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17
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.
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
PursuitEvasion on Trees by Robot Teams
, 2010
"... We present GraphClear, a novel pursuitevasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges respectively. A sweep action ..."
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Cited by 25 (4 self)
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We present GraphClear, a novel pursuitevasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges respectively. A sweep action detects intruders in a vertex and represents the capability of the robot team to detect intruders in the region associated to the vertex. Similarly, a block action prevents intruders from crossing an edge and represents the capability to detect intruders as they move between regions. Both actions may require multiple robots to be executed. A strategy is a sequence of block and sweep actions detecting all intruders. When solving instances of GraphClear the goal is to determine optimal strategies, i.e. strategies using the least number of robots. We prove that for the general case of graphs the problem of computing optimal strategies is NPhard. Next, for the special case of trees we provide a polynomial time algorithm. The algorithm ensures that throughout the execution of the strategy all cleared vertices form a connected subtree, and we show it produces optimal strategies.
VisibilityBased PursuitEvasion with Bounded Speed
"... This paper presents an algorithm for a visibilitybased pursuitevasion problem in which bounds on the speeds of the pursuer and evader are given. The pursuer tries to find the evader inside of a simplyconnected polygonal environment, and the evader in turn tries actively to avoid detection. The ..."
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Cited by 23 (1 self)
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This paper presents an algorithm for a visibilitybased pursuitevasion problem in which bounds on the speeds of the pursuer and evader are given. The pursuer tries to find the evader inside of a simplyconnected polygonal environment, and the evader in turn tries actively to avoid detection. The algorithm is at least as powerful as the complete algorithm for the unbounded speed case, and with the knowledge of speed bounds, generates solutions for environments that were previously unsolvable. Furthermore, the paper develops a characterization of the set of possible evader positions as a function of time. This characterization is more complex than in the unboundspeed case, because it no longer depends only on the combinatorial changes in the visibility region of the pursuer.
Hoplites: A Market Framework for Complex Tight Coordination in MultiAgent Teams
, 2004
"... In this paper we present a new class of tasks for multirobot teams: those that require constant complex interaction between teammates. Much research has been done in the area of multirobot coordination, but no existing framework meets the technical demands of such tasks. We have developed Hoplites ..."
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Cited by 16 (1 self)
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In this paper we present a new class of tasks for multirobot teams: those that require constant complex interaction between teammates. Much research has been done in the area of multirobot coordination, but no existing framework meets the technical demands of such tasks. We have developed Hoplites in response to the need for a more capable framework. Hoplites is a marketbased framework that couples planning with both passive and active coordination strategies. It enables robots to change coordination strategies as the needs of the task change. Further, it efficiently facilitates tight coordination between multiple robots. We compare the performances of Hoplites and existing coordination frameworks in a security sweep domain. Our results show that Hoplites significantly improves the quality of solutions found by the team, particularly in the most complex instances of the domain.
Motion strategies for surveillance
 in Proceedings of Robotics: Science and Systems
, 2007
"... Abstract — We address the problem of surveillance in an environment with obstacles. We show that the problem of tracking an evader with one pursuer around one corner is completely decidable. The pursuer and the evader have complete information about each other’s instantaneous position. The pursuer h ..."
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Cited by 15 (2 self)
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Abstract — We address the problem of surveillance in an environment with obstacles. We show that the problem of tracking an evader with one pursuer around one corner is completely decidable. The pursuer and the evader have complete information about each other’s instantaneous position. The pursuer has complete information about the instantaneous velocity of the evader. We present a partition of the visibility region of the pursuer where based on the region in which the evader lies, we provide strategies for the evader to escape the visibility region of the pursuer or for the pursuer to track the target for all future time. We also present the solution to the inverse problem: given the position of the evader, the positions of the pursuer for which the evader can escape the visibility region of the target. These results have been provided for varying speeds of the pursuer and the evader. Based on the results of the inverse problem we provide an O(n 3 log n) algorithm that can decide if the evader can escape from the visibility region of a pursuer for some initial pursuer and evader positions. Finally, we extend the result of the target tracking problem around a corner in two dimensions to an edge in three dimensions. I.
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
Algorithms for Planning under Uncertainty in Prediction and Sensing
 CHAPTER 18 IN AUTONOMOUS MOBILE ROBOTS: SENSING, CONTROL, DECISIONMAKING, AND APPLICATIONS
, 2005
"... ..."
Mapping and PursuitEvasion Strategies For a Simple WallFollowing Robot
, 2010
"... This paper defines and analyzes a simple robot with local sensors that moves in an unknown polygonal environment. The robot can execute wallfollowing motions and can traverse the interior of the environment only when following parallel to an edge. The robot has no global sensors that would allow pr ..."
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Cited by 4 (1 self)
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This paper defines and analyzes a simple robot with local sensors that moves in an unknown polygonal environment. The robot can execute wallfollowing motions and can traverse the interior of the environment only when following parallel to an edge. The robot has no global sensors that would allow precise mapping or localization. Special information spaces are introduced for this particular model. Using these, strategies are presented for solving several tasks: 1) counting vertices, 2) computing the path winding number, 3) learning a combinatorial map, called the cut ordering, that encodes partial geometric information, and 4) solving pursuitevasion problems.
Mapping and PursuitEvasion Strategies For a Simple WallFollowing Robot
"... This paper defines and analyzes a simple robot with local sensors that moves in an unknown polygonal environment. The robot can execute wallfollowing motions and can traverse the interior of the environment only when following parallel to an edge. The robot has no global sensors that would allow pr ..."
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Cited by 1 (1 self)
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This paper defines and analyzes a simple robot with local sensors that moves in an unknown polygonal environment. The robot can execute wallfollowing motions and can traverse the interior of the environment only when following parallel to an edge. The robot has no global sensors that would allow precise mapping or localization. Special information spaces are introduced for this particular model. Using these, strategies are presented for solving several tasks: 1) counting vertices, 2) computing the path winding number, 3) learning a combinatorial map, called the cut ordering, that encodes partial geometric information, and 4) solving pursuitevasion problems.