Comparison of Centralized Task Allocation Methods with A* Path Planning for Multi-Quadruped Robot

Abstract

This work focuses on analyzing different centralized task allocation methods for multiple quadruped systems. The goal is to assign tasks to agents by considering obstacles in the area in a way that minimizes power consumption, completes the mission in the shortest possible time, and maximizes task completion ratio. The power consumption and cost-of-transmission for cheetah-type quadruped are analyzed, and the power consumption is extrapolated for speeds between (0.1,0.8) m/s using the results from the literature. A* path planning algorithm is utilized to consider obstacles in the area. Particle swarm optimization and genetic algorithm analyzed to show that a combination of power consumption, mission completion time, and task completion ratio can result in a more efficient and effective task allocation process compared to shortest greedy distance-based allocations. The findings can contribute to the development of more advanced and autonomous systems in various fields, leading to increased productivity, accuracy, and efficiency.