The Research On Methods Of Mobile Robot Path Planning In 2-D Environment

The research and development of mobile robots have been paid great attention to in recent years. The requirement of its ability to take corresponding measures in accordance with the environmental changes in its moving courses has become higher and higher. Therefore, the path planning, as an important fields in robot intelligence, appears particularly significant. Path planning is searching a optimal path without touch from the start to the end under way a performance indicators.This thesis mainly focuses on the study of the path planning of mobile robots in complicated environment and its relevant problems, especially the path planning in static and dynamic environment. Following are the main content of this thesis:The first part analyses the Khatib artificial potential field model in which we take the goal points with mobile robots with the same polarity as the charge and the obstacles with the conversely polarity as the charge. Path planning is under the attract and discouraging edge of electric charge. On the base of this model, a path planning method of mobile robot is designed.The second part describes the problem of goals non-reachable with obstacles nearby (GNRON) when using potential field methods for mobile robot path planning. Considering the problem is caused by the excessive of the artificial potential field discouraging function at the point of the goal, the relative positions of the robot and the goal is taken into considering. Amend the artificial potential field discouraging function by this way, the problem of goals non-reachable with obstacles nearby (GNRON) is solved. And the improved potential field model is used in path planning with dynamic environment the results of simulation have verified that the method can solve the problem effectively, even though there are stochastic moving obstacles in the surroundings.The third part describes the local minimum of the Khatib artificial potential field model and its cause. A new way of optimizing the path using improved potential field approach with genetic algorithm was proposed.