Bio-inspired Intelligence with Applications to Robotics: A Survey
Junfei Li, Zhe Xu, Danjie Zhu, Kevin Dong, Tao Yan, Zhu Zeng, Simon X., Yang

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…
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