Deep Reinforcement Learning Based Path Planning and Collision Avoidance for Smart Ships in Complex Environments

The port and shipping industry urgently needs to accelerate the transition to green and intelligent technologies in the post-pandemic era. Ship collision avoidance remains a pivotal challenge in achieving intelligent navigation. This paper proposes a deep reinforcement learning-based method for ship collision avoidance path planning in dynamic environments. Initially, a two-dimensional grid-based spatial environment is established to model ship domains, with collision risk levels assessed based on Automatic Identification System (AIS) data and the International Regulations for Preventing Collisions at Sea (COLREGs). The ship collision avoidance problem is formulated as a Markov Decision Process (MDP), wherein the observation space, action space, and reward function during collision avoidance are clearly defined. Utilizing this MDP framework, the Dueling Double Deep Q-network (D3QN) algorithm is employed to derive collision avoidance decisions. The algorithm incorporates prioritized experience replay and an adaptive decay greedy exploration strategy to enhance training efficiency. Simulation experiments are conducted across multiple encounter scenarios under collision regulations. The results substantiate the effectiveness of the proposed deep reinforcement learning approach for ship collision avoidance path planning.

I. Introduction

Intelligence in shipbuilding and shipping is a key trend enabling high-quality development in the post-pandemic era^[1]. Intelligent navigation, a core smart ship technology, is realized through advanced automation and navigation systems. These systems facilitate intelligent ship operations, enhancing the safety and efficiency of seaborne transport while providing substantial support to the crew. The primary components include autonomous navigation, automatic collision avoidance, and energy management systems. Advanced sensors and GPS technology monitor ship position, heading, and speed in real time. These systems also analyze sea and weather conditions, alongside other data, to automatically adjust course and speed, thereby ensuring navigational safety. Moreover, intelligent navigation is closely linked to optimizing ship speeds and routes to reduce fuel consumption and emissions, which is critical for mitigating environmental impacts and lowering operating costs in the shipping industry.