Results 1  10
of
43
Rendezvous without Coordinates
"... Abstract — We study minimalism in sensing and control by considering a multiagent system in which each agent moves like a Dubins car and has a limited sensor that reports only the presence of another agent within some sector of its windshield. Using a very simple quantized control law with three va ..."
Abstract

Cited by 22 (4 self)
 Add to MetaCart
(Show Context)
Abstract — We study minimalism in sensing and control by considering a multiagent system in which each agent moves like a Dubins car and has a limited sensor that reports only the presence of another agent within some sector of its windshield. Using a very simple quantized control law with three values, each agent tracks another agent assigned to it by maintaining that agent within this windshield sector. We use Lyapunov analysis to show that by acting autonomously in this way, the agents will achieve rendezvous if the initial assignment graph is connected. A distinguishing feature of our approach is that it does not involve any estimation procedure aimed at reconstructing coordinate information. Our scenario thus appears to be the first example in which an interesting task is performed with extremely coarse sensing and control, and without state estimation. The system was implemented in computer simulation, accessible through the Web, of which the results are presented in the paper. (a) (b)
Sensor Beams, Obstacles, and Possible Paths
, 2008
"... This paper introduces a problem in which an agent (robot, human, or animal) travels among obstacles and binary detection beams. The task is to determine the possible agent path based only on the binary sensor data. This is a basic filtering problem encountered in many settings, which may arise fro ..."
Abstract

Cited by 19 (11 self)
 Add to MetaCart
(Show Context)
This paper introduces a problem in which an agent (robot, human, or animal) travels among obstacles and binary detection beams. The task is to determine the possible agent path based only on the binary sensor data. This is a basic filtering problem encountered in many settings, which may arise from physical sensor beams or virtual beams that are derived from other sensing modalities. Methods are given for three alternative representations: 1) the possible sequences of regions visited, 2) path descriptions up to homotopy class, and 3) numbers of times winding around obstacles. The solutions are adapted to the minimal sensing setting; therefore, precise estimation, distances, and coordinates are replaced by topological expressions. Applications include sensorbased forensics, assisted living, security, and environmental monitoring.
Tracking Hidden Agents Through Shadow Information Spaces
, 2008
"... This paper addresses problems of inferring the locations of moving agents from combinatorial data extracted by robots that carry sensors. The agents move unpredictably and may be fully distinguishable, partially distinguishable, or completely indistinguishable. The key is to introduce information ..."
Abstract

