A Task-Space Receding Horizon Controller for Fast Collision Avoidance

2026-07-17

AI Summary

This work addresses the challenge of real-time robotic arm control in dynamically cluttered environments, where agents must balance rapid responsiveness with foresightful obstacle avoidance to prevent myopic constraint violations. The authors propose a task-space receding horizon controller that generates collision-free terminal pose references through short-horizon, contact-consistent forward simulations respecting non-penetration constraints, then computes only the first-step minimum-acceleration control input that smoothly transitions toward this reference. By integrating the strengths of receding horizon and reactive control, the method efficiently embeds information about contacts, moving obstacles, and self-collisions using inflated convex geometry and an iterative dynamics solver—without requiring full trajectory optimization. Simulations with 40 degrees of freedom demonstrate that a moderate horizon length effectively balances foresight, responsiveness, and computational cost, while hardware experiments on a 6-DOF manipulator confirm strong sim-to-real transfer, outperforming MPC and dynamic optimization fabric approaches in success rate under dynamic clutter while meeting real-time requirements.

Abstract

Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion and lookahead to avoid becoming trapped by near-future constraints. Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints. Conversely, horizon-free reactive methods are computationally efficient but can be short-sighted in dynamic clutter. We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference satisfying internal non-penetration constraints, then computes only the first input of a smooth minimum-acceleration transition toward that reference. Starting from a closed-loop inverse-kinematics regulation law, the rollout is performed with an iterative dynamics solver operating on inflated convex robot and obstacle geometries, so that robot-obstacle contacts, dynamic obstacle motion, and self-collisions can shape the terminal reference without requiring full constrained trajectory optimization. We analyze the contact-inactive closed loop and show local exponential task-space regulation under standard regularity assumptions. For contacts activated inside the rollout, we characterize the corresponding discrete updates and bound the effect of moving obstacles on regular operating sets. Simulations on a 40-DOF multi-chain system show that intermediate horizons balance anticipation, responsiveness, and computational cost. Hardware experiments on a 6-DOF platform demonstrate consistent sim-to-real behavior without accurate inertial parameter estimation, and comparisons against dynamic optimization fabrics and model predictive control (MPC) baselines show improved success rates in dynamic clutter while preserving solve times compatible with real-time execution in the tested regimes.

Problem

• collision avoidance
• receding horizon control
• robotic manipulators
• dynamic obstacles
• real-time control

Innovation

• receding-horizon control
• collision avoidance
• task-space regulation
• contact-consistent rollout
• real-time robotics