Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

Avram, Oliver and Baraldo, Stefano and Valente, Anna (2022) Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments. Frontiers in Robotics and AI, 9. ISSN 2296-9144

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Abstract

Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily (e.g., oil and gas, energy generation, aeronautics) cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system’s high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human’s emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human’s health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments.

Item Type:	Scientific journal article, Newspaper article or Magazine article
Subjects:	Engineering > Production & manufacturing engineering > Production & manufacturing engineering not elsewhere classified Computer sciences > Artificial intelligence > Knowledge representation Computer sciences > Artificial intelligence > Cognitive modelling
Department/unit:	Dipartimento tecnologie innovative > Istituto sistemi e tecnologie per la produzione sostenibile
Depositing User:	Stefano Baraldo
Date Deposited:	29 Sep 2022 14:33
Last Modified:	29 Sep 2022 14:33
URI:	http://repository.supsi.ch/id/eprint/13560

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