A Multi-Robot Allocation Scheme for Coverage Control Applications with Multiple Areas of Interest

Coverage control with a multi-robot system requires the robot team to be optimally distributed across some environment. This environment may have multiple, distinct areas of interest, each of which associated with a specific task that requires robots with particular capabilities. In such scenarios, the first problem to be solved is that of assigning each robot in the team to one of the importance areas, while accounting for the robots' capabilities and the task/area requirements. This paper investigates this problem and proposes a novel multi-robot task allocation (MRTA) scheme in the context of coverage control. More specifically, the proposed method employs integer linear programming (ILP) to solve the constrained robot-to-task allocation problem. The resulting algorithm allocates each robot in the heterogeneous team to one of the importance areas, subject to a number of realistic constraints such as the tasks' requirements, and the robots' capabilities and energy levels. Our approach introduces elements of fault tolerance and robustness to situational changes, since it can also be executed periodically during the coverage process, to reassign the robots in case of robot faults, changes in the tasks/areas, and other factors that alter the allocation solution. The proposed scheme also has the advantage of being separate from the coverage control algorithm, and therefore supports a modular framework. Finally, a set of realistic simulated scenarios are used to validate the task allocation scheme being proposed.