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Aiming at the problems of sparrow search algorithm (SSA) in the study of robot obstacle avoidance, such as early convergence to the local optimal and difficult to jump out, the initial population distribution is not wide enough, and the balance ability is poor, the improvement is made. Firstly, a three-layer neural network is used to model the planning environment. Secondly, Halton sequence was introduced to obtain the initial population distribution, and the individual locations with wider distribution and more traversal were obtained to improve the speed and efficiency of the later optimization. The Brownian motion was used to optimize the step size adjustment of sparrow position update, which helped the algorithm get rid of the local optimal solution and balanced the local search rhythm of global switch. Finally, the clothoid curve method is used to smooth the path, and the path satisfying the mechanical properties of the robot is obtained. After the verification of six standard functions and the comparison of Wilcoxon test P values, it can be seen that the improved algorithm is significantly optimized compared with SSA and CSSA algorithms in all indexes, and has the same level of time complexity as SSA. Finally, the smooth obstacle avoidance path of the robot is obtained through map simulation.