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.

Formation Transformation Based on Leader-Follower Algorithm

“…Thecoreofleader-followermethod (Zhang,Zhang&Liu,2008)forformationmaintenanceisthat oneorsomemembersareappointedastheleaderinaformationcomposedofapluralityofmotive objects,,andtherestisasretinueswhofollowthepositionandorientationoftheleaderatacertain range, thereby keeping various formations (Lin, Hwang & Wang, 2014). The structure of group formationiscomposedofparallelstructureandseriesstructureinthecontrolstructureofleaderfolloweralgorithm,asbelowinFigure1.…”

Minimum-Energy Leader-Following Formation of Distributed Multi-Agent Systems with Communication Constraints

“…Formation control of distributed multi-agent systems (DMASs) has shown an increased research interest in communities of control systems in recent years, which has a broad range of applications in various areas, such as multiple mobile robots [1,2], distributed sensor networks [3,4], unmanned aerial vehicles [5,6], and satellite groups [7−9]. Now, many approaches have been developed for formation control problems, such as virtual-structurebased strategy [10], behavior-based strategy [11,12], leader-follower-based strategy [13], and consensus-based strategy [14,15], and so on. In [16], it was proved that the former three strategies are the special cases of the consensus-based formation strategy.…”