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A survey on underactuated robotic systems: Bio-inspiration,trajectory planning and control
Affiliation:1. Department of Computer Science, University of York, Heslington YO10 5GH, United Kingdom;2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, United Kingdom;3. Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom;4. School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, United Kingdom;1. School of Aerospace, Transport and Manufacturing, Cranfield University, UK;2. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, UK;1. Higher Institute of Information and Communication Technologies, University of Carthage, 1164 Borj Cedria, Tunis, Tunisia;2. Laboratory of Robotics, Informatics and Complex Systems (RISC Lab-LR16ES07), National Engineering School of Tunis, University of Tunis El Manar, BP. 37, Le Belvédère, 1002 Tunis, Tunisia
Abstract:Underactuated robotic systems have become an important research topic aiming at significant improvement of the behavioural performance and energy efficiency. Adopting some bio-inspired ideas and properties, the self-organisation and main tasks of the robotic systems can be achieved by coordination of the subsystems and dynamic interaction with the environment. Conversely, biological systems achieve energy efficient and adaptive behaviours through extensive autologous and exogenous compliant interactions. The "trick" that give rise to the lifelike movements is appropriate application of the bio-inspired ideas and properties, and construction of control systems in a generally underactuated system. In this paper, we aim to strengthen the links between two research communities of robotics and control by presenting a systematic survey work in underactuated robotic systems, in which both key challenges and notable successes in bio-inspiration, trajectory planning and control are highlighted and discussed. One particular emphasis of this article lies on the illustration of roles of bio-inspired properties, control algorithms and prior knowledge in achieving these successes and specifically, how they contribute to the taming of the complexity of the linked domains. We demonstrate how bio-inspiration and control methods may be profitably applied, and we also note throughout open questions and the tremendous potential for future research.
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