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93
Efficient multi-robot search for a moving target
- Int. J. Robotics Research
, 2009
"... This paper examines the problem of locating a mobile, non-adversarial 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, non-adversarial 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 multi-robot efficient search path planning (MESPP) problem. Such path planning prob-lems are NP-hard, and optimal solutions typically scale exponen-tially in the number of searchers. We present an approximation al-gorithm that utilizes finite-horizon planning and implicit coordina-tion to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a non-decreasing, submodular objective function, which leads to theoreti-cal bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy non-line-of-sight 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 large-scale simulated environments, and we further validate our results using data from a novel ultra-wideband ranging sensor. Finally, we provide an analysis that demonstrates the rela-
A point-based POMDP planner for target tracking
- in Proc. ICRA
, 2008
"... 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 part ..."
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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 energy-conserving actions that allow the target to get out of sight, but do not compromise long-term tracking performance. To overcome the high computational complexity of solving POMDPs, we have developed SARSOP, a new point-based 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.
Multi-robot surveillance: an improved algorithm for the graph-clear problem
- In Proc. IEEE Intl. Conf. on Robotics and Automation
, 2008
"... Abstract—The main contribution of this paper is an im-proved algorithm for the GRAPH-CLEAR problem, a novel NP-complete graph theoretic problem we recently introduced as a tool to model multi-robot surveillance tasks. The proposed al-gorithm 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 im-proved algorithm for the GRAPH-CLEAR problem, a novel NP-complete graph theoretic problem we recently introduced as a tool to model multi-robot surveillance tasks. The proposed al-gorithm 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.
Parallel stochastic hill-climbing with small teams, in L.E.Parker et al., eds
- Multi-Robot 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 NP-hard, 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 18 (1 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 NP-hard, 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 NP-hardness 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 high-quality solutions. We use this insight in the development of a novel coordination algorithm that we call parallel stochastic hill-climbing with small teams, or Parish. This algorithm is designed specifically for use in multi-robot systems: it can run off-line or on-line, 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: multi-robot pursuit-evasion. In this demanding domain, a team of robots must coordinate their actions so as to guarantee location of a skilled evader. 1 2
S.: The graph-clear 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 GRAPH-CLEAR, 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 17 (6 self)
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Abstract — In this paper we present a novel graph theoretic problem, called GRAPH-CLEAR, 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 NP-complete. 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. GRAPH-CLEAR 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 multi-robot teams: those that require constant complex interaction between teammates. Much research has been done in the area of multi-robot 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 multi-robot teams: those that require constant complex interaction between teammates. Much research has been done in the area of multi-robot 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 market-based 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.
Bitbots: Simple robots solving complex tasks
- In AAAI National Conference on Artificial Intelligence
, 2005
"... Sensing uncertainty is a central issue in robotics. Sen-sor limitations often prevent accurate state estimation, and robots find themselves confronted with a compli-cated 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 16 (7 self)
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Sensing uncertainty is a central issue in robotics. Sen-sor limitations often prevent accurate state estimation, and robots find themselves confronted with a compli-cated information (belief) space. In this paper we define and characterize the information spaces of very simple robots, called Bitbots, which have severe sensor limi-tations. While complete estimation of the robot’s state is impossible, careful consideration and management of the uncertainty is presented as a search in the informa-tion 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 topo-logical representation of it and then perform pursuit-evasion (i.e., locate all moving targets inside this en-vironment). This paper introduces Bitbots, and provides both theoretical analysis of their information spaces and simulation results.
Efficient Planning under Uncertainty for a Target-Tracking Micro-Aerial 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, forward-search algorithm for planning under ..."
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Cited by 13 (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, forward-search 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 multi-step 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.
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 11 (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 a-priori 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
Cooperative observation of multiple moving targets: An algorithm and its formalization
- Int. J. Robot. Res
, 2007
"... This paper presents a distributed control algorithm for multi-target surveillance by multiple robots. Robots equipped with sensors and communication devices discover and track as many evasive targets as possible in an open region. The algorithm utilizes information from sensors, communication, and a ..."
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Cited by 11 (4 self)
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This paper presents a distributed control algorithm for multi-target surveillance by multiple robots. Robots equipped with sensors and communication devices discover and track as many evasive targets as possible in an open region. The algorithm utilizes information from sensors, communication, and a mechanism to predict the minimum time before a robot loses a target. Workload is shared locally between robots using a greedy assignment of targets. Across long distances robots cooperate through explicit communication. The approach is coined Behavioral Cooperative Multi-robot Observation of Multiple Moving Targets. A formal representation of the proposed algorithm as well as proofs of performance guarantee are provided. Extensive simulations confirm the theoretical results in practice. 1