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Li et al. Intell Robot 2021;1(1):58-83 Intelligence & Robotics
DOI: 10.20517/ir.2021.08

Review Open Access

Bio-inspired intelligence with applications to robotics:
a survey

Junfei Li, Zhe Xu, Danjie Zhu, Kevin Dong, Tao Yan, Zhu Zeng, Simon X. Yang

School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada.

Correspondence to: Prof. Simon X. Yang, Advanced Robotics & Intelligent Systems (ARIS) Laboratory, School of Engineering,
University of Guelph, Guelph, ON N1G 2W1, Canada. E-mail: syang@uoguelph.ca

How to cite this article: Li J, Xu Z, Zhu D, Dong K, Yan T, Zeng Z, Yang SX. Bio-inspired intelligence with applications to robotics: a
survey. Intell Robot 2021;1(1):58-83. http://dx.doi.org/10.20517/ir.2021.08
Received: 9 Sep 2021 First Decision: 20 Sep 2021 Revised: 26 Sep 2021 Accepted: 28 Sep 2021 Published: 12 Oct 2021

Academic Editor: Anmin Zhu Copy Editor: Xi-Jun Chen Production Editor: Xi-Jun Chen

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 charac-
terized 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 dis-
cussed in this paper.

Keywords: Biologically inspired algorithms, neurodynamics, path planning, mobile robots, cleaning robots, underwa-
ter robots, tracking control, formation control

© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar­
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
if changes were made.

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