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.

中文翻译：

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欠驱动机器人系统调查：生物启发、轨迹规划和控制

摘要 欠驱动机器人系统已成为一个重要的研究课题，旨在显着提高行为性能和能源效率。采用一些受生物启发的思想和特性，机器人系统的自组织和主要任务可以通过子系统的协调和与环境的动态交互来实现。相反，生物系统通过广泛的自体和外源顺应性相互作用实现节能和适应性行为。产生栩栩如生的运动的“技巧”是对仿生思想和特性的适当应用，以及在一般欠驱动系统中构建控制系统。在本文中，我们旨在通过介绍欠驱动机器人系统的系统调查工作来加强机器人和控制两个研究社区之间的联系，其中突出并讨论了生物启发、轨迹规划和控制方面的关键挑战和显着成功。本文的一个特别重点在于说明仿生特性、控制算法和先验知识在实现这些成功方面的作用，特别是它们如何有助于控制链接域的复杂性。我们展示了生物灵感和控制方法如何可以有利可图地应用，我们还注意到整个开放问题和未来研究的巨大潜力。强调并讨论了轨迹规划和控制。本文的一个特别重点在于说明仿生特性、控制算法和先验知识在实现这些成功方面的作用，特别是它们如何有助于控制链接域的复杂性。我们展示了生物灵感和控制方法如何可以有利可图地应用，我们还注意到整个开放问题和未来研究的巨大潜力。强调并讨论了轨迹规划和控制。本文的一个特别重点在于说明仿生特性、控制算法和先验知识在实现这些成功方面的作用，特别是它们如何有助于控制链接域的复杂性。我们展示了如何应用生物灵感和控制方法获利，我们还注意到整个开放问题和未来研究的巨大潜力。