A probabilistic approach to coordinated multi-robot indoor surveillance

Moors, Mark; Röhling, Timo; Schulz, Dirk

doi:10.1109/iros.2005.1545038

Cited by 32 publications

(22 citation statements)

References 7 publications

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“…Moors et al [4] presented a graph-based algorithm for coordinate multi-robot motion planning in 2D indoor environments. The scenario of this research is multi-robot indoor surveillance.…”

Section: Related Worǩmentioning

confidence: 99%

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Yan

Jouandeau

Chérif

2013

Advances in Intelligent Systems and Computing

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In this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning.Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches.

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“…Moors et al [4] presented a graph-based algorithm for coordinate multi-robot motion planning in 2D indoor environments. The scenario of this research is multi-robot indoor surveillance.…”

Section: Related Worǩmentioning

confidence: 99%

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Yan

Jouandeau

Chérif

2013

Advances in Intelligent Systems and Computing

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“…In [31] more complicated environments are considered and heuristics for distributing the robots across parts of the environments are developed. For the probabilistic detection of intruders there are heuristics developed by Moors et al in [32]. The authors create a graph by randomly placing vertices in the environment until all parts are covered, assuming that a vertex covers the area a sensor covers at the same location.…”

Section: Related Workmentioning

confidence: 99%

Pursuit-Evasion on Trees by Robot Teams

Kolling

Carpin

2010

IEEE Trans. Robot.

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Abstract-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.

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“…The environment in which the system is tested is fairly open and several heuristic policies for moving the robots are tested in real experiments. A similar probabilistic approach is developed in [10]. Therein the target moves according to a worst-case Markov process.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Graph-Clear

Kolling

Carpin

2009

2009 IEEE International Conference on Robotics and Automation

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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.

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A probabilistic approach to coordinated multi-robot indoor surveillance

Cited by 32 publications

References 7 publications

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Pursuit-Evasion on Trees by Robot Teams

Probabilistic Graph-Clear

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