Path Planning For Mobile Robot Based On Artificial Potential Field Guided Evolutionary Algorithm In Dynamic Environments

Path planning of mobile robot is the critical technology of robot and the hotspot of the certain research domain. In this paper, path planning of mobile robot and relative problems in dynamic environments were mainly studied. The objective of this paper is to construct path planning scheme that improves the intelligence level of mobile robot in dynamic environments and make the mobile robot adapt to the environmental changes better, then lays the foundation for further practical use. The main research work is as follows:Firstly, the status quo and research methods of mobile robot path planning were analyzed and summarized. Hardware system structure of mobile robot, movement control process, the sensor systems and so on were introduced.Secondly, potential functions of traditional artificial potential field were improved. The path planning of mobile robot was achieved in dynamic environments based on improved artificial potential field. Simulation results testified the effect of improved artificial potential field.Thirdly, based on the evolutionary algorithm theory, the application process of evolutionary algorithm during path planning was determined. Cost functions in evolutionary algorithm were improved. According to the blindness of single evolutionary algorithm, the artificial potential field would be used for guiding the evolutionary algorithm because of its advantages. Compared with the results of two ways, artificial potential guided evolutionary algorithm was testified better.Finally, the dead reckoning experiment was done based on the mobile robot platform and the map was built using data from sensors. Artificial potential guided evolutionary algorithm was used in mobile robot platform. Mobile robot path planning in dynamic environments was instructed.