An incremental deployment algorithm for mobile robot teams

Howard, Andrew; Matarić, Maja J.; Sukhatme, Gaurav S.

doi:10.1109/irds.2002.1041702

Cited by 182 publications

(230 citation statements)

References 11 publications

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“…There are three overloaded nodes and five underloaded nodes. M [3,3] Figure 2, the node and edge weighted bipartite graph shows weights of all edges connecting M [3,3] to underloaded nodes.…”

Section: Hungarian Methodsmentioning

confidence: 99%

“…For example, M [3,3] has three clone nodes labeled from 1 to 3. The Hungarian method is then applied to Figure 3 (a) and the optimal result is shown in Figure 3 (b).…”

Section: Hungarian Methodsmentioning

confidence: 99%

“…In incremental sensor deployment in [3], nodes are deployed one by one, using the location information of previously deployed nodes to deploy the current one. This algorithm is not scalable and is computationally expensive.…”

Section: Movement-assisted Deploymentmentioning

confidence: 99%

“…Incremental self-deployment [3] incrementally deploys additional sensors, usually one- * This work was supported in part by NSF grants ANI 0073736, EIA 0130806, CCR 0329741, CNS 0422762, CNS 0434533, and CNS 0531410. Email: jie@cse.fau.edu, syang1@fau.edu.…”

Section: Introductionmentioning

confidence: 99%

“…(2) We propose an optimal load balancing solution based on the Hungarian method that achieves minimum total moving distance and the number of moves. (3) We extend the scan-based solution to reduce total moving distance without resorting to global information. (4) We present several further extensions and discuss various trade-offs among total moving distance, number of moves, and converging speed.…”

Section: Introductionmentioning

confidence: 99%

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Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks

Yang

2007

Simulation Modelling Practice and Theory

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Section: Hungarian Methodsmentioning

confidence: 99%

“…For example, M [3,3] has three clone nodes labeled from 1 to 3. The Hungarian method is then applied to Figure 3 (a) and the optimal result is shown in Figure 3 (b).…”

Section: Hungarian Methodsmentioning

confidence: 99%

Section: Movement-assisted Deploymentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks

Yang

2007

Simulation Modelling Practice and Theory

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Coverage and Connectivity Issues in Wireless Sensor Networks

Ghosh

Das

2005

Mobile, Wireless, and Sensor Networks

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Wireless sensor networks [33,34] have inspired tremendous research interest in since the mid-1990s. Advancement in wireless communication and microelectromechanical systems (MEMSs) have enabled the development of low-cost, lowpower, multifunctional, tiny sensor nodes that can sense the environment, perform data processing, and communicate with each other untethered over short distances. A typical wireless sensor network consists of thousands of sensor nodes, deployed either randomly or according to some predefined statistical distribution, over a geographic region of interest. A sensor node by itself has severe resource constraints, such as low battery power, limited signal processing, limited computation and communication capabilities, and a small amount of memory; hence it can sense only a limited portion of the environment. However, when a group of sensor nodes collaborate with each other, they can accomplish a much bigger task efficiently. One of the primary advantages of deploying a wireless sensor network is its low deployment cost and freedom from requiring a messy wired communication backbone, which is often infeasible or economically inconvenient.Wireless sensor networks ensure a wide range of applications [2], starting from security surveillance in military and battlefields, monitoring previously unobserved environmental phenomena, smart homes and offices, improved healthcare, industrial diagnosis, and many more. For instance, a sensor network can be deployed in a remote island for monitoring wildlife habitat and animal behavior [25], or near the crater of a volcano to measure temperature, pressure, and seismic activities. In many of these applications the environment can be hostile where human Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions

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Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

Shih¹,

Yeh

Hsu³

2012

Asian Journal of Control

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To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior-based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots. An unknown environment is dynamically divided into several sub-areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).

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An incremental deployment algorithm for mobile robot teams

Abstract: Abstract

Cited by 182 publications

References 11 publications

Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks

Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks

Coverage and Connectivity Issues in Wireless Sensor Networks

Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

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