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94
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 (15 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
A PointBased POMDP Planner for Target Tracking
"... Abstract — Target tracking has two variants that are often studied independently with different approaches: target searching requires a robot to find a target initially not visible, and target following requires a robot to maintain visibility on a target initially visible. In this work, we use a par ..."
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Cited by 25 (7 self)
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Abstract — Target tracking has two variants that are often studied independently with different approaches: target searching requires a robot to find a target initially not visible, and target following requires a robot to maintain visibility on a target initially visible. In this work, we use a partially observable Markov decision process (POMDP) to build a single model that unifies target searching and target following. The POMDP solution exhibits interesting tracking behaviors, such as anticipatory moves that exploit target dynamics, informationgathering moves that reduce target position uncertainty, and energyconserving actions that allow the target to get out of sight, but do not compromise longterm tracking performance. To overcome the high computational complexity of solving POMDPs, we have developed SARSOP, a new pointbased POMDP algorithm based on successively approximating the space reachable under optimal policies. Experimental results show that SARSOP is competitive with the fastest existing pointbased algorithm on many standard test problems and faster by many times on some. I.
Parallel stochastic hillclimbing with small teams, in L.E.Parker et al., eds
 MultiRobot Systems: From Swarms to Intelligent Automata, Volume III’, Springer, the Netherlands
, 2005
"... Abstract We address the basic problem of coordinating the actions of multiple robots that are working toward a common goal. This kind of problem is NPhard, because in order to coordinate a system of n robots, it is in principle necessary to generate and evaluate a number of actions or plans that is ..."
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Cited by 21 (2 self)
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Abstract We address the basic problem of coordinating the actions of multiple robots that are working toward a common goal. This kind of problem is NPhard, because in order to coordinate a system of n robots, it is in principle necessary to generate and evaluate a number of actions or plans that is exponential in n (assuming P � = NP). However, we suggest that many instances of coordination problems, despite the NPhardness of the overall class of problems, do not in practice require exponential computation in order to arrive at good solutions. In such problems, it is not necessary to consider all possible actions of the n robots; instead an algorithm may restrict its attention to interactions within small teams, and still produce highquality solutions. We use this insight in the development of a novel coordination algorithm that we call parallel stochastic hillclimbing with small teams, or Parish. This algorithm is designed specifically for use in multirobot systems: it can run offline or online, is easily distributed across multiple machines, and is efficient with regard to communication. We state and analyze the Parish algorithm present results from the implementation and application of the algorithm for a concrete problem: multirobot pursuitevasion. In this demanding domain, a team of robots must coordinate their actions so as to guarantee location of a skilled evader. 1 2
Multirobot surveillance: an improved algorithm for the graphclear problem
 In Proc. IEEE Intl. Conf. on Robotics and Automation
, 2008
"... Abstract—The main contribution of this paper is an improved algorithm for the GRAPHCLEAR problem, a novel NPcomplete graph theoretic problem we recently introduced as a tool to model multirobot surveillance tasks. The proposed algorithm combines two previously developed solving techniques and p ..."
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Cited by 20 (6 self)
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Abstract—The main contribution of this paper is an improved algorithm for the GRAPHCLEAR problem, a novel NPcomplete graph theoretic problem we recently introduced as a tool to model multirobot surveillance tasks. The proposed algorithm combines two previously developed solving techniques and produces strategies that require less robots to be executed. We provide a theoretical framework useful to identify the conditions for the existence of an optimal solution under special circumstances, and a set of mathematical tools characterizing the problem being studied. Finally we also identify a set of open questions deserving more investigations. I.
Bitbots: Simple robots solving complex tasks
 In AAAI National Conference on Artificial Intelligence
, 2005
"... Sensing uncertainty is a central issue in robotics. Sensor limitations often prevent accurate state estimation, and robots find themselves confronted with a complicated information (belief) space. In this paper we define and characterize the information spaces of very simple robots, called Bitbots ..."
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Cited by 17 (8 self)
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Sensing uncertainty is a central issue in robotics. Sensor limitations often prevent accurate state estimation, and robots find themselves confronted with a complicated information (belief) space. In this paper we define and characterize the information spaces of very simple robots, called Bitbots, which have severe sensor limitations. While complete estimation of the robot’s state is impossible, careful consideration and management of the uncertainty is presented as a search in the information space. We show that these simple robots can solve several challenging online problems, even though they can neither obtain a complete map of their environment nor exactly localize themselves. However, when placed in an unknown environment, Bitbots can build a topological representation of it and then perform pursuitevasion (i.e., locate all moving targets inside this environment). This paper introduces Bitbots, and provides both theoretical analysis of their information spaces and simulation results.
