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A Constraint-Based Approach for Human-Robot Collision Avoidance

Dennis Mronga; Tobias Knobloch; José de Gea Fernández; Frank Kirchner
In: Advanced Robotics, Vol. 0, Pages 1-17, Taylor & Francis Online, 2020.


In this paper we present a software-based approach for collision avoidance that can be applied in human-robot collaboration scenarios. One of the contributions is a method for converting clustered 3D sensor data into computationally efficient convex hull representations used for robot-obstacle distance computation. Based on the computed distance vectors, we generate collision avoidance motions using a potential field approach and integrate them with other simultaneously running robot tasks in a constraint-based control framework. In order to improve control performance, we apply evolutionary techniques for parameter optimization within this framework based on selected quality criteria. Experiments are performed on a dual-arm robotic system equipped with several depth cameras. The approach is able to generate task-compliant avoidance motions in dynamic environments with high performance.


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