The objective of this paper is to design a mobile robot with automatic motion behaviors and obstacle avoidance functions. The robot is also able to make the SLAM (Simultaneous Localization And Mapping) at an unknown environment. The robot position is calculated by the developed software program from the motor encoders. An obstacle avoidance controller is developed by the fuzzy theory. A LRF(laser ranger finder) is installed on the robot. The sensing data of this LRF are applied to calculate the environmental information for the obstacle avoidance controller. Then, the ICP (Iterative Closest Point) algorithm is applied to compare the position error of the environmental data in order to obtain the estimated position of the LRF. Finally, these estimated position data are used to calculate the final SLAM of this mobile robot. Both the simulation and experimental results show that this developed robot system work very well. Key word: SLAM, obstacle avoidance, ICP(Iterative Closest Point), LRF(laser range finder).
The path planning is developed on the subject which aim to guide a walking robot from a starting point forwards a goal. The paper presents a now model merging the optimization of support vector machine (SVM) into the artificial potential field path planning. Using the path planning, robots can estimate a free smooth walking path of obstacles. Based on the statistical learning theory, the SVM can be used to optimize a zero-potential decision boundary in the 2-dimemsional map with a large margin. The idea of large margin implies that a wide path can be obtained with the employment of the SVM. With the RBF kernel, the presented method produces a 2-dimemsional potential-field map. In the map, obstacles are modeled as the sum of various parametric Gaussian distributions. As known, a map composed with the superposition of 2-dimemsional smooth Gaussian functions can also achieve the walking path smooth. Upon this, potential-field or road map based robot navigation can easily be applied to achieve the path smoother. The proposed model provides a way to search a wide smooth road for the robot. Detailed experiments and discussions are included in the paper.