Leaderless Swarm Formation Control: From Global Specifications to Local Control Laws

This paper introduces a distributed leaderless swarm formation control framework to address the problem of collectively driving a swarm of robots to track a time-varying formation. The swarm's formation is captured by the trajectory of an abstract shape that circumscribes the convex hull of robots' positions and is independent of the number of robots and their ordering in the swarm. For each robot in the swarm, given global specifications in terms of the trajectory of the abstract shape parameters, the proposed framework synthesizes a control law that steers the swarm to track the desired formation using the information available at the robot's local neighbors. For this purpose, we generate a suitable local reference trajectory that the robot controller tracks by solving the input-output linearization problem. Here, we select the swarm output to be the parameters of the abstract shape. For this purpose, we design a dynamic average consensus estimator to estimate the abstract shape parameters. The abstract shape parameters are used as the swarm state feedback to generate a suitable robot trajectory. We demonstrate the effectiveness and robustness of the proposed control framework by providing the simulation of coordinated collective navigation of a group of car-like robots in the presence of robots and communication link failures

Interior Point Algorithm for Multi-UAVs Formation Autonomous Reconfiguration

Here the problem of designing multi-UAVs formation autonomous reconfiguration is considered. Combined with three kinds of cost functions, nonlinear dynamic equations, and four inequality constraints, one nonlinear multiobjective optimization problem is constructed. After applying weighted sum method and separating all equality or inequality constraints, the former nonlinear multiobjective optimization problem can be converted into a standard nonlinear single objective optimization problem. Then the interior point algorithm is applied to solve it. Further some improvements are proposed to avoid rank deficiency of some matrices. The equivalence property between multiobjective optimization and single objective optimization through weighted sum method is proved. Finally the efficiency of the proposed strategy can be confirmed by the simulation example results

HIERARCHICAL HYBRID SYMBOLIC ROBOT MOTION PLANNING AND CONTROL

ABSTRACT This paper addresses the motion planning problem using hybrid symbolic techniques. The proposed approach develops a unified hierarchical hybrid control framework using a bismulation-based abstraction technique over the partitioned motion space that can be applied to autonomous aerial robots (3-D symbolic motion planning) or ground vehicles (2-D symbolic motion planning). The bisimulation relation between the abstracted model and the original continuous system guarantees that their behaviors are the same. This allows to design a discrete supervisor for the abstracted model, and then, the designed supervisor can be applied to the original system while the closed-loop behavior does not change. To apply the discrete supervisor to the original continuous system, an interface layer is developed, which on the one hand translates discrete commands of the supervisor to a continuous form applicable to the continuous plant and on the other hand, abstracts the continuous signals of the continuous low layer to discrete symbols understandable by the supervisor. The proposed algorithm is verified through implementation of a hybrid symbolic algorithm for the formation control of unmanned aerial vehicles

Hybrid Formation Control of Unmanned Helicopters

Ph.DDOCTOR OF PHILOSOPH

Hybrid three-dimensional formation control for unmanned helicopters

10.1016/j.automatica.2012.10.008Automatica492424-433ATCA