scholarly journals A Path Planning Algorithm of Closed Surface for Fiber Placement

2012 ◽  
Author(s):  
Zhenyu Han ◽  
Fu Qiangqiang ◽  
Yang Fan ◽  
Fu Yunzhong ◽  
Fu Hongya
Keyword(s):  
Path Planning ◽  
Closed Surface ◽  
Fiber Placement ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals An Efficient and Adaptable Path Planning Algorithm for Automated Fiber Placement Based on Meshing and Multi Guidelines

Materials ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4209
Author(s):  
Hong Xiao ◽  
Wei Han ◽  
Wenbin Tang ◽  
Yugang Duan
Keyword(s):  
Path Planning ◽  
Accuracy Analysis ◽  
Fiber Placement ◽  
Surface Meshing ◽  
Automated Fiber Placement ◽  
Planning Algorithm ◽  
Selection For ◽  
Update Strategy ◽  
Path Planning Algorithm

Path planning algorithms for automated fiber placement are used to determine the directions of the fiber paths and the start and end positions on the mold surfaces. The quality of the fiber paths determines largely the efficiency and quality of the automated fiber placement process. The presented work investigated an efficient path planning algorithm based on surface meshing. In addition, an update method of the datum direction vector via a guide-line update strategy was proposed to make the path planning algorithm applicable for complex surfaces. Finally, accuracy analysis was performed on the proposed algorithm and it can be adopted as the reference for the triangulation parameter selection for the path planning algorithm.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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