Dynamic Task Allocation with Closed-Loop Adjusting in Swarm Robotic Search for Multiple Targets

To search for multiple targets in parallel and simultaneously with swarm robots, those robots should be divided into some sub-swarm-coalitions by job allocation, so that each sub-swarm can cooperatively work focusing on its desired target. Due to varying of search subjects and search objects in unstructured environment, dynamic task allocation is required. Thus a dynamic allocation method with closed-loop adjusting based on the existing response threshold allocation model is proposed. First, a personalized task set is constructed including some meta tasks for each robot, according to cognition receiving by means of either detected signals (I-type target, direct) or informed information of target through neighborhood communications (Ⅱ-type target, indirect). Then responses to target stimuli are evaluated based on probability principle, and a desired target is selected independently from task set. Those robots having the same desired target confederate independently with each other to work cooperatively. Then, the level of robot resource configuration is measured with an average distance approach and the result is linked into allocation model as a negative feedback. This mechanism can makes dynamic migration occurred among different sub-swarm-coalitions on demand. Results from simulations reveal that overall search efficiency is improved by using our proposed method, comparing with the existing methods.