Luiz Chaimowicz

Dynamic role assignment for cooperative robots

Proposes a methodology for coordinating multi-robot teams in the execution of cooperative tasks. It is based on a dynamic role-assignment mechanism in which the robots assume and exchange roles during cooperation. We model the role assignment under a hybrid systems framework, using a hybrid automaton to represent roles, transitions and controllers. Using a multi-robot simulator, the methodology is demonstrated in a cooperative transportation task, in which a group of robots must find and cooperatively transport several objects scattered in the environment.

An architecture for tightly coupled multi-robot cooperation

Proposes an architecture for tightly coupled multi-robot coordination that is well suited to cooperative manipulation tasks. At all times, a robot is identified as a leader, while the others are designated as followers. The assignment of roles and the coordination between the robots is guaranteed by communication protocols and control algorithms. The key feature is the flexibility that allows changes in leadership and assignment of roles during the execution of a task. We describe the experimental implementation and demonstration in a cooperative transportation task, in which two and three heterogeneous robots cooperate to carry a large object in an environment containing obstacles.

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. nJournal of Field Robotics 24(11), 991–1014 (2007)

Decentralized controllers for shape generation with robotic swarms

We address the synthesis of controllers for a swarm of robots to generate a desired two-dimensional geometric pattern specified by a simple closed planar curve with local interactions for avoiding collisions or maintaining specified relative distance constraints. The controllers are decentralized in the sense that the robots do not need to exchange or know each others state information. Instead, we assume that the robots have sensors allowing them to obtain information about relative positions of neighbors within a known range. We establish stability and convergence properties of the controllers for a certain class of simple closed curves. We illustrate our approach through simulations and consider extensions to more general planar curves.

Controlling Swarms of Robots Using Interpolated Implicit Functions

We address the synthesis of controllers for large groups of robots and sensors, tackling the specific problem of controlling a swarm of robots to generate patterns specified by implicit functions of the form s(x, y) = 0. We derive decentralized controllers that allow the robots to converge to a given curve S and spread along this curve. We consider implicit functions that are weighted sums of radial basis functions created by interpolating from a set of constraint points, which give us a high degree of control over the desired 2D curves. We describe the generation of simple plans for swarms of robots using these functions and illustrate our approach through simulations and real experiments.

A Paradigm for Dynamic Coordination of Multiple Robots

In this paper, we present a paradigm for coordinating multiple robots in the execution of cooperative tasks. The basic idea in the paper is to assign to each robot in the team, a role that determines its actions during the cooperation. The robots dynamically assume and exchange roles in a synchronized manner in order to perform the task successfully, adapting to unexpected events in the environment. We model this mechanism using a hybrid systems framework and apply it in different cooperative tasks: cooperative manipulation and cooperative search and transportation. Simulations and real experiments demonstrating the effectiveness of the proposed paradigm are presented.

Aerial Shepherds: Coordination among UAVs and Swarms of Robots

We address the problem of deploying groups of tens or hundreds of unmanned ground vehicles (UGVs) in urban environments where a group of aerial vehicles (UAVs) can be used to coordinate the ground vehicles. We envision a hierarchy in which UAVs with aerial cameras can be used to monitor and command a swarm of UGVs, controlling the splitting and merging of the swarm into groups and the shape (distribution) and motion of each group. We call these UAVs Aerial Shepherds. We show a probabilistic approach using the EM algorithm for the initial assignment of shepherds to groups and present behaviors that allow an efficient hierarchical decomposition. We illustrate the framework through simulation examples, with applications to deployment in an urban environment.

Experiments in multirobot air-ground coordination

This paper addresses the problem of coordinating aerial and ground vehicles in tasks that involve exploration, identification of targets and maintaining a connected communication network. We focus on the problem of localizing vehicles in urban environments where GPS signals are often unreliable or unavailable. We first describe our multi-robot testbed and the control software used to coordinate ground and aerial vehicles. We present the results of experiments in air-ground localization analyzing three complementary approaches to determining the positions of vehicles on the ground. We show that the coordination of aerial vehicles with ground vehicles is necessary to get accurate estimates of the state of the system.

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.

Deploying Air-Ground Multi-Robot Teams in Urban Environments

We present some of the work performed in the GRASP Laboratory with the objective of deploying multi-robot teams in urban environments. Specifically, we focus on three important issues in this type of mission: the development of tools for providing situational awareness, the use of air and ground vehicles for cooperative sensing and the construction of radio maps to keep team connectivity. We describe the main approaches that we have been using for tackling these issues and present some preliminary results from experiments conducted with our team of air and ground vehicles.