To address the issues of hull damage, sinking, and personnel entrapment caused by ship collision accidents, a path planning method for ship engine room rescue robots based on an improved ant colony algorithm was proposed. Aiming at the problems of low search efficiency and slow convergence speed of traditional ant colony algorithm in path planning, the heuristic function and pheromone update were improved. Firstly, an adaptive iterative weighting factor coupled with a local obstacle density correction term was introduced to mitigate the low search efficiency prevalent in the algorithm’s initial stages. Secondly, pheromone reinforcement was strategically applied to the optimal path, while appropriate pheromone diminution was executed on suboptimal paths following each iteration, thereby suppressing the detrimental influence of inferior routes. Thirdly, adaptive parameters were introduced to enable the algorithm to assign different weights to the global optimal, iterative optimal, and worst paths at different iteration stages, thereby enhancing the convergence and robustness of the algorithm. Through the above improvements, the algorithm has achieved an optimization transformation from relying solely on distance and pheromone concentration to comprehensively considering target point gravity, obstacle rejection, local environment complexity, and adaptive search strategies. Experimental results show that the improved algorithm shortens the path by 10 % compared with the traditional algorithm, redundant turning nodes are reduced by 65%, and the number of iterations is reduced by 96.4%. The validation in real ship cabin scenarios further demonstrates that the proposed method significantly reduces detours and redundant turning points in path planning, accelerating convergence speed and effectively avoiding local optimum traps, thereby realizing more efficient and stable path search in complex environments.