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

Terrain Classification for Track-driven Agricultural Robots

A long-term goal of agricultural automation is to deploy intelligentrobots to facilitate labor-intensive tasks such as crop care or selective harvesting with minimum human supervision. To achieve this goal, the agricultural robots must be able to adapt themselves in response to various terrain conditions. The reason is that the terrain characteristics can jeopardize the performance of a robotin carrying out a taskor even causing it being trapped in the field. The aim of this work is to evaluate the effectiveness of using an intelligent algorithm, i.e. support vector machine (SVM) in recognizing various terrain conditions in an agricultural field. For this purpose, asmall tracked-driven mobile robot together witha terrain test bed has been developed. The terrain test bed emulates three types of terrain conditions, i.e. sand, gravel and vegetation. The tracked-driven robot is embedded with a low power MEMS accelerometer for measuring vibration signals resulted from the track-terrain interaction. An experimental study was conducted usinga SVMtrained with three different kernel functions, i.e. linear function, polynomial function and radial basis function (RBF). The results showed that the SVM can recognize different terrain conditions effectively. This work contributes to devising a self-adaptive agricultural robot in coping with changing terrain conditions