Hedonic Coalition Formation for Task Allocation with Heterogeneous Robots

Abstract

Tasks in the real world are complex in nature and often require multiple robots to collaborate in order to be accomplished. However, multiple robots with the same set of sensors working together might not be the optimal solution as one task might require different sensory inputs and actuation outputs. On the other hand, putting all types of sensors and/or actuators on a single robot is not a cost-effective solution. Therefore, multiple robots with different capabilities need to coordinate and form teams in order to accomplish such tasks. In this paper, we study the coalition formation problem for task allocation with multiple heterogeneous (equipped with different sets of sensors) robots. We use a hedonic coalition formation framework, rooted in game theory, to solve the mentioned problem. Our proposed algorithm aims to minimize the total cost of the formed coalitions and to maximize the matching between the required and the allocated types of robots to the tasks. Simulation results show that it produces near-optimal solutions in a negligible amount of time (0.19 ms. with 100 robots and 10 tasks).