The present manuscript explores the complex domain of distributed task allocation, with a specific  emphasis on optimizing the number of successfully completed tasks in multirobot systems. The inherent challenges arise due to the temporal limitations imposed by task deadlines and the restricted fuel capacities of robotic vehicles, often resulting in the unattainability of successfully completing all tasks. In this proposal, we present a groundbreaking paradigm that introduces the Effective and Efficient Performance Impact (EEPI) algorithm. This algorithm stands out due to its unique cost function and task release methodology, which contribute to its effectiveness and efficiency. The fundamental principles underlying the proposed cost function are centered on two pivotal tenets. The primary objective of this approach is to reduce the duration of travel required for each vehicle to reach their assigned task locations. This optimization is strategically designed to maximize the available time for task execution, taking into consideration the limited fuel resources at hand. Additionally, this approach guarantees the prompt commencement of each task in close proximity to its designated deadline, thereby facilitating the efficient prioritization of tasks with earlier deadlines over those with later deadlines. In order to address the challenges pertaining to the impacts on removal performance and inclusion performance, a strategy is proposed wherein tasks assigned to a vehicle are collectively released if said vehicle has achieved the highest task removal count during the task removal phase. The implementation of this strategic approach effectively enhances the overall quantity of tasks that are executed successfully. The effectiveness of the proposed Energy-Efficient Power Injection (EEPI) algorithm has been thoroughly validated through a combination of comprehensive simulations and real-world hardware-in-the-loop experiments. The effectiveness of the proposed distributed task allocation algorithm in maximizing task execution and its efficiency in reducing the number of iterations and convergence time have been substantiated through comparative analyses against state-of-the-art algorithms.

Index Terms- Task allocation, Distributed algorithm, hardware in loop experiment, multirobot system.