This paper proposes a decentralized approach for solving the problem of moving a swarm of agents into a desired formation. We propose a decentralized assignment algorithm which prescribes goals to each agent using only local information. The assignment results are then used to generate energy-optimal trajectories for each agent which have guaranteed collision avoidance through safety constraints. We present the conditions for optimality and discuss the robustness of the solution. The efficacy of the proposed approach is validated through a numerical case study to characterize the framework's performance on a set of dynamic goals.
 In this paper, we study the distributed optimal flocking control problem of multiple Unmanned Aircraft Systems (Multi-UAS). Using the emerging Neuro Dynamic Programming (NDP) technique, a novel distributed near optimal flocking design is proposed for ensuring the multi-UAS to follow the three heuristic flocking rules (i.e. cohesion, separation and alignment) in an optimal manner. First, an innovative cost function is developed by incorporating system cohesion, separation and alignment performance together. Subsequently, a novel neural network (NN) is proposed to approximate the minimized cost function value by using Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. Moreover, the near optimal flocking can be attained by minimizing estimated cost function. Using standard Lyapunov stability analysis, the uniformly ultimately boundedness (UUB) of the closed-loop multi-UAS is verified. Simulation results demonstrate the effectiveness of proposed scheme. In this paper, we investigate the formation control of multiple unmanned aerial vehicles (UAVs), specifically unmanned aircraft, in an obstacle-laden environment. The main contribution of this paper is to integrate the formation control, trajectory tracking, and obstacle collision avoidance into one unified optimal control framework. A nonquadratic avoidance cost is innovatively constructed via an inverse optimal control approach, which leads to an analytical, distributed, and optimal formation control law. The stability and optimality of the closed-loop system are proven. In addition, the proposed optimal control law is dependent only on the information from the local neighbors, rather than all UAVs' information. Simulation of multiple UAVs' formation flying demonstrates the effectiveness of the integrated optimal control design with desired behaviors including formation flying, trajectory tracking, and obstacle collision avoidance. This paper presents a computationally tractable, resolution-complete algorithm for generating dynamically feasible trajectories for N interchangeable (identical) aerial robots navigating through cluttered known environments to M goal states. This is achieved by assigning the robots to goal states while concurrently planning the trajectories for all robots. The algorithm minimizes the maximum cost over all robot trajectories. The computational complexity of this algorithm is shown to be cubic in the number of robots, substantially better than the expected exponential complexity associated with planning in the joint state space and the assignment of goals to robots. This algorithm can be used to plan motions and goals for tens of aerial robots, each in a 12-dimensional state space. Finally, experimental trials are conducted with a team of six quadrotor robots navigating in a constrained three-dimensional environment. We address the optimal dynamic formation problem in mobile leader-follower networks where an optimal formation is generated to maximize a given objective function while continuously preserving connectivity. We show that in a convex mission space, the connectivity constraints can be satisfied by any feasible solution to a mixed integer nonlinear optimization problem. When the optimal formation objective is to maximize coverage in a mission space cluttered with obstacles, we separate the process into intervals with no obstacles detected and intervals where one or more obstacles are detected. In the latter case, we propose a minimum-effort reconfiguration approach for the formation which still optimizes the objective function while avoiding the obstacles and ensuring connectivity. We include simulation results illustrating this dynamic formation process.