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

TL;DR

This paper introduces a novel multi-robot exploration and coordination framework inspired by hunter-gatherer strategies, optimizing task allocation and collaboration between exploration and task completion teams in complex environments.

Contribution

It proposes new algorithms for multi-robot exploration based on expected information gain and introduces a coordination method using profit margins, validated through extensive simulations.

Findings

Proposed algorithms outperform existing methods in obstacle-rich environments.

Effective hunter-gatherer coordination improves overall task efficiency.

Workload distribution among agents is balanced and unbiased.

Abstract

The hunter and gatherer approach copes with the problem of dynamic multi-robot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multi-robot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multi-robot exploration algorithm for hunters based on innovative notions of "expected information gain" to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacles complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly hurts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics.