A method is proposed for multi-robot path planning in complex environments, employing an improved rapidly-exploring random tree (IRRT) algorithm and predicted-improved artificial potential field (P-IAPF) algorithm to achieve obstacle avoidance in multi-robot systems. Firstly, in view of the shortcomings of slow convergence speed and random search range of RRT algorithm, the target-biased strategy is used to guide the generation of random sampling points, simultaneously, the improved artificial potential field method is integrated into the bidirectional random search tree to rapidly identify the global path. Secondly, in response to the problem of traditional APF algorithm being prone to getting stuck in local minima and having low path planning efficiency, a predicted APF algorithm with multiple virtual keypoints is proposed, the Douglas Peucker (DP) algorithm is used to find the sequence of sub keypoints in the planned global path, and the multi robot system switches keypoints to escape from local minima, thereby enhancing both the efficiency and smoothness of multi-robot path planning. Ultimately, to confirm the effectiveness of the proposed algorithm, simulation experiments in complex environments with U-shaped and long rectangular obstacles are carried out, and it has the advantages of high path planning efficiency and avoiding multi-robot collision.