Guilherme A. S. Pereira

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.

Decentralized Algorithms for Multi-Robot Manipulation via Caging

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.

Distributed Search and Rescue with Robot and Sensor Teams

We develop a network of distributed mobile sensor systems as a solution to the emergency response problem. The mobile sensors are inside a building and they form a connected ad-hoc network. We discuss cooperative localization algorithms for these nodes. The sensors collect temperature data and run a distributed algorithm to assemble a temperature gradient. The mobile nodes are controlled to navigate using this temperature gradient. We also discuss how such networks can assist human users to find an exit. We have conducted an experiment to at a facility used to train firefighters to understand the environment and to test component technology. Results from experiments at this facility as well as simulations are presented here.

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.

Control of swarms based on Hydrodynamic models

We address the problem of pattern generation in obstacle-filled environments by a swarm of mobile robots. Decentralized controllers are devised by using the Smoothed Particle Hydrodynamics (SPH) method. The swarm is modelled as an incompressible fluid subjected to external forces. Actual robot issues such as finite size and nonholonomic constraints are also addressed. Collision avoidance guarantees are discussed. Finally, in the absence of obstacles, we prove for the first time stability and convergence of controllers based on the SPH.

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.

Decentralized Algorithms for Multirobot Manipulation via Caging

This paper addresses 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 achieve a condition of object closure, and then, move toward a goal position while maintaining this condition. We present experimental results that show car-like robots controlled using visual feedback, transporting an object in an obstacle free environment toward a prescribed goal.

Cooperative Transport of Planar Objects by Multiple Mobile Robots Using Object Closure

This paper addresses the problem of transporting objects by 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. We present experimental results that show car-like robots controlled using visual feedback, transporting an object in an obstacle free environment toward a prescribed goal.

Coordination of multiple mobile robots in an object carrying task using implicit communication

Addresses the problem of coordinating multiple mobile robots in a tightly coupled task by means of implicit communication. This approach allows the development of controllers that do not depend on any explicit data flow between the robots, thus relying only on local sensor information. A box-carrying task is used to validate the proposed methodology both in simulation and in real-world experiments. Results show that implicit communication can be used together or replacing explicit communication for the cooperative box carrying task, making the system more robust to faulty communication environments.