Abstract
This letter introduces a dual-type proximity sensor and a control strategy for a robot manipulator to realize safe human-robot interactions (HRI) by using the sensor. Safety is an essential condition for HRI in practical scenarios. To achieve this condition, information about the relationship between an external objects and the robot is required. To obtain this information, we employ a dual-type proximity sensor, which consists of capacitive and inductive transducers and can detect the distance between a robot and external objects. Further, we propose a real-time trajectory planning method to deal with obstacles by using admittance control and distance measurements. To update the motion of the manipulator according to our control strategy, a Weight-Prioritized solution based on a QP (quadratic programming) formalism was applied. Further, the problem of self-sensing is solved via machine learning using a training dataset consisting of data corresponding to random joint positions. The proposed method was implemented on a collaborate robot (UR10). Experiments were conducted considering realistic human-robot interactions, and safety improvement was validated.
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