In this paper, a novel deep reinforcement learning (DRL)-based method is proposed to navigate the robot team through unknown complex environments, where the geometric centroid of the robot team aims to reach the goal position while avoiding collisions and maintaining connectivity. Decentralized robot-level policies are derived using a mechanism of centralized learning and decentralized executing. The proposed method can derive end-to-end policies, which map raw lidar measurements into velocity control commands of robots without the necessity of constructing obstacle maps. Simulation and indoor real-world unmanned ground vehicles (UGVs) experimental results verify the effectiveness of the proposed method.
In this paper, control laws are designed to achieve desired flight formations for a group of unmanned (uninhabited) aerial vehicles (UAVs). It is proposed that the formation is led and managed by a leader UAV, which determines desired (for instance, safe and achievable) flight trajectories for a group of follower UAVs. Having the desired trajectories, control laws are designed to achieve flight formations according to one of the following scenarios: (1) Each UAV takes off toward its corresponding trajectory and locks on to it in finite time; the UAVs take off independently of each other and one at a time; (2) all UAVs take off simultaneously towards their corresponding trajectories and lock on to them at the same instance of time. Examples are presented to illustrate the efficacy of the designed control laws. This paper presents a method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The method is local and computes the optimal parameters for the formation within a neighborhood of the robots, allowing for reconfigurations, when required, by considering a set of target formations. The method consists of first computing the largest collision-free convex polytope in a neighborhood of the robots, followed by a constrained optimization via sequential convex programming where the optimal parameters for the formation are obtained. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performed well in simulations with a large team of quadrators and in experiments with two mobile manipulators carrying a rigid object.  An approach for coordination and control of 3D heterogeneous formations of unmanned aerial and ground vehicles under hawk-eye-like relative localization is presented in this paper. The core of the method lies in the use of visual top-view feedback from flying robots for the stabilization of the entire group in a leader-follower formation. We formulate a novel model predictive control-based methodology for guiding the formation. The method is employed to solve the trajectory planning and control of a virtual leader into a desired target region. In addition, the method is used for keeping the following vehicles in the desired shape of the group. The approach is designed to ensure direct visibility between aerial and ground vehicles, which is crucial for the formation stabilization using the hawk-eye-like approach. The presented system is verified in numerous experiments inspired by search-and-rescue applications, where the formation acts as a searching phalanx. In addition, stability and convergence analyses are provided to explicitly determine the limitations of the method in real-world applications. In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called spl alpha -lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or spl alpha -agents, and virtual agents associated with spl alpha -agents called spl beta - and spl gamma -agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms. Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real-world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity. Potential function approaches to robot navigation provide an elegant paradigm for expressing multiple constraints and goals in mobile robot navigation problems. As an example, a simple reactive navigation strategy can be generated by combining repulsion from obstacles with attraction to a goal. Advantages of this approach can also be extended to multirobot teams. In this paper we present a new class of potential functions for multiple robots that enables homogeneous large-scale robot teams to arrange themselves in geometric formations while navigating to a goal location through an obstacle field. The approach is inspired by the way molecules "snap" into place as they form crystals; the robots are drawn to particular "attachment sites" positioned with respect to other robots. We refer to these potential functions as "social potentials" because they are constructed with respect to other agents. Initial results, generated in simulation, illustrate the viability of the approach.