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Efficient multirobot search for a moving target
 Int. J. Robotics Research
, 2009
"... This paper examines the problem of locating a mobile, nonadversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to ..."
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Cited by 29 (14 self)
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This paper examines the problem of locating a mobile, nonadversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multirobot efficient search path planning (MESPP) problem. Such path planning problems are NPhard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation algorithm that utilizes finitehorizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a nondecreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy nonlineofsight ranging measurements to the target. For this scenario, we derive and integrate online Bayesian measurement updating into our framework. We demonstrate the performance of our framework in two largescale simulated environments, and we further validate our results using data from a novel ultrawideband ranging sensor. Finally, we provide an analysis that demonstrates the rela
Nonmyopic Adaptive Informative Path Planning for Multiple Robots
"... Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an i ..."
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Cited by 17 (1 self)
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Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an important problem to trade off exploration (gathering information about the environment) and exploitation (using the current knowledge about the environment most effectively) for efficiently observing these environments. Even the nonadaptive setting, where paths are planned before observations are made, is NPhard, and has been subject to much research. In this paper, we present a novel approach to adaptive informative path planning that addresses this explorationexploitation tradeoff. Our approach is nonmyopic, i.e. it plans ahead for possible observations that can be made in the future. We quantify the benefit of exploration through the “adaptivity gap ” between an adaptive and a nonadaptive algorithm in terms of the uncertainty in the environment. Exploiting the submodularity (a diminishing returns property) and locality properties of the objective function, we develop an algorithm that performs provably nearoptimally in settings where the adaptivity gap is small. In case of large gap, we use an objective function that simultaneously optimizes paths for exploration and exploitation. We also provide an algorithm to extend any single robot algorithm for adaptive informative path planning to the multi robot setting while approximately preserving the theoretical guarantee of the single robot algorithm. We extensively evaluate our approach on a search and rescue domain and a scientific monitoring problem using a real robotic system.
Designing the HRTeam Framework: Lessons Learned from a RoughandReady Human/MultiRobot Team
 In Proceedings of the Workshop on Autonomous Robots and Multirobot Systems (ARMS) at AAMAS
, 2011
"... Abstract. In this workshop paper, we share the design and ongoing implementation of our HRTeam framework, which is constructed to support multiple robots working with a human operator in a dynamic environment. The team is comprised of one human plus a heterogeneous set of inexpensive, limitedfunct ..."
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Cited by 11 (11 self)
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Abstract. In this workshop paper, we share the design and ongoing implementation of our HRTeam framework, which is constructed to support multiple robots working with a human operator in a dynamic environment. The team is comprised of one human plus a heterogeneous set of inexpensive, limitedfunction robots. Although each individual robot has restricted mobility and sensing capabilities, together the teammembers constitute a multifunction, multirobot facility. We describe lowlevel system architecture details and explain how we have integrated a popular robotic control and simulation environment into our framework to support application of multiagent techniques in a hardwarebased environment. We highlight lessons learned regarding the integration of multiple varying robot platforms into our system, from both hardware and software perspectives. Our aim is to generate discussion amongst multirobot researchers concerning issues that are of particular interest and present particular difficulties to the multirobot systems community. 1
A Graph Search Algorithm for Indoor Pursuit / Evasion
, 2008
"... Using concepts from both robotics and graph theory, we formulate the problem of indoor pursuit / evasion in terms of searching a graph for a mobile evader. We present an offline, greedy, iterative algorithm which performs guaranteed search, i.e. no matter how the evader moves, it will eventually be ..."
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Cited by 9 (2 self)
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Using concepts from both robotics and graph theory, we formulate the problem of indoor pursuit / evasion in terms of searching a graph for a mobile evader. We present an offline, greedy, iterative algorithm which performs guaranteed search, i.e. no matter how the evader moves, it will eventually be captured; in fact our algorithm can be applied to either an adversarial (actively trying to avoid capture) or randomly moving evader. Furthermore the algorithm produces an internal search (the searchers move only along the edges of the graph, “teleporting” is not used) and can accommodate “extended” (across nodes) visibility and finite or infinite evader speed. We present search experiments for several indoor environments, some of them quite complicated, in all of which the algorithm succeeds in clearing the graph (i.e. capturing the evader).
