An improved sparrow search based intelligent navigational algorithm for local path planning of mobile robot

In this paper, an improved sparrow search algorithm (SSA) for local path planning problem of mobile robot in an unknown environment is presented. The problems of premature convergence and decline of population diversity of basic SSA are solved by the inspiration of fitness-distance balance (FDB) selection and Harris Hawks Algorithm. A hybrid fitness function is formulated considering both path length and path safety, which enables the mobile robot to move to the target location safely. The effectiveness and superiority of the proposed improved SSA (ISSA) is verified in CEC 2017 suite for comparison experiments with multiple intelligent optimization algorithms. Local path planning simulation experiments are implemented using the proposed algorithm in the unknown environment and compared with other algorithms, and the results show that our algorithm is effective and robust in solving local path planning problem of mobile robots. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.