Swarm Intelligence for Multi-Robot Coordination in Agricultural Automation

摘要

The research investigates the optimization of multi-robot coordination in agricultural automation through Particle Swarm Optimization. The methodology unfolds in four key aspects: firstly, by harnessing swarm intelligence principles, the approach enhances coordination among multiple robots in agricultural settings, fostering collaboration and dynamic task allocation in response to real-time changes. Secondly, the integration of Particle Swarm Optimization (PSO) introduces a powerful optimization technique, enabling the algorithm to dynamically adapt the swarm's configuration. This adaptability optimizes task allocation and individual robot positions, enhancing overall efficiency. Thirdly, the methodology systematically tackles scalability, adaptability, and robustness challenges in agricultural automation. By synergizing swarm intelligence and PSO, it ensures efficient scalability with an increasing number of robots and responsiveness to evolving agricultural demands. The holistic design makes it well-suited for practical implementation in real-world agricultural scenarios. A series of experiments varying PSO parameters were conducted, revealing nuanced relationships between iterations, swarm size, and coefficients and their impact on convergence time and fitness metrics. The outcomes demonstrate the trade-offs involved in selecting these parameters for efficient coordination in agricultural tasks. A comparative analysis with ABO and ACO highlights PSO's superior performance, achieving remarkable fitness value of 98% in the context of robotic swarm applications.