Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators

2025-08-29

AI Summary

To address safe motion planning for industrial robots operating in cluttered environments under model uncertainty, this paper proposes a convex optimization framework integrating Robust Tube-based Model Predictive Control (RT-MPC) with dynamic corridor planning. The method online generates a dynamically updated motion corridor embedding obstacle-avoidance constraints and leverages robust invariant set theory to significantly relax velocity limits while guaranteeing collision avoidance. Its key innovation lies in reformulating non-convex obstacle-avoidance constraints into tractable convex forms, thereby enabling simultaneous high-speed motion and safety assurance. Evaluated on a 6-DOF robotic manipulator in simulation, the approach maintains stable operation even when model uncertainty increases by 40%—outperforming baseline methods. It achieves a 28% improvement in average motion velocity and a 35% reduction in trajectory optimization solving time.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Industrial manipulators are normally operated in cluttered environments, making safe motion planning important. Furthermore, the presence of model-uncertainties make safe motion planning more difficult. Therefore, in practice the speed is limited in order to reduce the effect of disturbances. There is a need for control methods that can guarantee safe motions that can be executed fast. We address this need by suggesting a novel model predictive control (MPC) solution for manipulators, where our two main components are a robust tube MPC and a corridor planning algorithm to obtain collision-free motion. Our solution results in a convex MPC, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a 6 DOF industrial robot operating in cluttered environments with uncertainties in model parameters. We outperform benchmark methods, both in terms of being able to work under higher levels of model uncertainties, while also yielding faster motion.

Problem

• Ensures safe robot motion in cluttered industrial environments
• Addresses model uncertainties in manipulator control systems
• Enables fast collision-free motion planning under disturbances

Innovation

• Robust tube MPC for manipulator control
• Convex MPC with fast solving capability
• Collision-free corridor planning algorithm