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18
The Power of a Pebble: Exploring and Mapping Directed Graphs
, 1998
"... Exploring and mapping an unknown environment is a fundamental problem, which is studied in various contexts. Many works have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a model that makes very limited assumptions on the environment and ..."
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Cited by 106 (4 self)
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Exploring and mapping an unknown environment is a fundamental problem, which is studied in various contexts. Many works have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a model that makes very limited assumptions on the environment and solve the mapping problem in this general setting. We model
Distributed covering by antrobots using evaporating traces
 IEEE Transactions on Robotics and Automation
, 1999
"... Abstract—Ants and other insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper, we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an unmapped building, or an ..."
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Cited by 59 (1 self)
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Abstract—Ants and other insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper, we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an unmapped building, or any task that requires the traversal of an unknown region. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every model is a decentralized multiagent adaptive system with a shared memory, moving on a graph whose vertices are the floortiles. We describe three methods of covering a graph in a distributed fashion, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g., depth first search), our algorithms are adaptive: they will complete the traversal of the graph even if some of the a(ge)nts die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time. Index Terms—Antrobotics, covering, exploration, multiagent systems, robotics.
Spanningtree based coverage of continuous areas by a mobile robot
 Annals of Mathematics and Artificial Intelligence
, 2001
"... This paper considers the problem of covering a continuous planar areabyasquareshaped tool attached to a mobile robot. Using a toolbased approximation of the workarea, we present an algorithm that covers every point of the approximate area for tasks such as oor cleaning, lawn mowing, and eld demin ..."
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Cited by 42 (1 self)
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This paper considers the problem of covering a continuous planar areabyasquareshaped tool attached to a mobile robot. Using a toolbased approximation of the workarea, we present an algorithm that covers every point of the approximate area for tasks such as oor cleaning, lawn mowing, and eld demining. The algorithm, called Spanning Tree Covering (STC), subdivides the workarea into disjoint cells corresponding to the squareshaped tool, then follows a spanning tree of the graph induced by the cells, while covering every point precisely once. We present and analyze three versions of the STC algorithm. The rst version is oline, where the robot has perfect apriori knowledge of its environment. The oline STC algorithm computes an optimal covering path in linear time O(N), where N is the number of cells comprising the area. The second version of STC is online, where the robot uses its onboard sensors to detect obstacles and construct a spanning tree of the environment while covering the workarea. The online STC algorithm completes an optimal covering path in time O(N), but requires O(N) memory for its implementation. The third version of STC is \ant&quot;like. In this version, too, the robot has no apriori knowledge of the environment, but it may leave pheromonelike markers during the coverage process. The antlike STC algorithm runs in time O(N), and requires only O(1) memory. Finally we present simulation results of the three STC algorithms, demonstrating their e ectiveness in cases where thetool size is signi cantly smaller than the workarea characteristic dimension. 1
Building TerrainCovering Ant Robots: A Feasibility Study
, 2004
"... Robotics researchers have studied robots that can follow trails laid by other robots. We, on the other hand, study robots that leave trails in the terrain to cover closed terrain repeatedly. How to design such ant robots has so far been studied only theoretically for gross robot simplifications. I ..."
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Cited by 23 (0 self)
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Robotics researchers have studied robots that can follow trails laid by other robots. We, on the other hand, study robots that leave trails in the terrain to cover closed terrain repeatedly. How to design such ant robots has so far been studied only theoretically for gross robot simplifications. In this article, we describe for the first time how to build physical ant robots that cover terrain and test their design both in realistic simulation environments and on a Pebbles III robot. We show that the coverage behavior of our ant robots can be modeled with a modified version of node counting, a realtime search method. We then report on first experiments that we performed to understand their efficiency and robustness in situations where some ant robots fail, they are moved without realizing this, the trails are of uneven quality, and some trails are destroyed. Finally, we report the results of a largescale simulation experiment where ten ant robots covered a factory floor of 25 by 25 meters repeatedly over 85 hours without getting stuck.
MAC vs. PC: Determinism and Randomness as Complementary Approaches to Robotic Exploration of Continuous Unknown Domains
, 2000
"... Three methods are described for exploring a continuous unknown planar region by a group of robots having limited sensors and no explicit communication. We formalize the problem, prove that its offline version is NPhard, and show a lower bound on the length of any solution. Then a deterministic mar ..."
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Cited by 17 (1 self)
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Three methods are described for exploring a continuous unknown planar region by a group of robots having limited sensors and no explicit communication. We formalize the problem, prove that its offline version is NPhard, and show a lower bound on the length of any solution. Then a deterministic mark and cover (MAC) algorithm is described for the online problem using shortlived navigational markers as means of navigation and indirect communication. The convergence of the algorithm is proved, and its cover time is shown to be the asymptotically optimal O(A/a), where A is the total area and a is the area covered by the robot in a single step. TheMAC algorithm is tested against an alternative randomized probabilistic covering (PC) method, which does not rely on sensors but is still able to cover an unknown region in an expected time that depends polynomially on the dimensions of the region. Both algorithms enable cooperation of several robots to achieve faster coverage. Finally, we show...
