Enhanced collision detection method using frequency boundary of dynamic model

Abstract

This paper presents development and experimental evaluation of a collision detection method for robot manipulators sharing workspace with humans. For the safety of cooperative workspace, fast and reliable collision detection is important. In the same time, collision distinction is necessary to prevent false alarm for the performance of workspace. One of the main reasons of false alarm in collision detection is modeling error caused by work pieces or tools. The collision detection algorithm using band pass filter (Band designed Disturbance Observer, BdDOB) is used to eliminate this problem. We discovered the tendency of the manipulator dynamic model's maximum frequency boundary during operations. This tendency is used to design the new BdDOB which contains changing frequency window. Thanks to the dynamics-considered filter band, the collision is successfully distinguished from other false alarm-candidates. In the result, the BdDOB shows not only fast but also robust collision detection performance under the condition of faulty sensor and uncertain model data. The experimental result from collision between a 7 dof serial manipulator and a car crash test dummy is reported.