Optimizing Swarm Robotics for Collaborative Task Execution in Dynamic Environments (Comprehensive Journal of Science, 2025)
The optimisation of swarm robotics systems—which are made up of several basic, autonomous robots that cooperatewithout centralised control—for carrying out cooperative tasks in intricate, dynamic settings is examined in this work.The research focusses on the integration of distributed algorithms, real-time communication protocols, and adaptivedecision-making models like Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and ReinforcementLearning (RL), drawing on concepts from computer engineering and artificial intelligence. Swarms of 50–200 robots aresimulated in a variety of virtual environments as part of the technique to assess performance in a number of areas, suchas job completion time, energy efficiency, communication latency, scalability, and fault tolerance. The findings showthat RL-based swarms perform better than conventional models in terms of efficiency, robustness, and flexibility,especially when faced with changing objectives, reconfigured obstacles, or partial system breakdown. The paper alsoemphasises the significance of decentralised communication, energy-aware scheduling, and hardware-software co-designin attaining scalable and resilient swarm behaviour. The study intends to close the gap between simulation and real-worldimplementation by providing a fundamental framework for utilising swarm robotics in domains like autonomous delivery,environmental monitoring, smart agriculture, and emergency response. It is motivated by real-world demonstrations likeChina's 10,000-drone synchronised light show.
Keywords: Swarm Robotics; Distributed Systems; Collaborative Task Execution; Particle Swarm Optimization (PSO); Ant Colony Optimization (ACO)