Predictive Relative-Velocity Steering for Safe Robotic Manipulator Teleoperation in Dynamic Environments

2026-08-13

AI Summary

This work addresses the challenge of obstacle avoidance in teleoperated robots operating in dynamic environments, where network latency and operator attention limitations hinder safe navigation. To this end, the authors propose a lightweight, modular active obstacle avoidance framework that operates at the end-effector velocity command level. By preprocessing point cloud data to anticipate potential collisions, the method integrates a time-to-collision (TTC) mechanism with overshoot protection. It employs Rodrigues’ rotation formula to deflect the direction of relative velocity without altering its magnitude, thereby circumventing the deadlock issues commonly associated with traditional artificial potential field methods and compensating for perception delays. Experimental results demonstrate that the proposed approach achieves higher obstacle avoidance success rates than baseline methods across diverse simulated scenarios and validates its practical efficacy on a physical robotic platform.

Abstract

Recent advances in teleoperation have enabled robotic manipulators to perform dexterous, human-arm-like motions. However, human operators may fail to avoid suddenly appearing obstacles promptly and effectively, particularly under network latency or limited attention, thereby creating safety risks. To address this issue, we propose a lightweight and modular framework for proactive collision avoidance, operating directly at the end-effector velocity-command level. After preprocessing the point cloud, the framework first predicts potential collisions based on time-to-collision (TTC) with integrated overshoot protection, and subsequently rotates the relative-velocity vector using Rodrigues' rotation formula. The deflection changes only the direction of the relative velocity while preserving its magnitude, thereby mitigating the deadlock problem commonly encountered by conventional artificial potential field (APF) methods. The prediction module compensates for point-cloud processing latency introduced by complex teleoperation pipelines, while the lightweight design enables the high-frequency control required for teleoperation. Simulations across diverse scenarios show that the proposed method achieves a higher end-effector collision avoidance rate than the baseline methods. Experiments on a physical robotic system further validate its collision-avoidance effectiveness.

Problem

• teleoperation
• collision avoidance
• dynamic environments
• safety risks
• obstacle avoidance

Innovation

• predictive collision avoidance
• relative-velocity steering
• time-to-collision (TTC)
• Rodrigues' rotation
• teleoperation safety