Luciano C. A. Pimenta

Sensing and coverage for a network of heterogeneous robots

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.

Simultaneous Coverage and Tracking (SCAT) of Moving Targets with Robot Networks

We address the problem of simultaneously covering an environment and tracking intruders (SCAT). The problem is translated to the task of covering environments with time-varying density functions under the locational optimization framework. This allows for coupling the basic subtasks: task assignment, coverage, and tracking. A decentralized controller with guaranteed exponential convergence is devised. The SCAT algorithm is verified in simulations and on a team of robots.

Swarm Coordination Based on Smoothed Particle Hydrodynamics Technique

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.

Multi-UAV Routing for Area Coverage and Remote Sensing with Minimum Time.

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.

Fluids in Electrostatic Fields: An Analogy for Multirobot Control

This paper addresses the problem of controlling a large group of robots in a 2-D pattern generation task. Different from previous methodologies, our approach can be used in generic static environments, where obstacles may appear. This approach is based on the analogy with the simulation of fluids in electrostatic fields. By means of a weak coupling between the smoothed particle hydrodynamics and the finite element method we derive a scalable solution where decentralized controllers are provided

Robot navigation based on electrostatic field computation

This paper addresses the problem of mobile robot navigation using artificial potential fields. Many potential field based methodologies are found in the robotics literature, but most of them have problems with spurious local minima, which cause the robot to stop before reaching its target position. Although some free of local minima methodologies are found in the literature, none of them are easy to implement and generalize for complex shaped environments and robots. We propose a perfect analogy between electrostatic field computation and robot path planning. Thus, an easy solution to the problem, which is based on standard finite-element methods, can be applied with generic geometries and can even take into account the robots orientation. To demonstrate the elegance of the proposed methodology, several experimental results with actual mobile robots are included

Robot Navigation in Multi-terrain Outdoor Environments

This paper presents a methodology for motion planning in outdoor environments that takes into account specific characteristics of the terrain. Instead of decomposing the robot configuration space into “free” and “occupied”, we consider the existence of several regions with different navigation costs. In this paper, costs are determined experimentally by navigating the robot through the regions and measuring the influence of the terrain on its motion. We measure the robots vertical acceleration, which reflects the terrain roughness. The paper presents a hybrid (discrete—continuous) approach to guide and control the robot. After decomposing the map into triangular cells, a path planning algorithm is used to determine a discrete sequence of cells that minimizes the navigation cost. Robot control is accomplished by a fully continuous vector field that drives the robot through the sequence of triangular cells. This vector field allows smooth robot trajectories from any position inside the sequence to the goal, even for a small number of large cells. Moreover, the vector field is terrain dependent in the sense it changes the robot velocity according to the characteristics of the terrain. Experimental results with a differential driven, all-terrain mobile robot illustrate the proposed approach.

Conditions for Consensus of Multi-Agent Systems With Time-Delays and Uncertain Switching Topology

This paper proposes a new approach for the analysis of consensus of multi-agent systems subject to time-varying delayed control inputs and switching topology. The main contribution is a condition for consensus for a networked system based on linear matrix inequalities that takes into account the joint effect of time-varying delays and switching network topology. Topology changes are modeled using Markov jumps with uncertain rates of transitions. A practical example is shown to illustrate the main result in various scenarios.

Fully continuous vector fields for mobile robot navigation on sequences of discrete triangular regions

Several recent works have combined discrete and continuous motion planning methods for robot navigation and control. The basic idea of some of these works is to plan a path, by determining a sequence of neighboring discrete regions of the configuration space, and to assign a vector field that drives the robots through these regions. This paper addresses the problem of efficiently computing vector fields over a sequence of consecutive triangles. Differently from previous numerical approaches, which were not able to compute fully continuous fields in triangulated spaces, this paper presents an algorithm that is able to compute guaranteed continuous vector fields over a sequence of adjacent triangles.

On Computing Complex Navigation Functions

This paper addresses the problem of efficiently computing robot navigation functions. Navigation functions are potential functions free of spurious local minima that present an exact solution to the robot motion planning and control problem. Although some methodologies were found in the literature, none of them are easy to implement and generalize for complex shaped workspaces and robots. We discuss some of the difficulties encountered in the current methodologies and propose a novel approach using a Finite Element method for potential field computation.