Optimizing Swarm Robotics for Collaborative Task Execution in Dynamic Environments
Keywords:
Swarm Robotics, Distributed Systems, Collaborative Task Execution, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)Abstract
The optimisation of swarm robotics systems—which are made up of several basic, autonomous robots that cooperate
without 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 adaptive
decision-making models like Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Reinforcement
Learning (RL), drawing on concepts from computer engineering and artificial intelligence. Swarms of 50–200 robots are
simulated in a variety of virtual environments as part of the technique to assess performance in a number of areas, such
as job completion time, energy efficiency, communication latency, scalability, and fault tolerance. The findings show
that 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 also
emphasises the significance of decentralised communication, energy-aware scheduling, and hardware-software co-design
in attaining scalable and resilient swarm behaviour. The study intends to close the gap between simulation and real-world
implementation 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 like
China's 10,000-drone synchronised light show.
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