Intelligent Control of Swarm Robotics Employing Biomimetic Deep Learning

Abstract

The collective motion of biological species has robust and flexible characteristics. Since the individual of the biological group interacts with other neighbors asymmetrically, which means the pairwise interaction presents asymmetrical characteristics during the collective motion, building the model of the pairwise interaction of the individual is still full of challenges. Based on deep learning (DL) technology, experimental data of the collective motion on Hemigrammus rhodostomus fish are analyzed to build an individual interaction model with multi-parameter input. First, a Deep Neural Network (DNN) structure for pairwise interaction is designed. Then, the interaction model is obtained by means of DNN proper training. We propose a novel key neighbor selection strategy, which is called the Largest Visual Pressure Selection (LVPS) method, to deal with multi-neighbor interaction. Based on the information of the key neighbor identified by LVPS, the individual uses the properly trained DNN model for the pairwise interaction. Compared with other key neighbor selection strategies, the statistical properties of the collective motion simulated by our proposed DNN model are more consistent with those of fish experiments. The simulation shows that our proposed method can extend to large-scale group collective motion for aggregation control. Thereby, the individual can take advantage of quite limited local information to collaboratively achieve large-scale collective motion. Finally, we demonstrate swarm robotics collective motion in an experimental platform. The proposed control method is simple to use, applicable for different scales, and fast for calculation. Thus, it has broad application prospects in the fields of multi-robotics control, intelligent transportation systems, saturated cluster attacks, and multi-agent logistics, among other fields.

AI Driven Approaches in Swarm Robotics - A review

“…In 2021, a study [28] In the paper [29], the authors used a 6-PPSS redundant mobile platform and included an Extended Kalman Filter routine. They also applied a variant of the crawling probabilistic road map motion planning algorithm.…”

“…This concise overview facilitates a comprehensive understanding of the diverse applications of AI in swarm robotics, alongside the testing environments and specific methodologies employed across the studies. -√ Large language model (LLM) [21] -√ RL algorithm [5] √ -Dueling Double Deep Q-Network (D3QN) [6] √ -Deep Learning Trained by Genetic Algorithm (DL-GA) [8] √ -3D StringNet herding [10] √ -Decision-making mechanisms [12] √ -Deep Imitation Reinforcement Learning (DIRL) [17] Augmented Lagrangian particle swarm optimization (ALPSO) [20] √ √ Automatic modular design approach (AutoMoDe) [24] Coordination -√ AudioLocNetv(deep learning module) [31] √ -Not specified [32] √ -End-to-end Neural Networks to train robots [27] √ -Mean-field feedback control [28] √ -Deep Neural Network (DNN) model [29] √ -variant of the crawling probabilistic road map motion planning algorithm [33] √ -distributed online reinforcement learning method [34] √ -coordination algorithm [51] Optimization -√ PSO algorithm [53] -√ streamlined algorithms [36] √ -Genetic algorithm (GA) [46] √ -Particle Swarm Optimization (PSO) [49] √ -Robot Bean Optimization Algorithm (RBOA) [50] √ -Automatic modular design method: AutoMoDe-Cedrata and AutoMoDe-Maple [52] √ -PPO algorithm [54] √ -Dijkstra algorithm [55] √ -WC and WET algorithms [44] √ √ Decentralized ergodic planning [35] Optimization and Navigation √ -YOLOv8 [41] √ -Quantum-based path-planning algorithm and Grover's search algorithm [42] √ -Genetic algorithms (GA) and Cellular automata techniques [9] √ -Mean-Field Control (MFC), deep reinforcement learning (RL), and collision avoidance algorithms [22] Optimization and Coordination √ -Knowledge-Based Neural Ordinary Differential Equations (KNODE) [23] √ -Surrogate models ...…”

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

“…With the advancement of artificial intelligence, intelligent bionics algorithms have emerged as the principal path planning algorithms [6][7][8] . The graph search algorithm uses multi-source sensor information to search feasible path options at high frequency and finally calculates the optimal path from the starting point to the destination through an iterative process.…”