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18
Pursuit-Evasion on Trees by Robot Teams
, 2010
"... We present Graph-Clear, a novel pursuit-evasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges respectively. A sweep action ..."
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Cited by 25 (4 self)
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We present Graph-Clear, a novel pursuit-evasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges respectively. A sweep action detects intruders in a vertex and represents the capability of the robot team to detect intruders in the region associated to the vertex. Similarly, a block action prevents intruders from crossing an edge and represents the capability to detect intruders as they move between regions. Both actions may require multiple robots to be executed. A strategy is a sequence of block and sweep actions detecting all intruders. When solving instances of Graph-Clear the goal is to determine optimal strategies, i.e. strategies using the least number of robots. We prove that for the general case of graphs the problem of computing optimal strategies is NP-hard. Next, for the special case of trees we provide a polynomial time algorithm. The algorithm ensures that throughout the execution of the strategy all cleared vertices form a connected subtree, and we show it produces optimal strategies.
MSP Algorithm: Multi-Robot Patrolling based on Territory Allocation using Balanced Graph Partitioning
"... This article addresses the problem of efficient multi-robot patrolling in a known environment. The proposed approach assigns regions to each mobile agent. Every region is represented by a subgraph extracted from the topological representation of the global environment. A new algorithm is proposed in ..."
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Cited by 14 (10 self)
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This article addresses the problem of efficient multi-robot patrolling in a known environment. The proposed approach assigns regions to each mobile agent. Every region is represented by a subgraph extracted from the topological representation of the global environment. A new algorithm is proposed in order to deal with the local patrolling task assigned for each robot, named Multilevel Subgraph Patrolling (MSP) Algorithm. It handles some major graph theory classic problems like graph partitioning, Hamilton cycles, non-Hamilton cycles and longest path searches. The flexible, scalable, robust and high performance nature of this approach is testified by simulation results.
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).
Probabilistic Graph-Clear
- In Proceedings of the IEEE International Conference on Robotics and Automation
, 2009
"... Abstract — This paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. Sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. The ..."
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Cited by 6 (3 self)
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Abstract — This paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. Sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. The model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as Graph-Clear. This extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how many robots are needed to obtain a certain confidence that the environment is free from intruders, and 2) given a certain number of robots, how should they coordinate their actions to minimize their failure rate. I.
Hierarchical Visibility for Guaranteed Search in Large-Scale Outdoor Terrain
- AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
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GSST: anytime guaranteed search
, 2010
"... ... Trees (GSST), an anytime algorithm for multi-robot search. The problem is as follows: clear the environment of any adversarial target using the fewest number of searchers. This problem is NP-hard on arbitrary graphs but can be solved in linear-time on trees. Our algorithm generates spanning tree ..."
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Cited by 4 (1 self)
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... Trees (GSST), an anytime algorithm for multi-robot search. The problem is as follows: clear the environment of any adversarial target using the fewest number of searchers. This problem is NP-hard on arbitrary graphs but can be solved in linear-time on trees. Our algorithm generates spanning trees of a graphical representation of the environment to guide the search. At any time, spanning tree generation can be stopped yielding the best strategy so far. The resulting strategy can be modified online if additional information becomes available. Though GSST does not have performance guarantees after its first iteration, we prove that several variations will find an optimal solution given sufficient runtime. We test GSST in simulation and on a human-robot search team using a distributed implementation. GSST quickly generates clearing schedules with as few as 50 % of the searchers used by competing algorithms.
Moving game theoretical patrolling strategies from theory to practice: An usarsim simulation
- In Proceedings of the 27th international conference on robotics and automation (ICRA 2010
, 2010
"... Abstract — Game theoretical approaches have been recently used to develop patrolling strategies for mobile robots. The idea is that the patroller and the intruder play a game, whose outcome depends on the combination of their actions. From the analysis of this game, an optimal strategy for the patro ..."
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Cited by 3 (0 self)
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Abstract — Game theoretical approaches have been recently used to develop patrolling strategies for mobile robots. The idea is that the patroller and the intruder play a game, whose outcome depends on the combination of their actions. From the analysis of this game, an optimal strategy for the patrolling robot can be derived. Although game theoretical approaches are promising, their applicability in real settings is still an open problem. In this paper, we experimentally evaluate the practical applicability of the most general game theoretical approach for patrolling strategies, called BGA model. Experiments are conducted by using USARSim, with the goal of studying the behavior of the optimal patrolling strategy returned by the BGA model both in situations that violate its idealized assumptions and in comparison with other patrolling strategies that can be developed with much less computational effort. I.
Deployment of swarms of micro-aerial vehicles: from theory to practice
- In Proceedings of the IEEE International Conference on Robotics and Automation
, 2014
"... Abstract—We study the problem of deploying a high number of low-cost, low-complexity robots inside a known environment with the objective that at least one robotic platform reaches each of N preassigned goal locations. Our study is inspired by SensorFly, a micro-aerial vehicle successfully used for ..."
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Cited by 3 (2 self)
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Abstract—We study the problem of deploying a high number of low-cost, low-complexity robots inside a known environment with the objective that at least one robotic platform reaches each of N preassigned goal locations. Our study is inspired by SensorFly, a micro-aerial vehicle successfully used for mobile sensor network applications. SensorFly nodes feature limited on-board sensors, so one has to rely on simple navigation strategies and increase performance through redundance in the team. We introduce a simple, fully scalable deployment algorithm exploiting the limited capabilities offered by the SensorFly platform, and we explore its performance by feeding the simulation system with parameters extracted from the real SensorFly platform. I.
Theoretical Foundations of High-Speed Robot Team Deployment
"... Abstract—In this paper we study the multi-robot de-ployment problem under hard temporal constraints. After proposing a model for this task, we consider the simplest deployment algorithm and we analyze the relationship between three fundamental parameters, the temporal dead-line, the probability of s ..."
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Cited by 3 (3 self)
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Abstract—In this paper we study the multi-robot de-ployment problem under hard temporal constraints. After proposing a model for this task, we consider the simplest deployment algorithm and we analyze the relationship between three fundamental parameters, the temporal dead-line, the probability of success, and the number of robots. Because an exact analysis of even the simplest algorithm is computationally intractable, we derive an approximate bound leading to performance curves useful to answer design questions (how many robots are needed to get a certain performance guarantee?) or analysis questions (what is the probability of success given a certain deadline and number of robots?) Simulations show that the bounds are sharp and provide a useful tool to predict team deployment performance and tradeoffs. I.
