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17
Visibilitybased pursuitevasion in an unknown planar environment
 International Journal of Robotics Research
, 2004
"... We address an online version of the visibilitybased pursuitevasion problem. We take a minimalist approach in modeling the capabilities of a pursuer robot. A point pursuer moves in an unknown, simplyconnected, piecewisesmooth planar environment, and is given the task of locating any unpredictable ..."
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Cited by 49 (6 self)
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We address an online version of the visibilitybased pursuitevasion problem. We take a minimalist approach in modeling the capabilities of a pursuer robot. A point pursuer moves in an unknown, simplyconnected, piecewisesmooth planar environment, and is given the task of locating any unpredictable, moving evaders that have unbounded speed. The evaders are assumed to be points that move continuously. To solve the problem, the pursuer must for each target have an unobstructed view of it at some time during execution. The pursuer is equipped with a range sensor that measures the direction of depth discontinuities, but cannot provide precise depth measurements. All pursuer control is specified either in terms of this sensor or wallfollowing movements. The pursuer does not have localization capability or perfect control. We present a complete algorithm that enables the limited pursuer to clear the same environments that a pursuer with a complete map, perfect localization, and perfect control can clear (under certain general position assumptions). Theoretical guarantees that the evaders will be found are provided. The resulting algorithm to compute this strategy has been implemented in simulation. Results are shown for several examples. The approach is efficient and simple enough to be useful towards the development of real robot systems that perform visual searching. 1
DistanceOptimal Navigation in an Unknown Environment without Sensing Distances
 IEEE TRANSACTIONS ON ROBOTICS
"... 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 t ..."
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Cited by 43 (17 self)
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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 shortestpath 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, piecewiseanalytic 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 Navigation and Object Finding Without Geometric Maps or . . .
 IN PROC. IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 2003
"... In this paper we present a dynamic data structure, useful for robot navigation in an unknown, simplyconnected planar environment. The guiding philosophy in this work is to avoid traditional problems such as complete map building and localization by constructing a minimal representation based entirel ..."
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Cited by 36 (10 self)
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In this paper we present a dynamic data structure, useful for robot navigation in an unknown, simplyconnected planar environment. The guiding philosophy in this work is to avoid traditional problems such as complete map building and localization by constructing a minimal representation based entirely on critical events in online sensor measurements made by the robot. Furthermore, this representation provides a sensorfeedback motion strategy that guides the robot along an optimal trajectory between any two environment locations, and allows the search of static targets, even though there is no geometric map of the environment. We present algorithms for building the data structure in an unknown environment, and for using it to perform optimal navigation. We implemented these algorithms on a real mobile robot. Results are presented in which the robot builds the data structure online, and is able to use it without needing a global reference frame. Simulation results are also shown to demonstrate how the robot is able to find interesting objects in the environment.
Gap navigation trees: Minimal representation for visibilitybased tasks
 In Proc. Workshop on the Algorithmic Foundations of Robotics
, 2004
"... Abstract. In this paper we present our advances in a data structure, the Gap Navigation Tree (GNT), useful for solving different visibilitybased robotic tasks in unknown planar environments. We present its use for optimal robot navigation in simplyconnected environments, locally optimal navigation ..."
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Cited by 30 (9 self)
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Abstract. In this paper we present our advances in a data structure, the Gap Navigation Tree (GNT), useful for solving different visibilitybased robotic tasks in unknown planar environments. We present its use for optimal robot navigation in simplyconnected environments, locally optimal navigation in multiplyconnected environments, pursuitevasion, and robot localization. The guiding philosophy of this work is to avoid traditional problems such as complete map building and exact localization by constructing a minimal representation based entirely on critical events in online sensor measurements made by the robot. The data structure is introduced from an information space perspective, in which the information used among the different visibilitybased tasks is essentially the same, and it is up to the robot strategy to use it accordingly for the completion of the particular task. This is done through a simple sensor abstraction that reports the discontinuities in depth information of the environment from the robot’s perspective (gaps), and without any kind of geometric measurements. The GNT framework was successfully implemented on a real robot platform. 1
Hybrid Control for VisibilityBased PursuitEvasion Games
, 2004
"... Pursuitevasion games in complex environments have a rich but disconnected history. Continuous or differential pursuitevasion games focus on optimal control methods, and rely on very intense computations in order to provide locally optimal controls. Discrete pursuitevasion games on graphs are algo ..."
