Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

open access articleAutonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%

Determination of forest road surface roughness by kinect depth imaging

Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities. Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect\u2122 depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness. The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles

Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

A Near-to-Far Learning Framework for Terrain Characterization Using an Aerial/Ground-Vehicle Team

In this thesis, a novel framework for adaptive terrain characterization of untraversed far terrain in a natural outdoor setting is presented. The system learns the association between visual appearance of different terrain and the proprioceptive characteristics of that terrain in a self-supervised framework. The proprioceptive characteristics of the terrain are acquired by inertial sensors recording measurements of one second traversals that are mapped into the frequency domain and later through a clustering technique classified into discrete proprioceptive classes. Later, these labels are used as training inputs to the adaptive visual classifier. The visual classifier uses images captured by an aerial vehicle scouting ahead of the ground vehicle and extracts local and global descriptors from image patches. An incremental SVM is utilized on the set of images and training sets as they are grabbed sequentially. The framework proposed in this thesis has been experimentally validated in an outdoor environment. We compare the results of the adaptive approach with the offline a priori classification approach and yield an average 12% increase in accuracy results on outdoor settings. The adaptive classifier gradually learns the association between characteristics and visual features of new terrain interactions and modifies the decision boundaries

Road terrain type classification based on laser measurement system data

For road vehicles, knowledge of terrain types is useful in improving passenger safety and comfort. The conventional methods are susceptible to vehicle speed variations and in this paper we present a method of using Laser Measurement System (LMS) data for speed independent road type classification. Experiments were carried out with an instrumented road vehicle (CRUISE), by manually driving on a variety of road terrain types namely Asphalt, Concrete, Grass, and Gravel roads at different speeds. A looking down LMS is used for capturing the terrain data. The range data is capable of capturing the structural differences while the remission values are used to observe anomalies in surface reflectance properties. Both measurements are combined and used in a Support Vector Machines Classifier to achieve an average accuracy of 95% on different road types

The Commonality of Earthquake and Wind Analysis

Earthquakes and wind loadings constitute dynamic effects that often must be considered in the design of buildings and structures. The primary purpose of this research study was to investigate the common features of general dynamic analysis procedures employed for evaluating the effects of wind and earthquake excitation. Another major goal was to investigate and develop a basis for generating response spectra for wind loading, which in turn would permit the use of modal analysis techniques for wind analysis in a manner similar to that employed for earthquake engineering. In order to generate wind response spectra, the wind loading is divided into two parts, a mean load treated as a static component and a fluctuating load treated as a dynamic component. The spectral representation of the wind loading constitutes a simple procedure for estimating the forces associated with the dynamic component of the gusting wind. Several illustrative examples are presented demonstrating the commonality.National Science Foundation Grants ENV 75-08456 and ENV 77-0719

Detection of Communities within the Multibody System Dynamics Network and Analysis of Their Relations

Multibody system dynamics is already a well developed branch of theoretical, computational and applied mechanics. Thousands of documents can be found in any of the well-known scientific databases. In this work it is demonstrated that multibody system dynamics is built of many thematic communities. Using the Elsevier’s abstract and citation database SCOPUS, a massive amount of data is collected and analyzed with the use of the open source visualization tool Gephi. The information is represented as a large set of nodes with connections to study their graphical distribution and explore geometry and symmetries. A randomized radial symmetry is found in the graphical representation of the collected information. Furthermore, the concept of modularity is used to demonstrate that community structures are present in the field of multibody system dynamics. In particular, twenty-four different thematic communities have been identified. The scientific production of each community is analyzed, which allows to predict its growing rate in the next years. The journals and conference proceedings mainly used by the authors belonging to the community as well as the cooperation between them by country are also analyzed

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds