Abstract
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.
Highlights
From the first stirrings of life, nature has been providing a suitable breeding ground for the intelligence of organisms
This paper focuses a comprehensive survey of the state-of-the-art research on bio-inspired neurodynamics models with their applications to path planning and control of autonomous robots
The bio-inspired neurodynamics trajectory tracking control for a single nonholonomic mobile robot can be extended to the formation control for multiple nonholonomic mobile robots, in which the follower can track its real-time leader by the proposed kinematic controller
Summary
From the first stirrings of life, nature has been providing a suitable breeding ground for the intelligence of organisms. Bio-inspired neurodynamics models have been studied for real-time path planning and control of various robotic systems during the past decades[2]. Based on the shunting neurodynamics model and its model variants, several new algorithms have been successfully developed for real-time path planning and control of various autonomous robots[13,14]. This paper focuses a comprehensive survey of the state-of-the-art research on bio-inspired neurodynamics models with their applications to path planning and control of autonomous robots. The overall studies give an insight into neurodynamics models on autonomous robot applications, which could inspire potential ideas for future developments of novel intelligent bio-inspired path planning and control for diversified autonomous robotic systems.
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