Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team
This addresses the challenge of multi-robot task allocation in hazardous environments, but it appears incremental as it builds on existing optimization methods for coordination.
The paper tackles the coordination problem of multiple robots performing exploration and cooperative disarming of hazardous targets in an unknown area, presenting a nature-inspired approach and mathematical model to balance these conflicting tasks, with results simulated in a 2D terrain under static and dynamic conditions.
This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results.