Multi-Robot Dynamic Task Allocation for Exploration and Destruction

Environmental exploration is one of the common tasks in the robotic domain which is also known as foraging. In comparison with the typical foraging tasks, our work focuses on the Multi-Robot Task Allocation (MRTA) problem in the exploration and destruction domain, where a team of robots is required to cooperatively search for targets hidden in the environment and attempt to destroy them. As usual, robots have the prior knowledge about the suspicious locations they need to explore but they don't know the distribution of interested targets. So the destruction task is dynamically generated along with the execution of exploration task. Each robot has different strike ability and each target has uncertain anti-strike ability, which means either the robot or target is likely to be damaged in the destruction task according to that whose ability is higher. The above setting significantly increases the complexity of exploration and destruction problem. The auction-based approach, vacancy chain approach and a deep Q-learning approach based on strategy-level selection are employed in this paper to deal with this problem. A new simulation system based on Robot Operating System and Gazebo is specially built for this MRTA problem. Subsequently, extensive simulation results are provided to show that all proposed approaches are able to solve the MRTA problem in exploration and destruction domain. In addition, experimental results are further analyzed to show that each method has its own advantages and disadvantages.