Aiming at the problems of low efficiency and long time consumption in path planning of sparrow algorithm, an improved sparrow algorithm is proposed for path planning of inspection robots in distribution rooms. Firstly, using Logistic Tent chaotic mapping to optimize the quality of sparrow population and reduce subsequent blind searches. Secondly, a controllable adaptive random exploration discoverer update strategy was proposed to enhance the algorithm′s global search capability while further improving planning efficiency and shortening search time. Next, in order to avoid algorithm stagnation in the later stage, a spiral position update factor is introduced to enhance local development capabilities. Finally, combining cubic interpolation B-splines for smoothing processing makes the path more suitable for the distribution room environment. The experimental results show that the improved sparrow algorithm can efficiently complete the path planning task during inspections. Compared to the original algorithm, it is significantly optimized in terms of iteration efficiency, path search time, and other aspects.