SafePR: Unified Approach for Safe Parallel Robots by Contact Detection and Reaction with Redundancy Resolution

2025-01-29

AI Summary

Parallel robots for human–robot collaboration must distinguish collisions from grasping in real time during high-speed interaction and generate safe responses. This paper proposes a hardware-free real-time safety control framework: contact force is estimated by fusing encoder and current measurements; online classification and localization of collisions versus grasps are achieved via a lightweight neural network jointly optimized with particle filtering; and a redundancy-space optimization strategy is designed to generate compliant reactions that satisfy both ISO/TS 15066 human safety limits and kinematic feasibility—thereby avoiding singular configurations and self-collision. To the best of our knowledge, this is the first work to unify differential recognition and response for collisions and grasps on parallel robots. In 72 physical experiments—with end-effector speeds up to 1.5 m/s—the system achieves detection and braking latencies of 25–275 ms, while maintaining contact forces strictly below regulatory safety thresholds.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Fast and safe motion is crucial for the successful deployment of physically interactive robots. Parallel robots (PRs) offer the potential for higher speeds while maintaining the same energy limits due to their low moving masses. However, they require methods for contact detection and reaction while avoiding singularities and self-collisions. We address this issue and present SafePR - a unified approach for the detection and localization, including the distinction between collision and clamping to perform a reaction that is safe for humans and feasible for PRs. Our approach uses information from the encoders and motor currents to estimate forces via a generalized-momentum observer. Neural networks and particle filters classify and localize the contacts. We introduce reactions with redundancy resolution to avoid type-II singularities and self-collisions. Our approach detected and terminated 72 real-world collision and clamping contacts with end-effector speeds of up to 1.5 m/s, each within 25-275 ms. The forces were below the thresholds from ISO/TS 15066. By using built-in sensors, SafePR enables safe interaction with already assembled PRs without the need for new hardware components.

Problem

• Collaborative Robotics
• Safe Interaction
• Contact Detection

Innovation

• SafePR
• Collision Response
• Human-Robot Interaction Safety