Abstract
In this paper, we propose a dynamic territorializing approach for the problem of distributing tasks among a group of robots. We consider the scenario in which a task comprises two subtasks—detection and completion; two complementary teams of agents, hunters and gatherers, are assigned for the subtasks. Hunters are assigned with the task of exploring the environment, i.e., detection, whereas gatherers are assigned with the latter subtask. To minimize the workload among the gatherers, the proposed algorithm utilizes the center of mass of the known targets to form territories among the gatherers. The concept of center of mass has been adopted because it simplifies the task of territorial optimization and allows the system to dynamically adapt to changes in the environment by adjusting the assigned partitions as more targets are discovered. In addition, we present a game-theoretic analysis to justify the agents’ reasoning mechanism to stay within their territory while completing the tasks. Moreover, simulation results are presented to analyze the performance of the proposed algorithm. First, we investigate how the performance of the proposed algorithm varies as the frequency of territorializing is varied. Then, we examine how the density of the tasks affects the performance of the algorithm. Finally, the effectiveness of the proposed algorithm is verified by comparing its performance against an alternative approach.
Highlights
Fair and efficient distribution of tasks in an area-coverage problem among the agents in a multirobot system is a common objective and has been widely considered in the literature
We focus on the relevant related work in the literature. We have structured it into two different sections. e first section presents several studies that have focused on the problem of task allocation as a primitive, global, and prominent problem in the context of multirobot task allocation (MRTA). e subsequent section highlights some of the research studies carried out towards the work-balancing assignment problem in MRTA
We investigate the effect of varying the number of targets in the field F. en, we compare the proposed algorithm with an alternative approach, where F has no territories for the gatherers. e comparison is quantized in terms of measure of the mission accomplishment time and the effect on workload W(t). e simulation platform has been developed in MATLAB
Summary
Fair and efficient distribution of tasks in an area-coverage problem among the agents in a multirobot system is a common objective and has been widely considered in the literature. In terms of a real-world setting for the hunter and gatherer approach, we can consider the USAR in a disaster site where several victims have been stranded in unknown locations and need immediate rescue operations For such an operation, the hunters could be a group of lightweight unmanned aerial vehicles (UAVs) as they would offer agility and provide faster exploration and their mission would be to search the site and locate the victims. We encourage the readers to review [16, 17], which considers practical implementation of similar operations and offers insight into solutions related to exploration, localization, and mapping of UGVs and UAVs. Balancing the workload amongst the agents in this work is based on environment partitioning through locational optimization.
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