Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

Larson, A.C.; Demir, Güleser Kalaycı; Voyles, Richard M.

doi:10.1007/s10514-005-0605-5

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…In such operations, determining the environment of the vehicle improves its efficiency. Estimation of the current terrain type can be useful since the terrain conditions affect both planning and motion control stages [ 2 ]. An automated terrain-dependent driving process can be obtained with terrain classification.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

Kurban

Beşdok

2009

Sensors

125

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This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others.

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Section: Introductionmentioning

confidence: 99%

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

Kurban

Beşdok

2009

Sensors

125

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“…Larson et al use a single camera and measure the "gait bounce" to determine characteristics of the terrain for legged robots [8]. Vandapel et al use LIDAR to classify safe and unsafe driving areas [9].…”

Section: Related Workmentioning

confidence: 99%

Surface classification for sensor deployment from UAV landings

Anthony

Basha

Ostdiek

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Using Unmanned Aerial Vehicles (UAVs) to deploy sensor networks promises an autonomous and useful method of installation in remote or hard to access locations. Some sensors, such as soil moisture sensors, must be physically installed in soft soil, yet UAVs cannot easily determine soil softness with remote sensors. In this paper, we use data from an onboard accelerometer measured during UAV landings to determine the softness of the ground. We collect and analyze over 200 data sets gathered from 8 different materials: foam, carpet, wood, tile, grass, dirt, concrete, and woodchips. Based on this analysis, we examine a number of features from the accelerometer and four classification algorithms: LDA, QDA, SVM, and binary decision trees. The decision tree performs well and is simple to implement onboard the UAV. We implement this in our UAV control system and perform experiments to verify that the UAV can accurately classify the softness of the surface with 90% accuracy. This lays the groundwork for our future work on developing a UAV capable of installing sensors in soft soil.

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“…For instance, Larson et al (2005) analysis the terrain roughness by means of spatial discrimination which then is (meta-) classified. Seraji and Howard (2002) assess the navigation strategy with the terrain's features of roughness, slopes and discontinuity.…”

Section: Terrain Roughness Recognitionmentioning

confidence: 99%

Wheeled-Robot Navigation With Velocity Updating on Rough Terrains

García

Alvarado

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

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Abstract:For navigation on outdoor surfaces, usually having different kind of roughness and soft irregularities, this paper proposal is that a wheeled robot combines the gradient method for path planning, alongside it adjusts velocity based on a multi-layer fuzzy neural network; the network integrates information about the roughness and the soft slopes of the terrain to compute the navigation velocity. The implementation is simple and computationally low-cost. The experimental tests show the advantage in the performance of the robot by varying the velocity depending on the terrain features.

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Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

Cited by 7 publications

References 25 publications

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

Surface classification for sensor deployment from UAV landings

Wheeled-Robot Navigation With Velocity Updating on Rough Terrains

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