Research On Path Planning Based On Improved Sparrow Search Algorithm

With the widespread application of robotics,various directions in the field of robotics have become popular directions of modern scientific and technological research.Path planning is an important research direction in the field of robotics.However,traditional or single-path planning methods are difficult to meet the needs of modern,progressively complex robotic applications.Therefore,the research of path planning methods with high efficiency,stability,and safety has become a real need in the field of robotics research.The first task of path planning for mobile robots is to perceive the surrounding environment and record map information.Secondly,the need to choose different algorithms for planning according to different scenarios arises,taking into account the computational complexity of the algorithm and the robot’s computing power.Moreover,various factors,such as robot speed,path distance,and safety,need to be considered during the path planning process,particularly in scenarios where human contact is possible.Therefore,the research on path planning for mobile robots requires comprehensive analysis and optimization from several aspects in order to achieve efficient and safe robot movement in different scenarios.Firstly,this thesis describes the development and application significance of path planning for mobile robots,introduces several traditional path planning methods and path optimization methods,and analyzes their advantages and disadvantages through experiments.Secondly,this thesis introduces in detail the two main applied path planning methods,the sparrow search algorithm and the dynamic window approach,and optimizes several aspects of the sparrow search algorithm to improve the efficiency and safety of path planning.The main research content is described as follows.1.Concerning the design of a fitness value evaluation function for path planning,the application characteristics of intelligent algorithms in this field are taken into account.Additionally,the characteristics of misleading local optimal individuals in the path planning of the sparrow search algorithm are considered,along with the low fault tolerance rate of the entire planning process.Based on this,the fitness function is improved through a second judgment and the elimination of local optimal individuals,thus reducing the number of misleading local optimal individuals in the iterative process.2.This thesis proposes two traditional methods for optimizing paths after the path planning of the sparrow search algorithm and analyzes their advantages and disadvantages.Additionally,a linear optimization path strategy is introduced to minimize invalid paths and useless nodes,shorten and smooth the paths,and improve the path finding efficiency.3.In order to improve the fault tolerance of the path planning of the sparrow search algorithm,chaotic mapping is used to generate the initial population and improve the fault tolerance of the path planning.To address the problem of the iterative process of path planning in the sparrow search algorithm making it easy to fall into the local optimum and the low population search range leading to low overall planning efficiency,this thesis proposes a local search strategy with dynamic parameters that change with the number of iterations and improves the way to update the position of the discoverer in the sparrow search algorithm.Furthermore,to address the drawbacks of the poor static path planning and obstacle avoidance abilities of the sparrow search algorithm,it is integrated with the dynamic window approach to ensure the safety of robot operation.Finally,the reasonableness and practicality of the algorithm improvement areverified by simulation experiments.