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Open AccessArticle

Intrinsically Distributed Probabilistic Algorithm for Human–Robot Distance Computation in Collision Avoidance Strategies

1
The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
2
Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(4), 548; https://doi.org/10.3390/electronics9040548
Received: 20 February 2020 / Revised: 11 March 2020 / Accepted: 21 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Bioinspired Computer Vision)
Humans and robots are becoming co-workers, both in industrial and medical applications. This new paradigm raises problems related to human safety. To accomplish and solve this issue, many researchers have developed collision avoidance strategies—mainly relying on potential field approaches—in which attractive and repulsive virtual forces are generated between manipulators and objects within a collaborative workspace. The magnitude of such virtual forces strongly depends on the relative distance between the manipulators and the approaching agents, as well on their relative velocity. In this paper, authors developed an intrinsically distributed probabilistic algorithm to compute distances between the manipulator surfaces and humans, allowing tuning the computational time versus estimation accuracy, based on the application requirements. At each iteration, the algorithm computes the human–robot distances, considering all the Cartesian points within a specific geometrical domain, built around humans’ kinematic chain, and selecting a random subset of points outside of it. Experimental validation was performed in a dynamic and unstructured condition to assess the performance of the algorithm, simulating up to six humans into the shared workspace. Tests showed that the algorithm, with the selected hardware, is able to estimate the distance between the human and the manipulator with a RMSE of 5.93 mm (maximum error of 34.86 mm). View Full-Text
Keywords: collaborative robotics; perception; human–robot collaboration; probabilistic algorithms; collision avoidance strategies collaborative robotics; perception; human–robot collaboration; probabilistic algorithms; collision avoidance strategies
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MDPI and ACS Style

Chiurazzi, M.; Diodato, A.; Vetrò, I.; Ortega Alcaide, J.; Menciassi, A.; Ciuti, G. Intrinsically Distributed Probabilistic Algorithm for Human–Robot Distance Computation in Collision Avoidance Strategies. Electronics 2020, 9, 548.

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