This paper presents a solution for the problem of minimum time coverage of ground areas using a group of unmanned air vehicles (UAVs) equipped with image sensors. The solution is divided into two parts: (i) the task modeling as a graph whose vertices are geographic coordinates determined in such a way that a single UAV would cover the area in minimum time; and (ii) the solution of a mixed integer linear programming problem, formulated according to the graph variables defined in the first part, to route the team of UAVs over the area. The main contribution of the proposed methodology, when compared with the traditional vehicle routing problem’s (VRP) solutions, is the fact that our method solves some practical problems only encountered during the execution of the task with actual UAVs. In this line, one of the main contributions of the paper is that the number of UAVs used to cover the area is automatically selected by solving the optimization problem. The number of UAVs is influenced by the vehicles’ maximum flight time and by the setup time, which is the time needed to prepare and launch a UAV. To illustrate the methodology, the paper presents experimental results obtained with two hand-launched, fixed-wing UAVs.

We address the problem of covering an environment with robots equipped with sensors. The robots are heterogeneous in that the sensor footprints are different. Our work uses the location optimization framework in with three significant extensions. First, we consider robots with different sensor footprints, allowing, for example, aerial and ground vehicles to collaborate. We allow for finite size robots which enables implementation on real robotic systems. Lastly, we extend the previous work allowing for deployment in non convex environments.

This paper presents a methodology for computation of artificial vector fields that allows a robot to converge to and circulate around generic curves specified in $n$ -dimensional spaces. These vector fields may be directly applied to solve several robot-navigation problems such as border monitoring, surveillance, target tracking, and multirobot pattern generation, with special application to fixed-wing aerial robots, which must keep a positive forward velocity and cannot converge to a single point. Unlike previous solutions found in the literature, the approach is based on fully continuous vector fields and is generalized to time-varying curves defined in $n$-dimensional spaces. We provide mathematical proofs and present simulation and experimental results that illustrate the applicability of the proposed approach. We also present a methodology for construction of the target curve based on a given set of its samples.

In this paper we address the problem of transporting objects with multiple mobile robots using the concept of “object closure”. In contrast to other manipulation techniques that are typically derived from form or force closure constraints, object closure requires the less stringent condition that the object be trapped or caged by the robots. Our basic goal in this paper is to develop decentralized control policies for a group of robots to move toward a goal position while maintaining a condition of object closure. We present experimental results that show polygonal mobile robots controlled using visual feedback, transporting a convex polygonal object in an obstacle free environment toward a prescribed goal.

The focus of this study is on the design of feedback control laws for swarms of robots that are based on models from fluid dynamics. We apply an incompressible fluid model to solve a pattern generation task. Possible applications of an efficient solution to this task are surveillance and the cordoning off of hazardous areas. More specifically, we use the smoothed-particle hydrodynamics (SPH) technique to devise decentralized controllers that force the robots to behave in a similar manner to fluid particles. Our approach deals with static and dynamic obstacles. Considerations such as finite size and nonholonomic constraints are also addressed. In the absence of obstacles, we prove the stability and convergence of controllers that are based on the SPH method. Computer simulations and actual robot experiments are shown to validate the proposed approach.

The engineering design of a hand-launched, small unmanned aerial vehicle (SUAV), including guidance strategy and control design, together with real data from practical flight tests, is presented. The main goal in this work is the implementation of a low cost, portable, and reliable aerial platform for ground reconnaissance. The vehicle was specially designed so that the number of necessary sensors and actuators was reduced, without precluding the feasibility of the assigned mission.