Improved EO fusion DWA for sand fixation robot path planning

To achieve autonomous navigation of sand fixing robot in a dynamic environment, improved EO fusion DWA is proposed for sand fixing robot path planning. In global path planning, covariance learning is proposed for the initiating concentration of the traditional EO algorithm to establish the connection between initial concentrations, which is easy to jump out of the local optimum; sinusoidal adjustment factors are proposed for the exponential term coefficients to balance the exploration and mining ability of the algorithm; and a new fitness function is added to measure the superiority or inferiority of the global path. Use quadratic path optimization to improve path smoothing. Incorporate DWA algorithm for local path planning. Simulation results show that the algorithm in this paper can not only effectively shorten the path distance but also effectively avoid obstacles in complex environments.