Efficient, Guaranteed Search with MultiAgent Teams
"... Abstract — Here we present an anytime algorithm for clearing an environment using multiple searchers. Prior methods in the literature treat multiagent search as either a worstcase problem (i.e., clear an environment of an adversarial evader with potentially infinite speed), or an averagecase prob ..."
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Cited by 8 (2 self)
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Abstract — Here we present an anytime algorithm for clearing an environment using multiple searchers. Prior methods in the literature treat multiagent search as either a worstcase problem (i.e., clear an environment of an adversarial evader with potentially infinite speed), or an averagecase problem (i.e., minimize average capture time given a model of the target’s motion). We introduce an algorithm that combines finitehorizon planning with spanning tree traversal methods to generate plans that clear the environment of a worstcase adversarial target and have good averagecase performance considering a target motion model. Our algorithm is scalable to large teams of searchers and yields theoretically bounded averagecase performance. We have tested our proposed algorithm through a large number of experiments in simulation and with a team of robot and human searchers in an office building. Our combined search algorithm both clears the environment and reduces average capture times by up to 75 % when compared to a purely worstcase approach. I.
Anytime Guaranteed Search using Spanning Trees
, 2008
"... This technical report presents an anytime algorithm for solving the multirobot guaranteed search problem. Guaranteed search requires a team of robots to clear an environment of a potentially adversarial target. In other words, a team of searchers must generate a search strategy guaranteed to find a ..."
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Cited by 8 (2 self)
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This technical report presents an anytime algorithm for solving the multirobot guaranteed search problem. Guaranteed search requires a team of robots to clear an environment of a potentially adversarial target. In other words, a team of searchers must generate a search strategy guaranteed to find a target. This problem is known to be NPcomplete on arbitrary graphs but can be solved in lineartime on trees. Our proposed algorithm reduces an environment to a graph representation and then randomly generates a spanning tree of the graph. Guards are then placed on nodes in the graph to eliminate nontree edges, and a search strategy for the remaining tree is found using a lineartime algorithm from prior work. To reduce the number of guards, our algorithm takes advantage of the temporal characteristics of the search schedule to reuse guards no longer necessary at their previous locations. Many spanning trees are analyzed prior to search to determine the tree that yields the best search strategy. At any time, spanning tree generation can be stopped yielding the best strategy thus far. Our proposed algorithm is demonstrated on two complex graphs derived from physical environments, and several methods for generating candidate spanning trees are compared.
Combining search and action for mobile robots
 In Proc. int’l conf. robotics and automation
, 2009
"... Abstract — We explore the interconnection between search and action in the context of mobile robotics. The task of searching for an object and then performing some action with that object is important in many applications. Of particular interest to us is the idea of a robot assistant capable of perf ..."
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Abstract — We explore the interconnection between search and action in the context of mobile robotics. The task of searching for an object and then performing some action with that object is important in many applications. Of particular interest to us is the idea of a robot assistant capable of performing worthwhile tasks around the home and office (e.g., fetching coffee, washing dirty dishes, etc.). We prove that some tasks allow for search and action to be completely decoupled and solved separately, while other tasks require the problems to be analyzed together. We complement our theoretical results with the design of a combined search/action approximation algorithm that draws on prior work in search. We show the effectiveness of our algorithm by comparing it to stateoftheart solvers, and we give empirical evidence showing that search and action can be decoupled for some useful tasks. Finally, we demonstrate our algorithm on an autonomous mobile robot performing object search and delivery in an office environment. I.
Matching and fairness in threatbased mobile sensor coverage
 IEEE Trans. Mobile Computing
"... Abstract—Mobile sensors can be used to effect complete coverage of a surveillance area for a given threat over time, thereby reducing the number of sensors necessary. The surveillance area may have a given threat profile as determined by the kind of threat, and accompanying meteorological, environme ..."
