Collision Detection for Industrial Applications

In the manufacturing industry, the request of complex products and the decreasing of production time have led to more and more sophisticated CNC-controlled multi-axis machines. Often their setup process is affected by different mistakes caused by the persons responsible that lead to collisions inside the working area. Those collisions often lead to damage the tool and the work piece. The thesis deals with this problem, providing new insights for a fast and robust collision detection. Imagining to start from scratch, through a dynamic analysis of the impact in a mechanical transmission, we reached to identify the sensors which provide the optimal trade-off between the quality of impact information measured, feasibility and costs. Then, we propose two new collision detection algorithms able to identify the unwanted event as fast as possible, with the goal to reduce the impact force and containing the damage. Furthermore, their performance are compared with the most successful algorithm found in literature on two different mechanical systems: a heavy automatic access gate and the laboratory’s robotic arm.