Simultaneous learning of motion and sensor model parameters for mobile robots

Yap, Teddy N.; Shelton, Christian R.

doi:10.1109/robot.2008.4543515

Cited by 4 publications

(5 citation statements)

References 22 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A probabilistic motion model can better describe the movements of sensor nodes in real world scenarios. We use the probabilistic motion model of sensor nodes given in [58]. In this probabilistic motion model, movements of a sensor node s i for a given drive ( ( ) i d n ) and turn ( ( ) i r n ) command is described using the following equations: (14) ( ) …”

Section: -7 Experimentsmentioning

confidence: 99%

A cellular learning automata-based deployment strategy for mobile wireless sensor networks

Esnaashari

Meybodi

2011

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

Abstract:One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automata-based deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPS-based devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.

show abstract

Section: -7 Experimentsmentioning

confidence: 99%

A cellular learning automata-based deployment strategy for mobile wireless sensor networks

Esnaashari

Meybodi

2011

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

show abstract

“…A probabilistic motion model can better describe the movements of sensor nodes in real world scenarios. We use the probabilistic motion model of sensor nodes given in [98]. In this probabilistic motion model, movements of a sensor node s i for a given drive (d i (n)) and turn (r i (n)) command is described using the following equations:…”

Section: Methodsmentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

View full text Add to dashboard Cite

Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

show abstract

“…There is far too much work in this area to properly mention here; however the approaches of [14] and [15] deserve mention through their use of an EM approach to learning noise parameters of odometry models (similar to the approach described in Section IV).…”

Section: Localization To Prior Datamentioning

confidence: 99%

Monte Carlo Localization and registration to prior data for outdoor navigation

Silver

Stentz

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-GPS has become the de facto standard for obtaining a global position estimate during outdoor autonomous navigation. However, GPS can become degraded due to occlusion or interference, to the detriment of autonomous performance. In addition, GPS positions must be aligned with prior data, a tedious and continual process. This work presents a solution to these two problems based on learning generic observation models in the presence of GPS to use in its absence. The models are non-parametric and compared to traditional approaches require few assumptions about either the prior data available or a robot's onboard sensors. Along with allowing for localization to prior data under GPS-denied conditions, this learning approach can be coupled with an EM procedure to automatically register GPS and prior data positions. Experimental results are presented based on data from more than 15 km of autonomous navigation through challenging outdoor terrain.

show abstract

Simultaneous learning of motion and sensor model parameters for mobile robots

Cited by 4 publications

References 22 publications

A cellular learning automata-based deployment strategy for mobile wireless sensor networks

A cellular learning automata-based deployment strategy for mobile wireless sensor networks

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Monte Carlo Localization and registration to prior data for outdoor navigation

Contact Info

Product

Resources

About