Research On Path Planning Method Of Mobile Robot

Path planning is a fundamental technology that enables robots to navigate autonomously.By means of path planning,the robot is capable of selecting the most advantageous trajectory based on task specifications and variations in the surrounding milieu,while simultaneously circumventing collisions with obstructions.The increasing scope of application for mobile robots has led to a heightened complexity in path planning.Simple obstacle environments no longer suffice to meet the practical requirements of individuals.The present study investigates the algorithmic approach to path planning for mobile robots operating in intricate environments.The present study involves a comprehensive analysis of the conventional Rapidly-exploring Random Trees(Rapidly-exploring Random Trees,RRT)path planning algorithm and the artificial potential field method.The algorithm is subsequently enhanced and validated through simulation using Python programming.The primary focus of this research encompasses the following topics:(1)This study proposes an enhanced RRT algorithm with sub-sampling area bias to address the issues of low search efficiency,inadequate expansion orientation,and path redundancy in the global environment.The proposed approach involves the introduction of the initial target expansion space and obstacle expansion space,followed by the formation of a new sampling area based on their inclusion relationship.Subsequently,distinct bias strategies are established for different partitions of the new sampling area.Finally,the two-point straight-through method is employed.Conduct de-duplication procedures on the path that was searched.The findings of the simulation study indicate that the algorithm exhibits superior stability,shorter search time,reduced sampling nodes,and enhanced path finding ability in comparison to the RRT algorithm and Goal Bias RRT.(2)In this study,we present an enhanced artificial potential field approach that addresses the challenges of the unattainable target and local minimum issues in a confined setting.This method builds upon the potential field function and incorporates the breakaway force to achieve improved performance.The repulsion function incorporates the location of the target point and modifies the distance parameter to regulate the magnitude of the repulsion force.This approach effectively addresses the issue of obstacles obstructing the target point.In the event of a local minimum,a breakaway force is introduced to disrupt the equilibrium of forces and facilitate escape from the extreme point.(3)The study proposes the integration of the enhanced Rapidly-exploring Random Tree(RRT)algorithm and the enhanced Artificial Potential Field(APF)method to address the intricacies of environments that comprise both static and dynamic obstacles.The two algorithms are switched based on the robot’s proximity to the dynamic obstacle repulsion field,in order to achieve both global and local optimal path planning.