A Simple Scheme for Formation Control Based on Weighted Behavior Learning

Abstract

Several correlated issues of autonomy and simplicity regarding formation control for robots with a self-awareness mechanism in unstructured environments are considered. To achieve autonomy and simplicity, a hybrid scheme is derived for robot maneuvering based on a multibehavioral system. The system holds some self-awareness capabilities ensuring precision and robustness in the presence of internal and external disturbances within the limited capacity of interrobot communication. This is to ensure that the robots can march and simultaneously maintain their assigned formation and avoid hazardous collisions on the way to their destination. These self-awareness capabilities are achieved through a layered reinforcement learning algorithm. At the bottom level, robots are equipped with a set of primitive behaviors learned prior to team formation. The high-level combined behavior is generated by multiplying the outputs of each primitive behavior by its weight, and then summing and normalizing the results. The weights keep adaptively adjusting to the proposed reinforcement learning method. Once a robot receives a command providing the formation shape and the location of the destination, the robot approaches the destination autonomously and keeps an appropriate distance from its neighbors to maintain the assigned pattern. Leadership was given to a robot occupying the lead position. The volunteer leader takes the responsibility for keeping the formation, reporting its existence to the other robots, and resetting the position assignment after passing obstacles. Simulations show the practicality and performance of the proposed approach in both static and dynamic obstacle environments.