Research Article
A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning
| (1) | Initialize: connecting the vertices of convex polymorphism to each other and forming an aggregate L | | (2) | for find the shortest line k in the aggregate L | | (3) | repeat: | | (4) | ifk passes through the obstacles then | | (5) | select the next line in the aggregate L | | (6) | else | | (7) | if the two outer angles formed by the line k and the corresponding convex polygon boundary are more than 180° then | | (8) | if the angle between k and other candidate lines exceeds 180° then | | (9) | select the next line in the aggregate L | | (10) | else delete other lines of the vertex and keep the shortest line k | | (11) | else select the next line in the aggregate L | | (12) | Until: All vertices are traversed and the phase-free network graph is constructed |
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