A Constraint-Based Approach for Human-Robot Collision Avoidance
Dennis Mronga, Tobias Knobloch, José de Gea Fernández, Frank Kirchner
In Advanced Robotics, Taylor & Francis Online, volume 0, pages 1-17, 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.
Collision Avoidance, Human-Robot Collaboration, Real-Time Robot Control, Parameter Optimization