Terrain classification for track-driven agricultural robots
A long-term goal of agricultural automation is to deploy intelligent robots 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 robot in carrying out a task or 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, a small tracked-driven mobile robot together with a 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 using a SVM trained 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.