In the past decades, considerable attention has been paid to bio-inspired intelligence and its applications to robotics. This paper provides a comprehensive survey of bio-inspired intelligence, with a focus on neurodynamics approaches, to various robotic applications, particularly to path planning and control of autonomous robotic systems. Firstly, the bio-inspired shunting model and its variants (additive model and gated dipole model) are introduced, and their main characteristics are given in detail. Then, two main neurodynamics applications to real-time path planning and control of various robotic systems are reviewed. A bio-inspired neural network framework, in which neurons are characterized by the neurodynamics models, is discussed for mobile robots, cleaning robots, and underwater robots. The bio-inspired neural network has been widely used in real-time collision-free navigation and cooperation without any learning procedures, global cost functions, and prior knowledge of the dynamic environment. In addition, bio-inspired backstepping controllers for various robotic systems, which are able to eliminate the speed jump when a large initial tracking error occurs, are further discussed. Finally, the current challenges and future research directions are discussed in this paper.
翻译:在过去几十年里,人们相当重视生物激励型情报及其对机器人的应用,本文件对生物激励型情报及其应用于各种机器人应用,特别是自主机器人系统的路径规划和控制,进行了全面的调查,重点是神经动力学方法,对生物激励型情报进行了全面调查,对各种机器人应用,特别是各种自主机器人系统的路径规划和控制,首先,引进了生物激励型捕猎模型及其变体(添加模型和门底极模型),并详细介绍了其主要特点。随后,对两种主要神经动力学应用应用于各种机器人系统的实时路径规划和控制进行了审查。一个以神经动力模型为特征的生物激励型神经网络框架,为移动机器人、清洁机器人和水下机器人讨论了神经元的特征。最后,生物激励型神经网络被广泛用于实时无碰撞导航与合作,没有学习程序、全球成本功能和对动态环境的先前知识。此外,还进一步讨论了各种机器人系统的生物激励型后退控制器,这些系统能够在发生重大初始跟踪错误时消除速度跳动。最后,本文讨论了当前的挑战和未来的研究方向。