S.: The graphclear problem: definition, theoretical properties and its connections to multirobot aided surveillance
 In: Intelligent Robots and Systems, 2007. IROS
"... Abstract — In this paper we present a novel graph theoretic problem, called GRAPHCLEAR, useful to model surveillance tasks where multiple robots are used to detect all possible intruders in a given indoor environment. We provide a formal definition of the problem and we investigate its basic theore ..."
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Cited by 16 (6 self)
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Abstract — In this paper we present a novel graph theoretic problem, called GRAPHCLEAR, useful to model surveillance tasks where multiple robots are used to detect all possible intruders in a given indoor environment. We provide a formal definition of the problem and we investigate its basic theoretical properties, showing that the problem is NPcomplete. We then present an algorithm to compute a strategy for the restriction of the problem to trees and present a method how to use this solution in applications. The method is then tested in simple simulations. GRAPHCLEAR is useful to describe multirobot pursuit evasion games when robots have limited sensing capabilities, i.e. multiple agents are needed to perform basic patrolling operations. I.
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.
Optimal search for multiple targets in a built environment
 Proc. IEE/RSJ Int. Conf. on Intelligent Robots and Systems
, 2005
"... Abstract – The main contribution of this paper is an algorithm for autonomous search that minimizes the expected time for detecting multiple targets present in a known built environment. The proposed technique makes use of the probability distribution of the target(s) in the environment, thereby mak ..."
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Cited by 13 (2 self)
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Abstract – The main contribution of this paper is an algorithm for autonomous search that minimizes the expected time for detecting multiple targets present in a known built environment. The proposed technique makes use of the probability distribution of the target(s) in the environment, thereby making it feasible to incorporate any additional information, known apriori or acquired while the search is taking place, into the search strategy. The environment is divided into a set of distinct regions and an adjacency matrix is used to describe the connections between them. The costs of searching any of the regions as well as the cost of travel between them can be arbitrarily specified. The search strategy is derived using a dynamic programming algorithm. The effectiveness of the algorithm is illustrated using an example based on the search of an office environment. An analysis of the computational complexity is also presented. Index Terms – Multiple targets, target search, dynamic programming, topological map, probability distribution
Efficient Planning under Uncertainty for a TargetTracking MicroAerial Vehicle
"... Ahelicopteragenthastoplantrajectoriestotrack multiple ground targets from the air. The agent has partial information of each target’s pose, and must reason about its uncertainty of the targets’ poses when planning subsequent actions. We present an online, forwardsearch algorithm for planning under ..."
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Cited by 12 (1 self)
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Ahelicopteragenthastoplantrajectoriestotrack multiple ground targets from the air. The agent has partial information of each target’s pose, and must reason about its uncertainty of the targets’ poses when planning subsequent actions. We present an online, forwardsearch algorithm for planning under uncertainty by representing the agent’s belief of each target’s pose as a multimodal Gaussian belief. We exploit this parametric belief representation to directly compute the distribution of posterior beliefs after actions are taken. This analytic computation not only enables us to plan in problems with continuous observation spaces, but also allows the agent to search deeper by considering policies composed of multistep action sequences; deeper searches better enable the agent to keep the targets welllocalized. We present experimental results in simulation, as well as demonstrate the algorithm on an actual quadrotor helicopter tracking multiple vehicles on a road network constructed indoors.
Probabilistic strategies for pursuit in cluttered environments with multiple robots
 In Proc. Int’l Conf. on Robotics and Automation
, 2007
"... Abstract — In this paper, we describe a method for coordinating multiple robots in a pursuitevasion domain. We examine the problem of multiple robotic pursuers attempting to locate a nonadversarial mobile evader in an indoor environment. Unlike many other approaches to this problem, our method see ..."
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Cited by 12 (6 self)
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Abstract — In this paper, we describe a method for coordinating multiple robots in a pursuitevasion domain. We examine the problem of multiple robotic pursuers attempting to locate a nonadversarial mobile evader in an indoor environment. Unlike many other approaches to this problem, our method seeks to minimize expected time of capture rather than guaranteeing capture. This allows us to examine the performance of our algorithm in complex and cluttered environments where guaranteed capture is difficult or impossible with limited pursuers. We present a probabilistic formulation of the problem, discretize the environment, and define cost heuristics for use in planning. We then propose a scalable algorithm using an entropy cost heuristic that searches possible movement paths to determine coordination strategies for the robotic pursuers. We present simulated results describing the performance of our algorithm against state of the art alternatives in a complex office environment. Our algorithm successfully reduces capture time with limited pursuers in an environment beyond the scope of many other approaches. I.