scholarly journals Research on Real-Time Optimal Path Planning Model and Algorithm for Ship Block Transportation in Shipyard

2020 ◽  
Vol 8 (12) ◽  
pp. 991
Author(s):  
Chong Wang ◽  
Kang Wang ◽  
Jiabin Tao ◽  
Yongqing Zhou
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Optimal Path ◽  
High Energy ◽  
Dijkstra Algorithm ◽  
The Real ◽  
Security Issues ◽  
Time Path ◽  
Time Optimal ◽  
Shortest Path Algorithms

Special vehicles called transporters are used to deliver heavy blocks in the shipyard. With the development and application of information and communication technology in shipyards, the real-time positioning and ship blocks online scheduling system for transporters are being developed. The real-time path planning of transporters is important for maintaining the overall production schedule of ship blocks. Because of the large volume and heavy weight of ship blocks, there may be some problems, such as high energy consumption, block deformation and other security issues, when transporters loading a block make a turn. So, fewer turns of the transporters are also important to make a block transportation schedule. The minimum driving distance and fewer turns are considered simultaneously for transporter real-time path planning in this paper. A hybrid model considering the number of turns and the optimal path of the transporter is constructed. Moreover, the optimal scheduling model, considering path missing, is also discussed. Several shortest path algorithms are analyzed, which show that the Dijkstra algorithm is the best way to solve this model. From the attained simulation results, we demonstrate that the proposed model and algorithm have the ability to effectively solve real-time path planning for the ship block transportation in shipyards.

scholarly journals A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jianjun Ni ◽  
Liuying Wu ◽  
Pengfei Shi ◽  
Simon X. Yang
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Planning Method ◽  
Planning Problem ◽  
Detection Range ◽  
The Real ◽  
Time Path

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

scholarly journals A Real-Time Path Planning Algorithm for AUV in Unknown Underwater Environment Based on Combining PSO and Waypoint Guidance

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 20 ◽  
Author(s):  
Zheping Yan ◽  
Jiyun Li ◽  
Yi Wu ◽  
Gengshi Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Time Path ◽  
Optimization Parameters ◽  
Planning Algorithm

It is a challengeable task to plan multi-objective optimization paths for autonomous underwater vehicles (AUVs) in an unknown environments, which involves reducing travel time, shortening path length, keeping navigation safety, and smoothing trajectory. To address the above challenges, a real-time path planning approach combining particle swarm optimization and waypoint guidance is proposed for AUV in unknown oceanic environments in this paper. In this algorithm, a multi-beam forward looking sonar (FLS) is utilized to detect obstacles and the output data of FLS are used to produce those obstacles’ outlines (polygons). Particle swarm optimization is used to search for appropriate temporary waypoints, in which the optimization parameters of path planning are taken into account. Subsequently, an optimal path is automatically generated under the guidance of the destination and these temporary waypoints. Finally, three algorithms, including artificial potential field and genic algorithm, are adopted in the simulation experiments. The simulation results show that the proposed algorithm can generate the optimal paths compared with the other two algorithms.

Non-Collision Path Planning of a Payload in Crane Operating Space

2013 ◽  
Vol 198 ◽  
pp. 559-564 ◽  
Author(s):  
Jaroslaw Smoczek ◽  
Janusz Szpytko ◽  
Pawel Hyla
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Optimal Path ◽  
Manufacturing Processes ◽  
Automated Manufacturing ◽  
Star Graph ◽  
Graph Searching ◽  
Optimal Path Planning ◽  
Time Optimal ◽  
Operating Space

The problem of ensuring the safe and efficient cranes operations in automated manufacturing processes involves the automation of the operating workspace identification, non-collision and time-optimal path planning, and real-time following a payload along the determined path by crane motion mechanisms with expected precision. The paper describes the stereo vision based system used for identification of workspace of the laboratory scaled overhead travelling crane. The time-optimal trajectory of a payload is determined by using the A-star graph searching algorithm, and next real-time trucking by PLC-based crane control system.

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