Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

Abstract

In order to complete automatic route planning in the complex navigation environment, this paper proposes a quadratic optimization genetic algorithm combining the motion characteristics of the ships. Furthermore, the constraints of the maneuvering characteristics of ship are considered to calculate the accurate planning routes fast. First of all, the turning and speed reduction model of ships are established, which is the foundation for the accurate calculation of approach states between the own ship and target ships. Second, in order to check the position relationship between the planned route and the land objects (such as the islands and rocks) on the chart, a detection algorithm between lines and irregular polygon boundary based on quadtree method is acquired. Third, a new genetic algorithm based on double-cycling optimization is proposed by this paper. Aiming at the complex problem of comprehensive collision avoidance planning under the conditions of static and dynamic obstacles, this paper realizes an efficient and feasible solution of dynamic programming for ship route. Finally, the algorithm is verified on a test fireboat, and the experiment data prove the feasibility and effectiveness of the proposed algorithm by this paper.

A Comparative Analysis of Energy Consumption by Conventional and Anchor Based Dynamic Positioning of Ship

“…Thanks to the improvement of the control algorithms, the positioning system can more efficiently distribute the power to the thrusters, which translates into lower energy consumption. The group also includes all kinds of algorithms related to determining the route of the passage for a ship [38,39], steering along a given route of passage [40][41][42], and determining anti-collision maneuvers [43,44]. A separate group consists of algorithms enabling the autonomous movement of the vessel in the maritime navigation environment, including the acquisition of objects, determination of the route of the passage, steering along the designated route, implementation of anti-collision maneuvers and precise maneuvering in port areas [45][46][47][48][49].…”

Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

“…Therefore, in UAV route planning, the primary task is to avoid detection of radar positions. The classical radar equation describing detection characteristics of radar is as follows [14]:…”

“…50. Wang et al [14] proposed a double cycle genetic algorithm (DCGA). When the environmental model and motion constraints are considered in the experiment, the route planning result of DCGA algorithm is more realistic than that of traditional genetic algorithm Ant colony algorithm (ACO) is a random search simulated evolutionary algorithm, which solves the optimization problem by simulating ant foraging.…”

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control

“…Until the 1990s, many scholars and experts began to consider and use computer means, soft computing, and other technologies to study collision avoidance algorithms to address the issue of multi-ship collision [10]. The collision avoidance methods include velocity obstacle method (VO) [11], artificial potential field (APF) [12], A-Star [13], rapidly exploring random tree (RRT) [14][15][16], genetic algorithm [17], fuzzy theory [18], deep reinforcement learning (DRL) [19], and spline curves [20]. But overall, it can be classified into four categories, such as traditional algorithms, soft computing algorithms, intelligent learning algorithms, and spline curves.…”