Cited by 15 (9 self)
 Add to MetaCart
(Show Context)
This paper addresses problems of inferring the locations of moving agents from combinatorial data extracted by robots that carry sensors. The agents move unpredictably and may be fully distinguishable, partially distinguishable, or completely indistinguishable. The key is to introduce information spaces that extract and maintain combinatorial sensing information. This leads to monitoring the changes in connected components of the shadow region, which is the set of points not visible to any sensors at a given time. When used in combination with a path generator for the robots, the approach solves problems such as counting the number of agents, determining movements of teams of agents, and solving pursuitevasion problems. An implementation with examples is presented.
On the value of ignorance: Balancing tracking and privacy using a twobit sensor
 in Proc. Workshop on the Algorithmic Foundations of Robotics
"... Abstract: We consider a target tracking problem in which, in addition to sensing some information about the position of a mobile target, the tracker must also ensure that the privacy of that target is preserved, even in the presence of adversaries that have complete access to the tracker’s sensor da ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
Abstract: We consider a target tracking problem in which, in addition to sensing some information about the position of a mobile target, the tracker must also ensure that the privacy of that target is preserved, even in the presence of adversaries that have complete access to the tracker’s sensor data. This kind of problem is important for many kinds of robot systems that involve communication systems or agents that cannot be fully trusted. In this paper, we (1) introduce a formal, quantitative definition for privacy, (2) describe algorithms that allow a robot to maintain conservative estimates of its performance in terms of tracking and privacy, (3) give strategies for the tracker to maximize its tracking performance, subject to constraints on the allowable privacy levels, and (4) present an implementation of these methods along with some experimental results. 1
Discovering a point source in unknown environments
 in “Algorithmic Foundation of Robotics VII
, 2009
"... Abstract: We consider the inverse problem of discovering the location of a source from very sparse point measurements in a bounded domain that contains impenetrable (and possibly unknown) obstacles. We present an adaptive algorithm for determining the measurement locations, and ultimately, the sour ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
(Show Context)
Abstract: We consider the inverse problem of discovering the location of a source from very sparse point measurements in a bounded domain that contains impenetrable (and possibly unknown) obstacles. We present an adaptive algorithm for determining the measurement locations, and ultimately, the source locations. Specifically, we investigate source discovery for the Laplace operator, though the approach can be applied to more general linear partial differential operators. We propose a strategy for the case when the obstacles are unknown and the environment has to be mapped out using a range sensor concurrently with source discovery. 1
Toward Simulating Realistic PursuitEvasion Using a RoadmapBased Approach
"... Abstract. Inthiswork,wedescribeanapproachformodelingandsimulating group behaviors for pursuitevasion that uses a graphbased representation of the environment and integrates multiagent simulation with roadmapbased path planning. We demonstrate the utility of this approach for a variety of scenari ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
(Show Context)
Abstract. Inthiswork,wedescribeanapproachformodelingandsimulating group behaviors for pursuitevasion that uses a graphbased representation of the environment and integrates multiagent simulation with roadmapbased path planning. We demonstrate the utility of this approach for a variety of scenarios including pursuitevasion on terrains, in multilevel buildings, and in crowds.
An Efficient Motion Strategy to Compute ExpectedTime Locally Optimal Continuous Search Paths in Known Environments
, 2009
"... In this paper we address the problem of finding timeoptimal search paths in known environments. In particular, we address the problem of searching a known environment for an object whose unknown location is characterized by a known probability density function (PDF). With this formulation, the time ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
In this paper we address the problem of finding timeoptimal search paths in known environments. In particular, we address the problem of searching a known environment for an object whose unknown location is characterized by a known probability density function (PDF). With this formulation, the time required to find the object is a random variable induced by the choice of search path together with the PDF for the object’s location. The optimization problem we consider is that of finding the path that minimizes the expected value of the time required to find the object. As the complexity of the problem precludes finding an exact optimal solution, we propose a twolevel, heuristic approach to finding the optimal search path. At the top level, we use a decomposition of the workspace based on critical curves to impose a qualitative structure on the solution trajectory. At the lower level, individual segments of this trajectory are refined using local numerical optimization methods. We have implemented the algorithm and present simulation results for the particular case when the object’s location is specified by the uniform PDF.
Sensing and filtering: A tutorial based on preimages and information spaces. Foundations and Trends in Robotics
"... This tutorial presents a fresh perspective on sensing uncertainty and filtering with the intention of understanding what information is minimally needed to achieve a specified task. The guiding principle is not to sense, represent, and encode more than is necessary. The concepts and tools are motiv ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
This tutorial presents a fresh perspective on sensing uncertainty and filtering with the intention of understanding what information is minimally needed to achieve a specified task. The guiding principle is not to sense, represent, and encode more than is necessary. The concepts and tools are motivated by many tasks of current interest, such as tracking, monitoring, navigation, pursuitevasion, exploration, and mapping. First, an overview of sensors that appear in numerous systems is presented. Following this, the notion of a virtual sensor is explained, which provides a mathematical way to model numerous sensors while abstracting away their particular physical implementation. Dozens of useful models are given, each as a mapping from the physical world to the set of possible sensor outputs. Preimages with respect to this mapping represent a fundamental source of uncertainty: These are equivalence classes of physical states that would produce the same sensor output. Pursuing this idea further, the powerful notion of a sensor lattice is introduced, in which all possible virtual sensors can be rigorously compared. The next part introduces filters that aggregate information from multiple sensor readings. The integration of information over space and time is considered. In the spatial setting, classical triangulation methods are expressed in terms of preimages. In the temporal setting, an informationspace framework is introduced that encompasses familiar Kalman and Bayesian filters, but also introduces a novel family called combinatorial filters. Finally, the planning problem is presented in terms of filters and information spaces. The tutorial concludes with some discussion about connections to many related research fields and numerous open problems and future research directions. 1 1
Optimal Gap Navigation for a Disc Robot
"... Abstract This paper considers the problem of globally optimal navigation with respect to Euclidean distance for a discshaped, differentialdrive robot placed into an unknown, simply connected planar region with piecewiseanalytic boundary. The robot is unable to build precise geometric maps or loca ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Abstract This paper considers the problem of globally optimal navigation with respect to Euclidean distance for a discshaped, differentialdrive robot placed into an unknown, simply connected planar region with piecewiseanalytic boundary. The robot is unable to build precise geometric maps or localize itself in any Euclidean frame. Most of the robot’s information comes from a gap sensor, which indicates depth discontinuities and allows the robot to move toward them. A motion strategy is presented that optimally navigates the robot to any landmark in the region. Optimality is proved and the method is illustrated in simulation. 1
Optimal exploration of terrains with obstacles
 Proc. 12th Scandinavian Symposium and Workshops on Algorithm Theory
"... Abstract. A mobile robot represented by a point moving in the plane has to explore an unknown flat terrain with impassable obstacles. Both the terrain and the obstacles are modeled as arbitrary polygons. We consider two scenarios: the unlimited vision, when the robot situated at a point p of the ter ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
Abstract. A mobile robot represented by a point moving in the plane has to explore an unknown flat terrain with impassable obstacles. Both the terrain and the obstacles are modeled as arbitrary polygons. We consider two scenarios: the unlimited vision, when the robot situated at a point p of the terrain explores (sees) all points q of the terrain for which the segment pq belongs to the terrain, and the limited vision, when we require additionally that the distance between p and q be at most 1. All points of the terrain (except obstacles) have to be explored and the performance of an exploration algorithm, called its complexity, is measured by the length of the trajectory of the robot. For unlimited vision we show an exploration algorithm with complexity O(P + D √ k), where P is the total perimeter of the terrain (including perimeters of obstacles), D is the diameter of the convex hull of the terrain, and k is the number of obstacles. We do not assume knowledge of these parameters. We also prove a matching lower bound showing that the above complexity is optimal, even if the terrain is known to the robot. For limited vision we show exploration algorithms with complexity O(P +A+ √ Ak), where A is the area of the terrain (excluding obstacles). Our algorithms work either for arbitrary terrains, if one of the parameters A or k is known, or for cfat terrains, where c is any constant (unknown to the robot) and no additional knowledge is assumed. (A terrain T with obstacles is cfat if R/r ≤ c, where R is the radius of the smallest disc containing T and r is the radius of the largest disc contained in T.) We also prove a matching lower bound Ω(P +A+ √ Ak) on the complexity of exploration for limited vision, even if the terrain is known to the robot.