In response to the challenges of slow convergence and suboptimal path optimization in complex obstacle environments, this paper presents an adaptive path planning algorithm based on IRRT*-Connect that is tailored to the environmental complexity. This algorithm combines Informed-RRT* and RRT-Connect, namely IRRT*-Connect algorithm for initial path planning to improve the efficiency of initial path planning; additionally, it introduces a sampling constraint probability p to confine sampled areas and augment the purposefulness of sampling. Furthermore, a step length calculation method based on environmental obstacle coefficients is devised to dynamically adjust extension step lengths, thereby bolstering the algorithm′s adaptability in traversing complex environments. Through the comparison of multiple sets of experiments, we show that the number of algorithm nodes is reduced by 9.75%, and the path length is reduced by 20.82%, and the planning time is shortened by 3.08%, which proves that the improved IRRT*-Connect adaptive step path planning algorithm has a strong ability to adapt to the environment, high node utilization, and the planning effect is better.