Physical Path Planning Using the GNATs

O’Hara, Keith J.; Bigio, V.; Dodson, E.R.; Irani, Aref Jalili; Walker, D.; Balch, Tucker

doi:10.1109/robot.2005.1570201

Cited by 15 publications

(13 citation statements)

References 8 publications

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“…38,39 The problem can be addressed by introducing additional passive nodes such as GNATs, which support goal-directed navigation in a decentralized manner without the use of mapping or localization. 123 However, a drawback with this approach is that the swarm will be unable to operate without these additional nodes, which in turn decreases its flexibility.…”

Section: Mapping and Localizationmentioning

confidence: 99%

Swarm robotics reviewed

2012

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SUMMARYWe present a review of recent activities in swarm robotic research, and analyse existing literature in the field to determine how to get closer to a practical swarm robotic system for real world applications. We begin with a discussion of the importance of swarm robotics by illustrating the wide applicability of robot swarms in various tasks. Then a brief overview of various robotic devices that can be incorporated into swarm robotic systems is presented. We identify and describe the challenges that should be resolved when designing swarm robotic systems for real world applications. Finally, we provide a summary of a series of issues that should be addressed to overcome these challenges, and propose directions for future swarm robotic research based on our extensive analysis of the reviewed literature.

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Section: Mapping and Localizationmentioning

confidence: 99%

Swarm robotics reviewed

2012

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“…In the robotics and sensor networking communities this area has burgeoned recently. Examples include investigations on sensor-network guided robot localization [10], sensor-network guided robot navigation [2,4,19,28] and exploration [22], robot-assisted sensor network localization [10], robotassisted sensor network deployment [9,15,22] and sensornetwork guided robot pursuit-evasion [27].…”

Section: Related Workmentioning

confidence: 99%

Scalable and practical pursuit-evasion with networked robots

Vieira

Govindan

Sukhatme

2009

Intel Serv Robotics

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In this paper, we consider the design and implementation of practical pursuit-evasion games with networked robots, where a communication network provides sensing-at-a-distance as well as a communication backbone that enables tighter coordination between pursuers. We first develop, using the theory of zero-sum games, an algorithm that computes the minimal completion time strategy for pursuit-evasion when pursuers and evaders have same speed, and when all players make optimal decisions based on complete knowledge. Then, we extend this algorithm to when evader Electronic supplementary material The online version of this article (are significantly faster than pursuers. Unfortunately, these algorithms do not scale beyond a small number of robots. To overcome this problem, we design and implement a partition algorithm where pursuers capture evaders by decomposing the game into multiple multi-pursuer single-evader games. We show that the partition algorithm terminates, has bounded capture time, is robust, and is scalable in the number of robots. We then describe the design of a real-world mobile robot-based pursuit evasion game. We validate our algorithms by experiments in a moderate-scale testbed in a challenging office environment. Overall, our work illustrates an innovative interplay between robotics and communication.

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“…The use of a pre-deployed embedded network to assist the navigation of a robot in the environment has been extensively studied by O'Hara et al [5][6][7][8]. In particular, the authors use small and relatively inexpensive embedded network platforms, the GNATs, to guide the navigation of a LEGO Mindstorm/RCX robot using infrared transmitter and receivers.…”

Section: On-line Algorithmsmentioning

confidence: 99%

“…The feasibility of the approach in real-time systems has been confirmed in Batalin and Sukhatme [1][2][3][4], who used radio beacons to guide the navigation of robots and assist them in the coverage of an unknown terrain, and in O'Hara et al [5][6][7][8], who deployed an extensive test-bed of small sensors (GNATs) to guide the navigation of a LEGO Mindstorm/RCX robot using infrared transmitters and receivers. The goal of the present work is therefore to provide a more sophisticated and better performing exploration algorithm to be used by the agents.…”

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confidence: 99%

Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes

Ferranti

Trigoni

Levene

2008

Auton Agent Multi-Agent Syst

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When an emergency occurs within a building, it may be initially safer to send autonomous mobile nodes, instead of human responders, to explore the area and identify hazards and victims. Exploring all the area in the minimum amount of time and reporting back interesting findings to the human personnel outside the building is an essential part of rescue operations. Our assumptions are that the area map is unknown, there is no existing network infrastructure, long-range wireless communication is unreliable and nodes are not locationaware. We take into account these limitations, and propose an architecture consisting of both mobile nodes (robots, called agents) and stationary nodes (inexpensive smart devices, called tags). As agents enter the emergency area, they sprinkle tags within the space to label the environment with states. By reading and updating the state of the local tags, agents are able to coordinate indirectly with each other, without relying on direct agent-to-agent communication. In addition, tags wirelessly exchange local information with nearby tags to further assist agents in their exploration task. Our simulation results show that the proposed algorithm, which exploits both tag-to-tag and agent-to-tag communication, outperforms previous algorithms that rely only on agent-to-tag communication.

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Physical Path Planning Using the GNATs

Cited by 15 publications

References 8 publications

Swarm robotics reviewed

Swarm robotics reviewed

Scalable and practical pursuit-evasion with networked robots

Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes

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