A Boosted Decision Tree Approach for a Safe Human-Robot Collaboration in Quasi-static Impact Situations

Publication from Robotics

Nemanja Kovincic, Hubert Gattringer, Andreas Müller, Mathias Brandstötter

Advances in Service and Industrial Robotics. RAAD 2020. Mechanisms and Machine Science, vol 84, Springer International Publishing, pp. 235-244 , 6/2020


According to the ISO/TS 15066, human safety in quasi-static impact situations in human-robot collaboration is assessed first by identifying all high-risk impact situations and then by measuring maximal and steady-state values the impact force and pressure at these possibly critical situations. This means that if something is changed in a collaborative application, the ISO/TS 15066 requires that the risk analysis and the force measurements must be redone, which severely limits the flexibility of a robotic system. In this paper, a physics guided boosted decision tree is proposed as a tool to assess human safety. The basic hypothesis is that a physics guided boosted decision tree can be trained to estimates the peak impact force for a given impact velocity, robot configuration, an impact point on the robot and a human body part. Based on experimental measurements done with the Universal Robots UR10e and on a simple mathematical model of an impact between a point on a robot and a point on a human body part, a feature vector is generated as an input to the boosted decision tree. After the training using Matlab’s Least-squares boosting algorithm, the boosted decision tree can predict the measured peak impact force with a relative error of less than 9% thus supporting the basic hypothesis. However, the predictions of the trained boosted decision tree are valid only for the case of a quasi-static impact in a vertical direction between a robot’s end-effector and a back of human’s non-dominant hand.


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Keywords: Human-robot collaboration, Safety, Robotics, Machine learning, Boosted decision tree, ISO/TS15066