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%

Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision

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

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

Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.Comment: To appear in the proceedings of the 18th International Symposium on Robotics Research (ISRR 2017

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

Rock type discrimination using Landsat-8 OLI satellite data in mafic-ultramafic terrain

The mafic-ultramafic terrain of the Bhavani complex in southern India is considered for lithological mapping. The Landsat-8 OLI satellite data was used for the interpretation of different rock types in the study area. The satellite data were digitally processed using ENVI 5.6 image processing software. In the OLI data, excluding bands 8 and 9, the remaining seven bands were used for the generation of colour composite images, band ratios, principal component analysis and SVM classification. Reflectance spectral measurements were carried out in laboratory conditions for five rock samples collected from the study area. The XRF analysis was carried out to estimate the composition of major oxides present in the rock samples. The results obtained from XRF analysis were compared with the rock spectra in characterizing the spectral features of the rock types. The colour composite images (B543, B567, B456, and B457), PCA composite image (PC312 and PC456), band ratios (BR5/5 and BR4/3), colour composite images from band ratios, and SVM classified output are useful in delineation various rock types in the terrain.  &nbsp

Real-Time odor classification through sequential bayesian filtering

The classification of volatiles substances with an e-nose is still a challenging problem, particularly when working under real-time, out-of-the-lab environmental conditions where the chaotic and highly dynamic characteristics of the gas transportation induce an almost permanent transient state in the e-nose readings. In this work, a sequential Bayesian filtering approach is proposed to efficiently integrate information from previous e-nose observations while updating the belief about the gas class on a real-time basis. We validate our proposal with two real olfaction datasets composed of dynamic time-series experiments (gas transitions are Considered, but no mixture of gases), showing an improvement in the classification rate when compared to a stand-alone probabilistic classifier.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech