The Control Policy For Mobile Robots With Eccentric Centroid Based On Learning From Demonstration

Mobile robot is a integrated system that includes several functions. Because of its high autonomy, intelligence and adaptability to the external environment, the mobile robot has a broad application prospects in various industries, and becomes an important branch in the field of robotics. In recent years, mobile robots have been widely used in planetary exploration, military reconnaissance, medical services, hazardous and harsh environment operations, agriculture, etc. This paper focuses on the mobile robot applications in agriculture.Autonomous learning requires that all agricultural robots are fully autonomous, that is to say, sensor information acquisition, mission planning, and motion control are done by the robot itself. In recent years, a method named Learning from Demonstration used in agriculture robot has been gradually developed and become the research point in artificial intelligence and machine learning areas。In the early 90s, Support Vector Machine presents with the gradual improvement of statistical learning theory, based on this theory algorithm. Support Vector Machine now has become a closely watched classification technology.When SVM trains data samples, it needs to consume a large amount of time. So to accelerate the training speed, we propose a new learning strategies—the learning strategy based on clustering of increamental svm. We analyze datas by combinating of time weight and data features to make the algorithm suitable for processing large amounts of datas. In particular, we prove the effectiveness and practical of this algorithm with the experiments.Mobile Robot often has an eccentric centroid because of the limited precision in producing causes. That results in difference of robot's final position between expected one and actual one since most control approaches are based on an ideal model whose centroid is not eccentric, thus extra energy is needed for robots to adjust that difference. To solve this problem, we build a model with eccentric centroid to analyze the distribution of traction on each wheel in this condition and get an optimization solution set of this traction with linear programming algorithm. Specifically, we conduct simulation experiments to demonstrate the algorithm is of positive effect in reducing cost of extra energy.