Centralized task allocation for multiple quadrupeds

Abstract

The thesis focuses on analyzing different centralized task allocation methods for multiple quadruped systems. The goal is to assign tasks to agents 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) m/s using the results from the literature. The methods are analyzed and simulated using MATLAB and its Global Optimization toolbox. Moreover, A* path planning algorithm is used to decide paths that agents take. Three different task assignment problems are explored where there are fewer tasks than agents, task and agent count are equal, and there are more tasks than agents. The resulting task assignments and metrics are analyzed. A greedy algorithm that aims to assign tasks according to the shortest distances between the agent quadrupeds is proposed and analyzed. However, since this algorithm does not consider power consumption and task completion ratio, it underperforms in some cases. The Genetic Algorithm and Particle Swarm Optimization methods are used by utilizing custom optimization (fitness) function. The optimization function is a geometric combination of total energy consumption, mission completion time, and task completion ratio. As a result, the resulting task assignments generally consume more power while reducing mission completion time tremendously. The growing trend of automation in various fields, leading to less error, more accurate results, and time-saving. The thesis also explains the concept of task allocation for autonomous systems and the different methods used for it. The methods can be decentralized or centralized, depending on whether there is a predefined decision-maker or not. In conclusion, the thesis provides valuable insights into the centralized task allocation process for multiple quadruped systems. The different methods and algorithms analyzed 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 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.