Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

Abstract

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprised of two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called "hunter and gatherer". The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of "certainty and uncertainty profit margins". The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively. It is also numerically shown that the proposed solutions function fairly, i.e. for each type of agent, the overall workload is distributed equally.Index Terms-Distributed multiagent system, dynamic task allocation, game theory, negotiation. I. INTRODUCTIONMultirobot systems are expected to undertake imperative roles in a wide variety of fields such as urban search and rescue (USAR) [1,2], agricultural field operations [3], security patrols [4,5], environmental monitoring [6], and industrial procedures [7]. Studies have shown that multi-robot systems have advantage over single-robot systems by offering more reliability, redundancy, and time efficiency when the nature of the tasks is inherently distributed [8]. Nonetheless, the problem of multi-robot task-allocation (MRTA) poses many critical challenges that has called for investigation in the past two decades [9][10][11]. In this regards, the complexity of MRTA problems increases significantly in a dynamic environment, where the number and location of tasks are unknown for agents [12,13]. Thus, robots need to explore the environment to find tasks before accomplishing them. In real world problems, any robot designated to perform one of the tasks in [1-7] needs to be sufficiently dexterous which makes it relatively heavy and incapable of agile exploration. Having said that, the dynamic problem can be turned into a problem where each task is comprised of sequential subtasks, each possible to be done only by a certain type of agent. In that case, for each type of subtask, a robot team of appropriate type must be employed. This case poses an unexplored MRTA problem whose coupling and cooperation between those complementary teams is the motivation of this work.In the context of MRTA, notable attention has been devoted for revealing ...

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

“…Besides, this work introduces a coordination factor designated for gatherers through which their behaviors range from completely indifferent to highly coordinated to hunters' locations in the environment. By the way of extensive simulations, we demonstrate that the effectiveness of the proposed algorithms is superior to the performance of the benchmark work [9]. Moreover, statistical analysis of the simulation results shows that the lack of an effective coordination between hunters and gatherers significantly hurts the total effectiveness of the planning.…”

“…Approach. In this section, we aim to compare the newly developed planning algorithms addressing the exploration and coordination aspects of the hunter-and-gatherer scenario with the benchmark hunter-and-gatherer mission planning introduced in [9]. To that end, we conduct the comparison in three environments containing different configurations of obstacles, as described in Figure 7.…”

“…Since in dynamic problems tasks are unknowingly distributed over an environment, the MRTA problem needs to be addressed from both task planning and multirobot exploration perspectives. e former has been addressed as the hunter-and-gatherer approach in our previous works [9,10] by dividing each task into two sequential subtasks, where each subtask can only be carried out by a certain type of agent.…”

“…For instance, it is theoretically challenging to employ an explorer agent with unique search algorithm in the central place foraging algorithm [32] since this exclusive search behavior interferes the swarm behavior of agents and disturbs the system's equilibrium due to decision-making dependency among agents. By way of contrast, solutions to the hunter-and-gatherer framework [9] provide a generic platform for dynamic multiagent task allocation that is more focused on individual autonomy with no decision-making dependency among agents.…”

A Dynamic Territorializing Approach for Multiagent Task Allocation

“…In this paper, we consider the workload balancing assignment issue in a nature-inspired problem proposed in [9,10], called "hunter and gatherer." More specifically, the dynamic MRTA problem is transfigured into a scenario where each task is composed of two sequential subtasks-exploration and completion.…”