Biologically Inspired Navigation Models for Autonomous Swarms

Abstract

Biologically inspired navigation models have emerged as a powerful paradigm for guiding autonomous robotic swarms in complex, dynamic environments. By emulating collective behaviors observed in natural systems—such as flocking birds, schooling fish, and foraging insects—these models enable large groups of simple agents to achieve robust, scalable navigation without centralized control. In this study, we present a unified framework that integrates four canonical bio-inspired strategies—Boids flocking rules, ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC)—into a comparative simulation environment. Furthermore, we propose a novel hybrid model combining local interaction rules with stigmergic pheromone signaling to leverage the complementary strengths of these approaches. Our simulation platform, built on ROS and Gazebo, facilitates controlled experiments across a range of obstacle densities, communication constraints, and swarm sizes. We perform rigorous statistical analyses (one-way ANOVA and Tukey’s HSD) to evaluate path efficiency, collision rates, and energy consumption over 30 trials per model. The hybrid model consistently outperforms individual strategies, achieving up to 14% improvement in path efficiency, a 44% reduction in collisions, and 15% lower energy usage. Detailed simulation studies further reveal the hybrid model’s resilience under high obstacle density and communication loss, as well as near-linear scalability up to 100 agents. These findings demonstrate that integrating local rule-based navigation with stigmergic communication yields a versatile, fault-tolerant solution for real-world swarm applications. Future work will validate these insights on physical robot platforms and explore adaptive parameter tuning via machine learning.