Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models

Abstract

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

“…Sani et al [ 10 ] considered a pursuit–evasion game based on the nonlinear model predictive control in static obstacle environments, where the pursuer and the evader are nonholonomic mobile robots. The pursuit–evasion problem has a wide range of applications, including navigation [ 11 ], surveillance [ 12 , 13 ], human–robot cooperation [ 14 ] and robotic foraging behaviors [ 15 ].…”