Energy Efficiency Optimization in Swarm Robotics for Smart Photovoltaic Monitoring

Abstract

Photovoltaic park (PV) and power generator monitoring is a crucial activity that calls for effective coverage path planning. Artificial intelligence and particularly swarm robotics have brought new methods to tasks such as coverage path planning by having multiple robots work together to cover a specific area. Nonetheless, enhancing energy efficiency in these systems continues to be a crucial obstacle, particularly with the growing focus on sustainability. This research investigates techniques to enhance energy efficiency in swarm robotics, focusing on coverage path planning assignments. The proposed approach merges advanced swarm robotics algorithms with energy-efficient methods to reduce power consumption while still ensuring effective coverage. Thorough simulations in simulated environments of Western Macedonia assess the efficiency of the proposed approach. Even though the proposed approach has a longer convergence time compared to a generic ACO approach, the findings of the simulations indicate that the MOACO approach has substantial enhancements up to 22% in path travel time, in terms of solution quality and energy consumption metrics. The findings of the present work offer valuable insights into the design of sustainable robotic systems and underscore the potential of swarm robotics in achieving efficient coverage path planning. This study adds to the overall objective of creating eco-friendly technologies in robotics, leading to upcoming advancements in the industry.