Collision detection algorithm robust to model uncertainty

With the widespread use of service robots, safety issues regarding human-robot collisions have received increasing attention. The collision detection algorithm, which allows a robot to effectively detect and react against a collision, is considered as one of the most practical solutions for ensuring collision safety. However, these algorithms are often model-based, so it cannot ensure collision safety under payload variations or model uncertainty. In this paper, a novel collision detection algorithm based on torque filtering is proposed to cope with this problem. The torque due to the motion of the robot can be effectively removed using the Butterworth 2nd-order BPF (band pass filter) so that only the torque due to a collision is used for collision detection. This improves the robustness of the algorithm against model uncertainties. The proposed algorithm does not require the use of acceleration data. The performance of the algorithm was experimentally verified.     

Collision detection and reaction on 7 DOF service robot arm using residual observer

The use of robots in service and manufacturing industries has increased significantly in past decades. These robots share the same workspace with humans, so physical interactions between humans and robots occur. Thus, collision between a human and a robot is inevitable during these interactions, and safety measures must be provided to protect humans. In this study, a disturbance observer based on generalized momentum is proposed to efficiently detect a collision. The proposed algorithm of the disturbance observer allows any multi-DOF robot to effectively detect a collision without the use of additional sensors or mechanisms. Once a collision is detected, the robot can perform the appropriate reaction to minimize the impact on the human and the robot. Several reaction strategies are proposed for different situations. The proposed algorithm was implemented on a 7 DOF robot arm to verify its performance. Furthermore, the proposed reaction strategies were also demonstrated using the same robot arm.     

Collision Detection Algorithm to Distinguish Between Intended Contact and Unexpected Collision

Industrial and service robots often physically interact with humans, and thus, human safety during these interactions becomes significantly important. Several solutions have been proposed to guarantee human safety, and one of the most practical, efficient solutions is the collision detection using generalized momentum and joint torque sensors. This method allows a robot to detect a collision and react to it as soon as possible to minimize the impact. However, the conventional collision detection methods cannot distinguish between intended contacts and unexpected collisions, and thus they cannot be used during certain tasks such as teaching and playback or force control. In this paper, we propose a novel collision detection algorithm which can distinguish intended contacts and unexpected collisions. In most cases, the external force during a collision shows a noticeably faster rate of change than that during an intended contact, and using this difference, the proposed observer can distinguish one from the other. Several experiments were conducted to show that the proposed algorithm can effectively distinguish intended contacts and unexpected collisions.