This paper addresses the problem of planning the motion of one or more pursuers in a polygonal environment to eventually "see" an evader that is unpredictable, has unknown initial position, and is capable of moving arbitrarily fast. This problem was first introduced by Suzuki and Yamashita. Our study of this problem is motivated in part by robotics applications, such as surveillance with a mobile robot equipped with a camera that must find a moving target in a cluttered workspace. A few bounds are introduced, and a complete algorithm is presented for computing a successful motion strategy for a single pursuer. For simplyconnected free spaces, it is shown that the minimum number of pursuers required is \Theta(lg n). For multiply-connected free spaces, the bound is \Theta( p h + lg n) pursuers for a polygon that has n edges and h holes. A set of problems that are solvable by a single pursuer and require a linear number of recontaminations is shown. The complete algorithm searches a f...

This paper introduces a visibility-based motion planning problem in which the task is to coordinate the motions of one or more robots that have omnidirectional vision sensors, to eventually "see" a target that is unpredictable, has unknown initial position, and is capable of moving arbitrarily fast. A visibility region is associated with each robot, and the goal is to guarantee that the target will ultimately lie in at least one visibility region. Both a formal characterization of the general problem and several interesting problem instances are presented. A complete algorithm for computing the motion strategy of the robots is also presented, and is based on searching a finite cell complex that is constructed on the basis of critical information changes. A few computed solution strategies are shown. Several bounds on the minimum number of needed robots are also discussed. 1 Introduction Have you ever searched for someone in a building, possibly exploring the same places multiple time...

This paper addresses the problem of planning the motion of one or more pursuers in a polygonal environment to eventually "see" an evader that is unpredictable, has unknown initial position, and is capable of moving arbitrarily fast. A visibility region is associated witheach pursuer, and the goal is to guarantee that the evader will ultimately lie in at least one visibility region. The study of this problem is motivated inpart by robotics applications, such as surveillance with a mobile robot equipped withacamera that must nd a moving target in a cluttered workspace. A few bounds are introduced, and a complete algorithm is presented for computing a successful motion strategy. For a simply-connected free space, a logarithmic bound is established on the minimum of pursuers needed. Loose bounds for multiply-connected free spaces are also given. A set of problems that are solvable by a single pursuer and require a linear number of recontaminations is shown. The complete algorithm searches a nite cell complex that is constructed onthebasis of critical information changes. This concept can be applied in principle to multiple-pursuer problems, and the case of a single pursuer has been implemented. Several solution strategies are shown, most of which were computed in a few seconds on a standard workstation.

Localization is a critical issue in mobile robotics. If the robot does not know where it is, it, cannot effectively plan movements, locate objects, or reach goals. In this paper, we describe probabilistic self-localization techniques for mobile robots that are based on the principal of maximum-likelihood estimation. The basic method is to compare a map generated at the current robot position to a previously generated map of the environment to prohabilistically maximize the agreement between the maps. This method is able to operate in both indoor and outdoor environments using either discrete features or an occupancy grid to represent the world map. The map may be generated using any method to detect features in the robot's surroundings, including vision, sonar, a d laser range-finder. A global search of the pose space is performed that guarantees that the best position in a discretized pose space is found according to the probabilistic: map agreement measure. In addition, fitting the likelihood function with a parameterized smface allows both subpixel localization and uncertainty estimation to be performed. The application of these techniques in several experiments is described, including experimental localization results for the Sojourner Mars rover. 1

Abstract—Map building is an important issue for all the applications in mobile robotics in which the environment is unknown and, in general, in order to have a robot exhibit a fully autonomous behavior. A major problem in map building is due to the imprecision of sensor measures. In this paper, we propose a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an environment by a robot capable of identifying landmarks. Knowledge being acquired is modeled by a relational graph whose vertices and arcs represent, respectively, landmarks and routes. Elastic correction is based on an analogy between the graph modeling the environment and a mechanical structure: the map is regarded as a truss where each route is an elastic bar and each landmark a node. Errors are corrected as a result of the deformations induced from the forces arising within the structure as inconsistent measures are taken. The elasticity parameters characterizing the structure are used to model the uncertainty on odometry. The paper presents results from simulations showing the effectiveness of the method for reducing the overall metric error and proving its robustness with reference to topological errors and to unpredictable sensor errors. Index Terms—Error correction, mobile robotics, odometry. I.

Abstract — This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments. Index Terms — Mobile robots, localization, machine vision I.

In this paper, we present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an EKF integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. We apply the SPmodel to the problem of location estimation in indoor mobile robotics, experimenting with the mobile robot MACROBE. We have chosen two types of complementary sensory information: 1) range images; 2) intensity images; obtained from a laser sensor. Results of these experiments show that fusing simple and computationally inexpensive sensory information can allow a mobile robot to precisely locate itself. They also demonstrate the generality of the proposed fusion and integration mechanism.

With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this article, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.

Introduction Robots are versatile mechanical devices equipped with actuators and sensors under the control of a computing system. They perform tasks by executing motions in the physical space. This space is populated by various objects and is subject to the laws of nature. A typical task consists of achieving a goal spatial arrangement of objects from a given initial arrangement, for example, assembling a product. Robots are programmable, which means that they can perform a variety of tasks by simply changing the software commanding them. This software embeds robot algorithms, which are abstract descriptions of processes consisting of motions and sensing operations in the physical space. Robot algorithms differ in significant ways from traditional computer algorithms. The latter have full control over, and perfect access to the data they use, letting aside, for example, problems related to floating-point arithmetic. In contrast, robot algorithms eventually apply to physical objects i

Determination of relative three-dimensional (3--D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to this problem that uses two-dimensional (2--D), intensity images from a single camera is desirable for real-time applications. Where the object geometry is unknown, the estimation of structure is also required. A single camera is advantageous because a standard video camera is low in cost, setup and calibration are simple, physical space requirements are small, reliability is high, and low-cost hardware is available for digitizing and processing the images. A di#culty in performing this measurement is the process of projecting 3--D object features to 2--D images, a nonlinear transformation. Noise is present in the form of perturbations to the assumed process dynamics, imperfections in system modeling, and errors in the feature locations extracted from the 2--D images. This dissertation presents solutions to the remote measurement problem for a dynamic system given a sequence of 2--D intensity images of an object where feature positions of the object are known relative to a base reference frame and where the feature positions are unknown relative to a base reference frame. The 3--D transformation is modeled as a nonlinear stochastic system with the state estimate providing six degree-of-freedom motion and position values. The stochastic model uses the iterated extended Kalman filter as an estimator and as a screw representation of the 3--D transformation based on dual quaternions. Dual quaternions provide a means to represent both rotation and translation in a unified notation. The method has been implemented and tes...