contour error and peak acceleration, decreases trajectory deviation during avoidance by over 80%, and improves computational efficiency by an order of magnitude compared to state-of-the-art methods—successfully handling multi-source disturbances in real-world dynamic environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executing evasive maneuvers. Thus, this paper derives a method that parametrizes the reference path by a path parameter and performs the optimization via RMPCC. In particular, Control Barrier Functions (CBFs) are introduced to avoid collisions and singularities in dynamic environments. A Jacobian-based linearization and Gauss-Newton Hessian approximation enable solving the nonlinear RMPCC problem at 100 Hz, outperforming state-of-the-art methods by a factor of 10. Experiments confirm that the framework handles dynamic obstacles in real-world settings with low contouring error and low robot acceleration.

Problem

• Avoids obstacles, singularities, and self-collisions dynamically
• Reduces path-following error during evasive maneuvers
• Enables real-time optimization at 100 Hz frequency

Innovation

• Reactive Model Predictive Contouring Control (RMPCC)
• Control Barrier Functions (CBFs) for collision avoidance
• Jacobian-based linearization for 100Hz optimization