Research On Path Planning Of Mobile Robot Based On Intelligent Optimization Algorithm

For mobile robots to complete tasks autonomously in complex environments,navigation technology is crucial,and path planning is an important part of navigation technology.Although traditional path planning algorithms are widely used,they will have problems such as poor adaptability in complex environments.Due to the good communication mechanism and parallel mechanism,strong computing power,and simple modeling,the intelligent optimization algorithm can better solve the path planning problem in complex environment.Aiming at the path planning problem in complex environment,this paper studies two intelligent optimization algorithms from the perspective of algorithm search mechanism itself and problem solving,and applies them to global path planning of mobile robots,the main research content is as follows:(1)Aiming at the problems of low accuracy and easy to fall into local extremums when performing path planning in complex environments,this paper proposes a particle swarm algorithm(RDS-PSO)based on restart strategy and adaptive dynamic adjustment mechanism.Firstly,a nonlinear decreasing inertia weight and adaptive dynamic adjustment learning factor are proposed to balance the local and global search capabilities.Then,the restart strategy is introduced to jump out of the local optimal and expand the search scope by re-random initialization.The penalty function is selected as the fitness function to solve for the shortest collision-free path.Finally,the improved algorithm is combined with cubic spline interpolation to solve the global smooth path planning problem,and simulation experiments show that the optimal path accuracy of the improved algorithm proposed in this paper reaches94% in the same time,which is 30% higher than the existing improved algorithm.The optimal path accuracy in complex environments is improved by 50% compared to standard PSOs.Significantly improved path quality.The local extremum and precocious maturation problems of the PSO algorithm are avoided.(2)Aiming at the problem of poor performance of arithmetic optimization algorithm(AOA)in solving complex functions,a multi-strategy fusion sine and cosine and arithmetic hybrid optimization algorithm(SSCAAOA)is proposed.Firstly,the improved sine chaotic mapping initialization is used to increase the diversity of the initial population of AOA.Then,the mathematical optimizer acceleration function(SMOA)and adaptive adjustment parameters are reconstructed through nonlinear functions to balance global search and local search capabilities.By fusing the sine and cosine algorithm with improved parameters and the position inertia and information sharing mechanism in the PSO algorithm,the optimization accuracy and convergence speed are improved,and the global search capability is enhanced.The performance test of SSCAAOA and different optimization algorithms on the benchmark function verifies the performance of the improved algorithm.Combined with the node quadratic optimization method,the improved algorithm is applied to the path planning problem,and finally the simulation experiment is carried out in three different environments,and the accuracy of the optimal path in the complex environment reaches 90% under the condition of ensuring that the feasible path can be found every time,and the experimental results verify the effectiveness and superiority of the improved algorithm.