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Abstract—Mobile sensors can be used to effect complete coverage of a surveillance area for a given threat over time, thereby reducing the number of sensors necessary. The surveillance area may have a given threat profile as determined by the kind of threat, and accompanying meteorological, environmental, and human factors. In planning the movement of sensors, areas that are deemed higher threat should receive proportionately higher coverage. We propose a coverage algorithm for mobile sensors to achieve a coverage that will match—over the long term and as quantified by an RMSE metric—a given threat profile. Moreover, the algorithm has the following desirable properties: 1) stochastic, so that it is robust to contingencies and makes it hard for an adversary to anticipate the sensor’s movement, 2) efficient, and 3) practical, by avoiding movement over inaccessible areas. Further to matching, we argue that a fairness measure of performance over the shorter time scale is also important. We show that the RMSE and fairness are, in general, antagonistic, and argue for the need of a combined measure of performance, which we call efficacy. We show how a pause time parameter of the coverage algorithm can be used to control the tradeoff between the RMSE and fairness, and present an efficient offline algorithm to determine the optimal pause time maximizing the efficacy. Finally, we discuss the effects of multiple sensors, under both independent and coordinated operation. Extensive simulation results—under realistic coverage scenarios—are presented for performance evaluation. Index Terms—Wireless sensor network, mobile application, distributed systems. Ç
Branch and Bound Strategies for Nonmaximal Suppression in Object Detection
"... Abstract. In this work, we are concerned with the detection of multiple objects in an image. We demonstrate that typically applied objectives have the structure of a random field model, but that the energies resulting from nonmaximal suppression terms lead to the maximization of a submodular functi ..."
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Cited by 4 (1 self)
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Abstract. In this work, we are concerned with the detection of multiple objects in an image. We demonstrate that typically applied objectives have the structure of a random field model, but that the energies resulting from nonmaximal suppression terms lead to the maximization of a submodular function. This is in general a difficult problem to solve, which is made worse by the very large size of the output space. We make use of an optimal approximation result for this form of problem by employing a greedy algorithm that finds one detection at a time. We show that we can adopt a branchandbound strategy that efficiently explores the space of all subwindows to optimally detect single objects while incorporating pairwise energies resulting from previous detections. This leads to a series of interrelated branchandbound optimizations, which we characterize by several new theoretical results. We then show empirically that optimal branchandbound efficiency gains can be achieved by a simple strategy of reusing priority queues from previous detections, resulting in speedups of up to a factor of three on the PASCAL VOC data set as compared with serial application of branchandbound. 1
1 The Eye and the Fist: Optimizing Search and Interdiction
"... Abstract. Interdiction operations involving search, identification, and interception of suspected objects are of great interest and high operational importance to military and naval forces as well as nation’s coast guards and border patrols. The interdiction scenario discussed in this paper includes ..."
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Cited by 3 (0 self)
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Abstract. Interdiction operations involving search, identification, and interception of suspected objects are of great interest and high operational importance to military and naval forces as well as nation’s coast guards and border patrols. The interdiction scenario discussed in this paper includes an area of interest with multiple neutral and hostile objects moving through this area, and an interdiction force, consisting of an airborne sensor and an intercepting surface vessel or ground vehicle, whose objectives are to search, identify, track, and intercept hostile objects within a given time frame. The main contributions of this paper are addressing both airborne sensor and surface vessel simultaneously, developing a stochastic dynamicprogramming model for optimizing their employment, and deriving operational insight. In addition, the search and identification process of the airborne sensor addresses both physical (appearance) and behavioral (movement pattern) signatures of a potentially hostile object. As the model is computationally intractable for realworld scenarios, we propose a simple heuristic policy, which is shown, using a bounding technique, to be quite effective. Based on a numerical case study of maritime interdiction operations, which includes several representative scenarios, we show that the expected number of intercepted hostile objects, following the heuristic decision policy, is at least 60 % of the number of hostile objects intercepted following an optimal decision policy. 1.