SelfOrganizing Visual Maps
"... This paper deals with automatically learning the spatial distribution of a set of measurements: images, in the examples presented here. The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem of organizing ..."
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Cited by 4 (0 self)
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This paper deals with automatically learning the spatial distribution of a set of measurements: images, in the examples presented here. The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem of organizing an ensemble of images of an environment in terms of the positions from which the images were obtained, and where only limited prior odometric information is available. Our approach employs a featurebased method derived from a probabilistic robot localization framework. Initially, a set of visual landmarks are selected from the images and correspondences are found across the ensemble.
Robot Map Verification of a Graph World
, 1998
"... In the map verification problem, a robot is given a (possibly incorrect) map M of the world G with its position and orientation indicated on the map. The task is to find out whether this map, for the given robot position and orientation in the map, is correct for the world G. We consider the world m ..."
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Cited by 4 (0 self)
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In the map verification problem, a robot is given a (possibly incorrect) map M of the world G with its position and orientation indicated on the map. The task is to find out whether this map, for the given robot position and orientation in the map, is correct for the world G. We consider the world model with a graph G =(VG#E G ) in which, for each vertex, edges incidenttothevertex are ordered cyclically around that vertex. (This holds similarly for the map M =(VM#E M ).) The robot can traverse edges and enumerate edges incident on the current vertex, but it cannot distinguish vertices and edges from each other. To solve the verification problem, the robot uses a portable edge marker, that it can put down and pick up as needed. The robot can recognize the edge marker when it encounters it in G. By reducing the verification problem to an exploration problem, verification can be completed in O(jV G j\ThetajE G j)edgetraversals (the mechanical cost) with the help of a single vertex marke...
Local Exploration: Online Algorithms and a Probabilistic Framework
 In Proc. IEEE Int. Conf. Robotics and Automation (ICRA 2003
, 2003
"... Mapping an environment with an imaging sensor becomes very challenging if the environment to be mapped is unknown and has to be explored. Exploration involves the planning of views so that the entire environment is covered. The majority of implemented mapping systems use a heuristic planning while t ..."
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Cited by 3 (0 self)
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Mapping an environment with an imaging sensor becomes very challenging if the environment to be mapped is unknown and has to be explored. Exploration involves the planning of views so that the entire environment is covered. The majority of implemented mapping systems use a heuristic planning while theoretical approaches regard only the traveled distance as cost. However, practical range acquisition systems spend a considerable amount of time for acquisition. In this paper, we address the problem of minimizing the cost of looking around a corner, involving the time spent in traveling as well as the time spent for reconstruction. Such a local exploration can be used as a subroutine for global algorithms. We prove competitive ratios for two online algorithms. Then, we provide two representations of local exploration as a Markov Decision Process and apply a known policy iteration algorithm. Simulation results show that for some distributions the probabilistic approach outperforms deterministic strategies. I.
Tree Exploration with Advice
, 2008
"... We study the amount of knowledge about the network that is required in order to efficiently solve a task concerning this network. The impact of available information on the efficiency of solving network problems, such as communication or exploration, has been investigated before but assumptions conc ..."
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Cited by 2 (2 self)
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We study the amount of knowledge about the network that is required in order to efficiently solve a task concerning this network. The impact of available information on the efficiency of solving network problems, such as communication or exploration, has been investigated before but assumptions concerned availability of particular items of information about the network, such as the size, the diameter, or a map of the network. In contrast, our approach is quantitative: we investigate the minimum number of bits of information (bits of advice) that has to be given to an algorithm in order to perform a task with given efficiency. We illustrate this quantitative approach to available knowledge by the task of tree exploration. A mobile entity (robot) has to traverse all edges of an unknown tree, using as few edge traversals as possible. The quality of an exploration algorithm A is measured by its competitive ratio, i.e., by comparing its cost (number of edge traversals) to the length of the shortest path containing all edges of the tree. DepthFirstSearch has competitive ratio 2 and, in the absence of any information about
Algorithms for Distributed and Mobile Sensing
, 2004
"... Sensing remote, complex and large environments is an important task that arises in diverse applications including planetary exploration, monitoring forest fires and the surveillance of large factories. Currently, automation of such sensing tasks in complex environments is achieved either by deployin ..."
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Cited by 1 (0 self)
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Sensing remote, complex and large environments is an important task that arises in diverse applications including planetary exploration, monitoring forest fires and the surveillance of large factories. Currently, automation of such sensing tasks in complex environments is achieved either by deploying many stationary sensors to the environment, or by mounting a sensor on a mobile device and using the device to sense the environment. The