Abstract
Abstract This paper presents a novel method on building relationship between the vision features of the terrain images and the terrain traversability which manifests the difficulty of field robot traveling across one terrain. Vision features of the image are extracted based on color and texture. The travesability is labeled with the relative vibration. The support vector machine regression method is adopted to build up the inner relationship between them. In order to avoid the over-learning during training, k-fold method is used and average mean square error is defined as the target minimized to get the optimal parameters based on parameter space grid method. For the traveling smoothness of field robot, the original traversability prediction is transformed to computed traversability prediction based on different initial sub-regions. The optimal path is given by minimizing the sum of computed traversability prediction of all sub-regions in each path. Three experiments are discussed to demonstrate the effec...
Highlights
Traversability, which means the difficultly of field robot traveling across one region, is a description of traveling feature for one type of terrain
We give a novel method for traversability prediction based on vision features using support vector machine regression
The test platform is an field robot ourselves designed with a HD camera and an Crossbow VG400 Inertial Measurement Unit (IMU) 16, shown in Fig. 4, and the Support vector machine (SVM) implementation in the experiments is referred to the LIBSVM software 17
Summary
Traversability, which means the difficultly of field robot traveling across one region, is a description of traveling feature for one type of terrain. Traversability prediction is a much more important consideration for the robotics application in the field such as planet exploration, volcano detection, search and rescue work and so on. This prediction plays an unreplaced role in the optimization of path planning. Getting the trained SVM regression function, the image of the terrain in front of field robot is linearly divided into several sub-regions where the color and texture features are extracted from. The traversability of every sub-region described with relative vibration will calculated under the SVM regression function using the vision features. We give a novel method for path planning considering the traveling smoothness of field robot, through finding the sub-region with minimal traversability in each row.
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