# Bio-inspired Intelligence with Applications to Robotics: A Survey

arXiv: https://arxiv.org/abs/2206.08544 · HTML: https://mlsanity.com/paper/2206.08544/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation](https://mlsanity.com/paper/2301.01827/) (2023)
- [Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges](https://mlsanity.com/paper/2310.14485/) (2023)
- [Intelligent Collective Escape of Swarm Robots Based on a Novel Fish-inspired Self-adaptive Approach with Neurodynamic Models](https://mlsanity.com/paper/2402.04228/) (2024)
- [A Novel Feature Learning-based Bio-inspired Neural Network for Real-time Collision-free Rescue of Multi-Robot Systems](https://mlsanity.com/paper/2403.08238/) (2024)
- [A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs](https://mlsanity.com/paper/2506.04238/) (2025)

## Abstract (arXiv)

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.