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Path planning is a critical issue to ensure the safety and reliability of the autonomous navigation system of the autonomous underwater vehicles (AUVs). Due to the nonlinearity and constraint issues, existing algorithms perform unsatisfactorily or even cannot find a feasible solution when facing large-scale problem spaces. This paper improves the path planning of AUVs in terms of both the path planning model and the optimization algorithm. The proposed model is comprehensive, which aggregates the length, energy consumption, and collision risk into the objective function and incorporates the steering window constraint. Based on the model, we develop a nature-inspired ant colony optimization algorithm to search the optimal path. Our algorithm is named alarm pheromone-assisted ant colony system (AP-ACS), since it incorporates the alarm pheromone in addition to the traditional guiding pheromone. The alarm …