Andrew Howard

Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem

This paper considers the problem of deploying a mobile sensor network in an unknown environment. A mobile sensor network is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. Such networks are capable of self-deployment; i.e., starting from some compact initial configuration, the nodes in the network can spread out such that the area ‘covered’ by the network is maximized. In this paper, we present a potential-field-based approach to deployment. The fields are constructed such that each node is repelled by both obstacles and by other nodes, thereby forcing the network to spread itself throughout the environment. The approach is both distributed and scalable.

Design and use paradigms for Gazebo, an open-source multi-robot simulator

Simulators have played a critical role in robotics research as tools for quick and efficient testing of new concepts, strategies, and algorithms. To date, most simulators have been restricted to 2D worlds, and few have matured to the point where they are both highly capable and easily adaptable. Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots. Its open source status, fine grained control, and high fidelity place Gazebo in a unique position to become more than just a stepping stone between the drawing board and real hardware: data visualization, simulation of remote environments, and even reverse engineering of blackbox systems are all possible applications. Gazebo is developed in cooperation with the Player and Stage projects (Gerkey, B. P., et al., July 2003), (Gerkey, B. P., et al., May 2001), (Vaughan, R. T., et al., Oct. 2003), and is available from http://playerstage.sourceforge.net/gazebo/ gazebo.html.

An Incremental Self-Deployment Algorithm for Mobile Sensor Networks

This paper describes an incremental deployment algorithm for mobile sensor networks. A mobile sensor network is a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. The algorithm described in this paper will deploy such nodes one-at-a-time into an unknown environment, with each node making use of information gathered by previously deployed nodes to determine its deployment location. The algorithm is designed to maximize network ‘coverage’ while simultaneously ensuring that nodes retain line-of-sight relationships with one another. This latter constraint arises from the need to localize the nodes in an unknown environment: in our previous work on team localization (A. Howard, M.J. Matarić, and G.S. Sukhatme, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Switzerland, 2002; IEEE Transactions on Robotics and Autonomous Systems, 2002) we have shown how nodes can localize themselves by using other nodes as landmarks. This paper describes the incremental deployment algorithm and presents the results from an extensive series of simulation experiments. These experiments serve to both validate the algorithm and illuminate its empirical properties.

Most valuable player: a robot device server for distributed control

Successful distributed sensing and control require data to flow effectively between sensors, processors and actuators on single robots, in groups and across the Internet. We propose a mechanism for achieving this flow that we have found to be powerful and easy to use; we call it Player. Player combines an efficient message protocol with a simple device model. It is implemented as a multithreaded TCP socket server that provides transparent network access to a collection of sensors and actuators, often comprising a robot. The socket abstraction enables platform- and language-independent control of these devices, allowing the system designer to use the best tool for the task at hand Player is freely available from http://robotics.usc.edu/player.

Multi-robot Simultaneous Localization and Mapping using Particle Filters

This paper describes an on-line algorithm for multi-robot simultaneous localization and mapping (SLAM). The starting point is the single-robot Rao-Blackwellized particle filter described by Hähnel et al., and three key generalizations are made. First, the particle filter is extended to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, an approximation is introduced to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, it is assumed that pairs of robots will eventually encounter one another, thereby determining their relative pose. This relative attitude is used to initialize the filter, and subsequent observations from both robots are combined into a common map. Third and finally, a method is introduced to integrate observations collected prior to the first robot encounter, using the notion of a virtual robot travelling backwards in time. This novel approach allows one to integrate all data from all robots into a single common map.

Experiments with a Large Heterogeneous Mobile Robot Team: Exploration, Mapping, Deployment and Detection

We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large number of robots into an unexplored building, map the building interior, detect and track intruders, and transmit all of the above information to a remote operator. To satisfy these requirements, we developed a heterogeneous robot team consisting of approximately 80 robots. We sketch the key technical elements of this team, focusing on the novel aspects, and present selected results from supervised experiments conducted in a 600 m 2 indoor environment.

Localization for mobile robot teams using maximum likelihood estimation

This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks, that is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, external landmarks, or instrumentation of the environment. Our method assumes that each robot is able to measure the relative pose of nearby robots, together with changes in its own pose. Using a combination of maximum likelihood estimation and numerical optimization, we can subsequently infer the relative pose of every robot in the team. This paper describes the basic formalism, its practical implementation, and presents experimental results obtained using a team of four mobile robots.

On device abstractions for portable, reusable robot code

We seek to make robot programming more efficient by developing a standard abstract interface for robot hardware, based on familiar techniques from operating systems and network engineering. This paper describes the application of three well known abstractions, the character device model, the interface/driver model, and the client/server model to this purpose. These abstractions underlie Player/Stage, our open source project for rapid development of robot control systems. One product of this project is the Player Abstract Device Interface (PADI) specification, which defines a set of interfaces that capture the functionality of logically similar sensors and actuators. This specification is the central abstraction that enables Player-based controllers to run unchanged on a variety of real and simulated devices. We propose that PADI could be a starting point for development of a standard platform for robot interfacing, independent of Player, to enable code portability and re-use, while still providing access to the unique capabilities of individual devices.

A laser-based people tracker

Describes a method for real-time tracking of people in everyday environments, using multiple planar laser range-finders. People tracking is a well-studied problem in machine vision; we adapt some of those methods to laser range-finders. We group range measurements into entities such as blobs and objects, and use a Kalman filter to estimate trajectories for these objects. The filter is able to generate smooth trajectories, even when objects are occluded. The paper presents our evaluation of the trackers performance in a series of four experiments.

Putting the 'I' in 'team': an ego-centric approach to cooperative localization

This paper describes a cooperative method for relative localization of mobile robot teams; that is, it describes a method whereby every robot in the team can estimate the pose of every other robot, relative to itself. This robot does not require the use of GPS, landmarks, or maps of any kind; instead, robots make direct measurement of the relative pose of nearby robots, and broadcast this information to the team as a whole. Each robot processes this information independently to generate ego-centric estimate for the pose of other robots. Our method uses Bayesian formalism with a particle filter implementation, and is, as a consequence, very robust. It is also completely distributed, yet requires relatively little communication between robots. This paper describes the basic ego-centric formalism, sketches the implementation, and presents experimental results obtained using a team of four mobile robots.