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Cited by 9 (2 self)
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Pursuitevasion games in complex environments have a rich but disconnected history. Continuous or differential pursuitevasion games focus on optimal control methods, and rely on very intense computations in order to provide locally optimal controls. Discrete pursuitevasion games on graphs are algorithmically much more appealing, but completely ignore the physical dynamics of the players, resulting in possibly infeasible motions. In this paper, we present a provable and algorithmically feasible solution for visibilitybased pursuitevasion games in simplyconnected environments, for players with dynamic constraints. This is achieved by combining two recent but distant results.
LocallyOptimal Navigation in MultiplyConnected Environments Without Geometric Maps
"... In this paper we present an algorithm to build a sensorbased, dynamic data structure useful for robot navigation in an unknown, multiplyconnected planar environment. This data structure offers a robust framework for robot navigation, avoiding the need of a complete geometric map or explicit locali ..."
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Cited by 8 (3 self)
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In this paper we present an algorithm to build a sensorbased, dynamic data structure useful for robot navigation in an unknown, multiplyconnected planar environment. This data structure offers a robust framework for robot navigation, avoiding the need of a complete geometric map or explicit localization, by building a minimal representation based entirely on critical events in online sensor measurements made by the robot. There are two sensing requirements for the robot: it must detect when it is close to the walls, to perform wallfollowing reliably, and it must be able to detect discontinuities in depth information. It is also assumed that the robot is able to drop, detect and recover a marker. The navigation paths generated are optimal up to the homotopy class to which the paths belong, even though no distance information is measured.
Information spaces for mobile robots
 in Proc. Int. Work. on
, 2005
"... Planning with sensing uncertainty is central to robotics. Sensor limitations often prevent accurate state estimation of the robot. Two general approaches can be taken for solving robotics tasks given sensing uncertainty. The first approach is to estimate the state and to solve the given task using t ..."
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Cited by 6 (0 self)
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Planning with sensing uncertainty is central to robotics. Sensor limitations often prevent accurate state estimation of the robot. Two general approaches can be taken for solving robotics tasks given sensing uncertainty. The first approach is to estimate the state and to solve the given task using the estimate as the real state. However, estimation of the state may sometimes be harder than solving the original task. The other approach is to avoid estimation of the state, which can be achieved by defining the information space, the space of all histories of actions and sensing observations of a robot system. Considering information spaces brings better understanding of problems involving uncertainty, and also allows finding better solutions to such problems. In this paper we give a brief description of the information space framework, followed by its use in some robotic tasks. 1
IBug: An IntensityBased Bug Algorithm
"... Abstract — This paper introduces a sensorbased planning algorithm that uses less sensing information than any others within the family of bug algorithms. The robot is unable to access precise information regarding position coordinates, angular coordinates, time, or odometry, but is nevertheless abl ..."
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Cited by 6 (3 self)
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Abstract — This paper introduces a sensorbased planning algorithm that uses less sensing information than any others within the family of bug algorithms. The robot is unable to access precise information regarding position coordinates, angular coordinates, time, or odometry, but is nevertheless able to navigate itself to a goal among unknown piecewiseanalytic obstacles in the plane. The only sensor providing real values is an intensity sensor, which measures the signal strength emanating from the goal. The signal intensity function may or may not be symmetric; the main requirement is that the level sets are concentric topological circles. Convergence analysis and distance bounds are established for the presented approach. I.
A Complete PursuitEvasion Algorithm for Two Pursuers Using Beam Detection
 in IEEE International Conference on Robotics and Automation, 2002
, 2002
"... We present an algorithm for a pair of pursuers, each with one rotating beam (flashlight, laser or a camera), searching for an unpredictable, moving target in a 2D environment (simple polygon). Given a polygon with n edges, the algorithm decides in time O(n ) whether it can be cleared by the pursu ..."
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Cited by 5 (2 self)
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We present an algorithm for a pair of pursuers, each with one rotating beam (flashlight, laser or a camera), searching for an unpredictable, moving target in a 2D environment (simple polygon). Given a polygon with n edges, the algorithm decides in time O(n ) whether it can be cleared by the pursuers, and if so, constructs a search schedule. The pursuers are allowed to move on the boundary and in the interior of the polygon. They are not required to maintain mutual visibility throughout the pursuit.
The Pledge Algorithm Reconsidered under Errors in Sensors and Motion
 In Proc. of the 1th Workshop on Approximation and Online Algorithms
, 2004
"... We consider the problem of escaping from an unknown polygonal maze under limited resources and under errors in sensors and motion. ..."
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Cited by 4 (4 self)
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We consider the problem of escaping from an unknown polygonal maze under limited resources and under errors in sensors and motion.