scholarly journals A Novel Heuristic Emergency Path Planning Method Based on Vector Grid Map

2021 ◽  
Vol 10 (6) ◽  
pp. 370
Author(s):  
Bowen Yang ◽  
Jin Yan ◽  
Zhi Cai ◽  
Zhiming Ding ◽  
Dongze Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Intelligent Transportation Systems ◽  
Road Network ◽  
Transportation Systems ◽  
The Road ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Set Up ◽  
Planning Methodology ◽  
Path Planning Algorithm

Emergency path planning technology is one of the research hotspots of intelligent transportation systems. Due to the complexity of urban road networks and congested road conditions, emergency path planning is very difficult. Road congestion caused by urban emergencies directly affects the original road network structure. In this way, the static weight of the original road network is no longer suitable as the basis for path recommendation. To handle the dynamic situational road network, an equidistant grid emergency path planning framework will be designed. A novel situation grid road network model, based on situation information, is proposed and applied to an equidistant grid emergency path planning framework. A situational grid heuristic search will be proposed methodology based on this model, which can be used to detect the vehicles passing around the congestion area grid and the road to the destination in the shortest time. In the path planning methodology, a grid inspired search strategy based on quaternion function is included, which can make the algorithm converge to the target grid quickly. Three graph acceleration algorithms are proposed to improve the search efficiency of path planning algorithm. Finally, this paper will set up three experiments to verify our proposed method.

scholarly journals A CooperativeQ-Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoyong Zhang ◽  
Heng Li ◽  
Jun Peng ◽  
Weirong Liu
Keyword(s):  
Path Planning ◽  
Intelligent Transportation Systems ◽  
Optimal Path ◽  
Road Networks ◽  
Transportation Systems ◽  
Prediction Algorithm ◽  
Urban Road ◽  
Learning Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm

As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperativeQ-learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form thatQ-learning can be applied firstly. Then the gray prediction algorithm is combined intoQ-learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm.

Research on Path Planning Algorithm in Intelligent Transportation

2014 ◽  
Vol 536-537 ◽  
pp. 833-836
Author(s):  
Jin Peng Tang ◽  
Ling Lin Li
Keyword(s):  
Path Planning ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Density ◽  
Advantages And Disadvantages ◽  
Key Factor ◽  
Planning Algorithm ◽  
Transportation Vehicle ◽  
Path Planning Algorithm

Path planning is a key factor in intelligent transportation vehicle navigation system, compared to a variety of advantages and disadvantages of the path planning optimization algorithm. According obstacle distribution characteristics similar characteristics to the grid circuit resistor, proposed a Circuit Map path planning algorithm. The city's roads each segment was mapped to the circuit network, and the resistance of the resistor were mapped to with the Weighted sum of road length and width, and traffic density. This can be achieved by solving the circuit that intelligent transportation systems path planning and navigation services.

scholarly journals Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 127 ◽  
Author(s):  
Mingbin Zeng ◽  
Xu Yang ◽  
Mengxing Wang ◽  
Bangjiang Xu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Optimal Solution ◽  
Transportation Systems ◽  
Function Optimization ◽  
Dynamic Path Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

Research on pull-type multi-AGV system dynamic path optimization based on time window

2021 ◽  
pp. 095440702098271
Author(s):  
Hongying Shan ◽  
Chuang Wang ◽  
Cungang Zou ◽  
Mengyao Qin
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Planning Problem ◽  
Dijkstra Algorithm ◽  
Before And After ◽  
Common Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Set Up ◽  
Path Planning Algorithm

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

scholarly journals Any-Angle Path Planning

AI Magazine ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 85-107 ◽  
Author(s):  
Alex Nash ◽  
Sven Koenig
Keyword(s):  
Video Games ◽  
Path Planning ◽  
Shortest Path ◽  
Grid Cells ◽  
Grid Graph ◽  
Planning Algorithm ◽  
Planning Algorithms ◽  
Planning Methodology ◽  
Path Planning Algorithm

In robotics and video games, one often discretizes continuous terrain into a grid with blocked and unblocked grid cells and then uses path-planning algorithms to find a shortest path on the resulting grid graph. This path, however, is typically not a shortest path in the continuous terrain. In this overview article, we discuss a path-planning methodology for quickly finding paths in continuous terrain that are typically shorter than shortest grid paths. Any-angle path-planning algorithms are variants of the heuristic path-planning algorithm A* that find short paths by propagating information along grid edges (like A*, to be fast) without constraining the resulting paths to grid edges (unlike A*, to find short paths).

scholarly journals Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (24) ◽  
pp. 4974
Author(s):  
Dejun Feng ◽  
Xingyu Shen ◽  
Yakun Xie ◽  
Yangge Liu ◽  
Jian Wang
Keyword(s):  
Intelligent Transportation Systems ◽  
Road Network ◽  
Smart Cities ◽  
Heuristic Method ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Road Extraction ◽  
Learning Models ◽  
The Road ◽  
The Common

Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.

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