Exploration and Coordination of Complementary Multi-Robot Teams In a Hunter and Gatherer Scenario

This paper c onsider s the problem of dynamic task allocation, where tasks are unknowingly distributed over an environment. We aim to address the multi - robot exploration aspect of the problem, while solving the task - allocation aspect. To that end, we first propose a novel nature - inspired approach called "hunter and gatherer". W e consider each task comprised of two sequential su btasks: detection and completion, where each subtask can only be carried out by a certain type of agent. Thus, this approach employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gatherers) the tasks. Then, we propose a multi - robot exploration algorithm for hunters and a multi - robot task allocation algorithm for gatherer s, both in distributed manner and based on innovative notions of "certainty and uncertainty profit margins". Statistical analysis on simulation results confirm the efficacy of the proposed algorithms. Besides, it is statistically prove n that the proposed s olutions function fairly, i.e. for each type of agent, the overall workload is distributed equally. I. Introduction Multi - robot systems are expected to complete tasks that are unfeasible, laborious or inefficient for a single agent to accomplish [1] . Employing multi - robot systems entails addressing various problems on the subject of task allocation [2], exploration [3], coordination [4], learning [5], and heterogeneity [6] . Among all these problems, the problem of multi - robot task allocation (MRTA), assign ing a group of tasks to individual robots, is the most deep - seated problems of multi - robot systems, where its complexity increases considerably by a wide variety of factors. Regarding, a MRTA problem where tasks are unknowingly distributed over an environment needs to be addressed by solving the problem from both MRTA and multi - ro bot exploration perspectives. This problem can even get more complicated if each task is divided into two sequential subtasks and each subtask can only be carried out by a certain type of agent.