Energy-Aware Multi-Robot Task Scheduling using Meta-Heuristic Optimization methods for Ambiently-Powered Robot Swarms  

This paper presents a novel approach to address the challenges of energy-aware task scheduling in a collaborative swarm of robots equipped with energy-harvesting capabilities. With a primary focus on task execution timing, reliable task allocation, and efficient utilization of available energy resources, the task-scheduling process is approached with an energy-aware strategy. The developed architecture employs a centralized and autonomous approach that dynamically responds to a sequence-dependent setup time job shop scheduling demand. The proposed method incorporates energy consumption estimation, charging contingency approach, and energy harvesting prediction to minimize overall task execution time. The problem is optimized using Adaptive Particle Swarm Optimization and compared to other well-known meta-heuristic algorithms. A practical illustration of the proposed approach's real-world utility is demonstrated through a case study scenario conducted within a heterogeneous pick-drop delivery setting inside a warehouse.
The study was conducted utilizing the TurtelBot3 burger robot model within the Robotic Operating System and Gazebo simulation environment. Simulation results demonstrate the superiority of the energy-aware solution for multi-robot scheduling and task allocation problems over the energy-unaware methods by a 15 percent reduction in task completion time.