Path-planning algorithm based on elastic force contractions for autonomous navigation of unmanned container ships in waterborne transportation

Using unmanned container ships (UCSs) to reduce the environmental impact and cost of waterborne cargo transportation has become a prominent topic in maritime research. This study proposes an elastic force contraction algorithm (EFCA) and a minimum set method (MSM) to reduce operational costs and congestion in the waterway network. The EFCA rapidly plans a path for UCSs from an origin point to a destination, ensuring swift passage through the waterway. The MSM removes redundant nodes in the initial path planned by the EFCA, shortening the path length and thereby reducing operational costs. Finally, a UCS kinematic mathematical model is utilized for trajectory planning based on the path planned by the application of EFCA and MSM. Simulation experiments show that the computational efficiency of the proposed algorithm is 70.2% and 96.6% higher than that of the bidirectional rapidly exploring random trees (BIRRT) and state prediction rapidly exploring random trees (spRRT) algorithms, respectively. Additionally, the trajectory length is 8.76% shorter than that derived from the spRRT-informed algorithm. Thus, the proposed algorithm can rapidly provide feasible and economical path information for UCSs in practical applications.