A Radial Basis Functions approach to collision avoidance in collaborative tasks

In the last years, collaborative human-robot applications have become more and more appealing thanks to the robot’s easiness of programming and the promise of increasing precision and safety. However, by combining two resources (the cobot and the human operator) there is a problem of safety since cobot and human operator have to work in the same workspace. To ensure human safety, the distance between robot and operator must be assessed and the robot must adapt accordingly either by reducing its velocity or by modifying its trajectory. In this paper, we propose a new online method to adapt the trajectory of the robot to the human movements using a single depth camera. This algorithm eliminates the robot from the scene using a simple calibration process. Then, it interpolates the shared workspace, captured by the depth camera, using Radial Basis Functions (RBFs). The result is a continuous function that is representative of the risk of collision with obstacles on the plane. Its gradient is used as a repulsive potential in the Artificial Potential Field (APF) method to generate the path. This method eliminates the need to calculate the distance between operator and robot since it is intrinsically considered in the potentials. Results shows the validity of the method.