Research On Markov Chain-based Terrain Recognition

Terrain identification and classification technology is mainly based on visual recognition,though the precision was very high, but the visual method is susceptible to obstructions, andthe light-dependent. The identification and classification of the vibration method is notaffected by the light and the obstacle, so the application of vibration mode terrain hasimportant significance. Markov chain methods is very good in the description of the naturalprocess, have important applications in many areas, and has high practical value. So thispaper attempts to use Markov chain method to the identification and classification of terrain.First, through the experiment of a mobile robot run through the terrains to obtain theoriginal data, sequence of segmented data files were obtained from the original data, andintroduced the mobile robot, including the structure and the control of it, then introduced theterrain vibration data acquisition program, including experimental content, environmentalconditions and instrumentation. Focused on processing of the experimental data, the datafeature extraction.Second, attempted the Markov chain to terrain recognition and classification, obtain theprediction recognition results. Studied the Markov chain forecasting methods and steps todetermine sample grading method, attempted three kind of Markov chain to terrainrecognition and classification, two prediction methods, the fixed length of the sample and thedynamic sample length prediction recognition method. All of the terrain features of thevibration data files for processing. And the results were counted, got the statistical data tableof different characteristics, different speed, different positions.Finally, analysed and the results of the sequence of the vibration characteristics of theterrain by using the Markov chain to predict and recognition. Analysed and estimated thestatistical results of different characteristics, the direction of the different sensors, differenttravel speed, different terrains, different Markov chain forecast identification methods.Discovery the feature of ‘mean square’ value’s recognition accuracy rate is higher, data ofdirection z (vibration perpendicular to the direction of the ground) sensor is more conduciveto the recognition classification, both the the dynamic sample test mode and absolute Markovchain methods to identify classification results are better. The best average accuracy rate is 85%.Markov Chain methods applied to the terrain recognition and classification got a certainaccuracy, through experiments and tests found the main feature, the key position of the sensor,preferably prediction and identification method. But the accuracy rate depended on terrain andspeed. Markov chain applied to terrain identification and classification can be further studied.