Multiple approaches to the estimation of highorder motion derivatives for innovative control applications now rely on the data collected by redundant arrays of inertial sensors mounted on robots, with promising results. However, most of these works suffer scalability issues induced by the considerable amount of data generated by such large-scale distributed sensor systems. In this article, we propose a new adaptive sensor-selection algorithm, for distributed inertial measurements. Our approach consists in using the data of a subset of sensors, selected among a larger collection of inertial sensing elements covering a rigid robot link. The sensor selection process is formulated as an optimization problem, and solved using a projected gradient heuristics. The proposed method can run online on a robot and be used to recalculate the selected sensor arrangement on the fly when physical interaction or potential sensor failure is detected. The tests performed on a simulated UR5 industrial manipulator covered with a multimodal artificial skin, demonstrate the consistency and performance of the proposed sensor-selection algorithm. Index Terms—Acceleration Feedback, Artificial Robot Skin, Automatic Sensor Selection, Greedy Algorithm     «

Multiple approaches to the estimation of highorder motion derivatives for innovative control applications now rely on the data collected by redundant arrays of inertial sensors mounted on robots, with promising results. However, most of these works suffer scalability issues induced by the considerable amount of data generated by such large-scale distributed sensor systems. In this article, we propose a new adaptive sensor-selection algorithm, for distributed inertial measurements. Our ap...     »