Rafael Murrieta-Cid

Distance-Optimal Navigation in an Unknown Environment Without Sensing Distances

This paper considers what can be accomplished using a mobile robot that has limited sensing. For navigation and mapping, the robot has only one sensor, which tracks the directions of depth discontinuities. There are no coordinates, and the robot is given a motion primitive that allows it to move toward discontinuities. The robot is incapable of performing localization or measuring any distances or angles. Nevertheless, when dropped into an unknown planar environment, the robot builds a data structure, called the gap navigation tree, which enables it to navigate optimally in terms of Euclidean distance traveled. In a sense, the robot is able to learn the critical information contained in the classical shortest-path roadmap, although surprisingly it is unable to extract metric information. We prove these results for the case of a point robot placed into a simply connected, piecewise-analytic planar environment. The case of multiply connected environments is also addressed, in which it is shown that further sensing assumptions are needed. Due to the limited sensor given to the robot, globally optimal navigation is impossible; however, our approach achieves locally optimal (within a homotopy class) navigation, which is the best that is theoretically possible under this robot model.

Optimal Paths for Landmark-Based Navigation by Differential-Drive Vehicles With Field-of-View Constraints

In this paper, we consider the problem of planning optimal paths for a differential-drive robot with limited sensing, that must maintain visibility of a fixed landmark as it navigates in its environment. In particular, we assume that the robots vision sensor has a limited field of view (FOV), and that the fixed landmark must remain within the FOV throughout the robots motion. We first investigate the nature of extremal paths that satisfy the FOV constraint. These extremal paths saturate the camera pan angle. We then show that optimal paths are composed of straight-line segments and sections of these these extremal paths. We provide the complete characterization of the shortest paths for the system by partitioning the plane into a set of disjoint regions, such that the structure of the optimal path is invariant over the individual regions

Planning exploration strategies for simultaneous localization and mapping

In this paper, we present techniques that allow one or multiple mobile robots to efficiently explore and model their environment. While much existing research in the area of Simultaneous Localization and Mapping (SLAM) focuses on issues related to uncertainty in sensor data, our work focuses on the problem of planning optimal exploration strategies. We develop a utility function that measures the quality of proposed sensing locations, give a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map. We also provide an efficient algorithm driven by our utility function. This algorithm is able to explore several steps ahead without incurring too high a computational cost. We have compared that exploration strategy with a totally greedy algorithm that optimizes our utility function with a one-step-look ahead. The planning algorithms which have been developed operate using simple but flexible models of the robot sensors and actuator abilities. Techniques that allow implementation of these sensor models on top of the capabilities of actual sensors have been provided. All of the proposed algorithms have been implemented either on real robots (for the case of individual robots) or in simulation (for the case of multiple robots), and experimental results are given. c 2005 Elsevier B.V. All rights reserved.

A reactive motion planner to maintain visibility of unpredictable targets

This paper deals with the problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape

Surveillance Strategies for a Pursuer with Finite Sensor Range

the shortest distance the target needs to move in order to escape the observers visibility region. Three main points are discussed: 1) the design and implementation of a reactive planner; 2) the integration and testing of such a planner in a robot system which includes perceptual and control capabilities; and 3) the design and simulation of a motion planner for the task of maintaining visibility of two targets using two mobile observers.

A Sampling-Based Motion Planning Approach to Maintain Visibility of Unpredictable Targets

This paper addresses the pursuit-evasion problem of maintaining surveillance by a pursuer of an evader in a world populated by polygonal obstacles. This requires the pursuer to plan colision-free motions that honor distance constraints imposed by sensor capabilities, while avoiding occlusion of the evader by any obstacle. The paper extends the three-dimensional cellular decomposition of Schwartz and Sharir to represent the four-dimensional configuration space of the pursuer-evader system, and derive necessary conditions for surveillance (equivalently, sufficient conditions for escape) in terms of this new representation A game theoretic formulation of the problem is then given, and this formulation is used to characterize optimal escape trajectories for the evader. A shooting algorithm is proposed that finds these trajectories using the minimun principle. Finally, noting the similarities between this surveillance problem and the problem of cooperative manipulation by two robots, several cooperation strategies are presented that maximize system performance for cooperative motions.

A Complexity result for the pursuit-evasion game of maintaining visibility of a moving evader

This paper deals with the surveillance problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape—the shortest distance the target must move to escape an observers visibility region. Since this optimization problem is intractable, we use randomized methods to generate candidate surveillance paths for the observers. We have implemented our algorithms, and we provide experimental results using real mobile robots for the single target case, and simulation results for the case of two targets-two observers.

3-D Modelling and Robot Localization from Visual and Range Data in Natural Scenes

In this paper we consider the problem of maintaining visibility of a moving evader by a mobile robot, the pursuer, in an environment with obstacles. We simultaneously consider bounded speed for both players and a variable distance separating them. Unlike our previous efforts [R. Murrieta-Cid et al., 2007], we give special attention to the combinatorial problem that arises when searching for a solution through visiting several locations. We approach evader tracking by decomposing the environment into convex regions. We define two graphs: one is called the mutual visibility graph (MVG) and the other the accessibility graph (AG). The MVG provides a sufficient condition to maintain visibility of the evader while the AG defines possible regions to which either the pursuer or the evader may go to. The problem is framed as a non cooperative game. We establish the existence of a solution, based on a k- Min approach, for the following givens: the environment, the initial state of the evader and the pursuer, including their maximal speeds. We show that the problem of finding a solution to this game is NP-complete.

Landmark identification and tracking in natural environment

This paper concerns the exploration of a natural environment by a mobile robot equipped with both a video camera and a range sensor (stereo or laser range finder); we focus on the interest of such a multisensory system to deal with the incremental construction of a global model of the environment and with the 3-D localization of the mobile robot. The 3-D segmentation of the range data provides a geometrical scene description: the regions issued from the segmentation step correspond either to the ground or to objects emerging from this ground (e.g. rocks, vegetations). The 3D boundaries of these regions can be projected on the video image, so that each one can be characterized and afterwards identified, by a probabilistic method, to obtain its nature (e.g. soil, rocks ...); the ground region can be over-segmented, adding visual information, such as the texture. During the robot motions, a slow and a fast processes are simultaneously executed; in the modelling process (currently 0.1Hz), a global landmark-based model is incrementally built and the robot situation can be estimated if some discriminant landmarks are selected from the detected objects in the range data; in the tracking process (currently 1Hz), selected landmarks are tracked in the visual data. The tracking results are used to simplify the matching between landmarks in the modelling process.

Optimal Motion Strategies Based on Critical Events to Maintain Visibility of a Moving Target

This paper deals with the development of the use of visual function in order to add useful information for tasks of a mobile robot roving in natural environments. We have proposed and implemented the nominative region model which indicates every regions nature in an image. A color segmentation algorithm provides a synthetic description of the scene. Regions obtained from the segmentation stage are then characterized by their color and texture and afterwards identified in order to obtain their nature (grass, rocks, ...). Probabilistic methods are used to determine the nature of current elements in the environment. Then, one specific landmark is chosen according to its nature and shape and this representation is tracked through an image sequence.