Bio-inspired Intelligence with Applications to Robotics: A Survey

TL;DR

This survey reviews bio-inspired neural network models and their applications in robotic path planning and control, emphasizing neurodynamics approaches for real-time navigation without prior learning or environment knowledge.

Contribution

It provides a comprehensive overview of neurodynamics models like the shunting model and their application to real-time robotic control and navigation.

Findings

Neurodynamics models enable collision-free navigation without prior learning.

Bio-inspired controllers improve speed stability during large initial errors.

Neural network frameworks facilitate real-time control of various robotic systems.

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.