Home /Research /Research on Collision Detection of Collaborative Robot using improved Momentum-based Observer
OTHER

Research on Collision Detection of Collaborative Robot using improved Momentum-based Observer

Yang Jiang, Yao Wu, Xingmao Wu, Bin Zhao

Year
2023
Citations
6

Abstract

With the increasing application of collaborative robots, it is essential to ensure staff safety during human-machine collaboration, so the robot collision detection problem must be addressed. Traditional collision detection requires external sensors, which increases the cost and the structure's complexity. This paper constructed a generalized momentum-based observer to detect external collisions and introduced a compensation link and variable damping design to improve collision detection speed and accuracy. At the same time, after witnessing an external collision, a collision response strategy based on time-scaling was adopted to protect staff safety. Through algorithm simulation, it was verified that the improved momentum-based observer had significantly improved the real-time performance and detection accuracy. At last, the proposed collision detection algorithm was carried out on a collaborative robot with 6 degrees of freedom. The results show that the proposed collision detection algorithm can accurately detect external collisions without external sensors and effectively protect staff safety.

Keywords

Collision detectionCollisionObserver (physics)RobotComputer scienceCollision avoidanceCompensation (psychology)SimulationControl theory (sociology)Real-time computing

Related papers

Browse all OTHER papers