Distributed algorithms for guiding navigation across a sensor network

Li, Qun; Rosa, Michael De; Rus, Daniela

doi:10.1145/938985.939017

Cited by 210 publications

(220 citation statements)

References 26 publications

(18 reference statements)

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“…Our work builds on important previous work in sensor networks [8,11,14] and unmanned aerial vehicles [3,16]. It bridges the two communities by integrating autonomous control of flying vehicles with multi-hop message routing in ad-hoc networks.…”

Section: Related Workmentioning

confidence: 99%

Deployment and Connectivity Repair of a Sensor Net with a Flying Robot

Corke

Hrabar

Peterson

et al. 2006

Springer Tracts in Advanced Robotics

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Abstract. We consider multi-robot systems that include sensor nodes and aerial or ground robots networked together. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We present a sensor network deployment method using autonomous aerial vehicles and describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for repair, to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.)

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Section: Related Workmentioning

confidence: 99%

Deployment and Connectivity Repair of a Sensor Net with a Flying Robot

Corke

Hrabar

Peterson

et al. 2006

Springer Tracts in Advanced Robotics

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“…This finds the shortest path across the network and is equivalent to Dijkstra's algorithm [27]. The hop-counts create a gradient that can be followed forwards or backwards.…”

Section: B Swarm Navigation Behaviourmentioning

confidence: 99%

Indoor navigation with a swarm of flying robots

Stirling

Roberts

Zufferey

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or longrange communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments.

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“…Similarly, the emergency personnel often lack an overview of where people are located and which rooms are affected by hazards. This makes evacuation planning particularly difficult [1,2].…”

Section: Introductionmentioning

confidence: 99%

Escape planning in realistic fire scenarios with Ant Colony Optimisation

2014

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An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when predefined escape routes are blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape routes in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected personconsidering dynamic spread of fires, movability impairments caused by the hazards and faulty unreliable data. Special focus in this paper is on empirical tests for the proposed algorithms. This paper brings together the Ant Colony approach with a realistic fire dynamics simulator, and shows that the proposed solution is not only able to outperform comparable alternatives in static and dynamic environments, but also in environments with realistic spreading of fire and smoke causing fatalities. The aim of the solutions is usage by both individuals, such as from a personal smartphone of one of the evacuees, or for emergency personnel trying to assist large groups from remote locations.

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Distributed algorithms for guiding navigation across a sensor network

Cited by 210 publications

References 26 publications

Deployment and Connectivity Repair of a Sensor Net with a Flying Robot

Deployment and Connectivity Repair of a Sensor Net with a Flying Robot

Indoor navigation with a swarm of flying robots

Escape planning in realistic fire scenarios with Ant Colony Optimisation

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