Work

Research on Energy-Efficient Path Planning for Tugboat Based on Ant Colony Optimization Integrated with Potential Field Maps

Year: 2026

Type: article

Abstract: To address the problems of high energy consumption and excessive navigation time in autonomous tugboat operations during cross-regional missions, an Ant Colony Optimization algorithm integrated with Potential Field Maps (PFM-ACO) is proposed. The proposed method is capable of planning routes that satisfy navigation time constraints, thereby improving navigation efficiency while minimizing voyage energy consumption. Specifically, time-based and energy-consumption-based potential field maps are constructed using ocean current data. The initial pheromone matrix and heuristic function are further redesigned to enhance target-oriented guidance. In addition, an adaptive heuristic factor based on a goal-biased strategy is introduced to strengthen the global search capability of the algorithm. Finally, the proposed PFM-ACO algorithm is compared with the A*, A*-DCE and NDACA algorithms. Experimental results demonstrate that, under navigation time constraints, the paths generated by PFM-ACO achieve both the lowest energy consumption and the highest path smoothness. Overall, the proposed algorithm outperforms the comparative methods, indicating its effectiveness and superiority in energy-efficient path planning for tugboat navigation.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse14060524

Authors: Yao Fang, Diju Gao

Institution: Shanghai Maritime University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137