An optimal path algorithm of high security based on Dijkstra algorithm

2013 ◽  
Author(s):  
Yong Zhu ◽  
Xiaohuan Liu ◽  
Xiaohong Yu
Keyword(s):  
Optimal Path ◽  
Dijkstra Algorithm ◽  
High Security

scholarly journals Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system

2021 ◽  
Vol 24 (2) ◽  
pp. 1173
Author(s):  
K. Ibrahim Ata ◽  
A. Che Soh ◽  
A. J. Ishak ◽  
H. Jaafar
Keyword(s):  
Shortest Path ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Search Tree ◽  
Binary Search ◽  
Ant Colony ◽  
Binary Search Tree ◽  
Dijkstra Algorithm ◽  
Dijkstra's Algorithm ◽  
Parking System

A common algorithm to solve the single-source shortest path (SSSP) is the Dijkstra algorithm. However, the traditional Dijkstra’s is not accurate and need more time to perform the path in order it should visit all the nodes in the graph. In this paper, the Dijkstra-ant colony algorithm (ACO) with binary search tree (BST) has been proposed. Dijkstra and ACO are integrated to produce the smart guidance algorithm for the indoor parking system. Dijkstra algorithm initials the paths to finding the shortest path while ACO optimizes the paths. BST has been used to store the paths that Dijkstra algorithm initialled. The proposed algorithm is aimed to control the shortest path as well as guide the driver towards the nearest vacant available space near the entrance. This solution depending on applying the optimization on an optimal path while the traditional ACO is optimizing the random path based on the greedy algorithm hence we get the most optimal path. Moreover, the reason behind using the BST is to make the generation of the path by Dijkstra’s algorithm more accurate and less time performance. The results show a range of 8.3% to 26.8% improvement in the proposed path compared to the traditional Dijkstra’s algorithm.

Research on Composite Web Services Selection Based on Dijkstra Algorithm

2014 ◽  
Vol 596 ◽  
pp. 861-867 ◽  
Author(s):  
Yu Qiang Li ◽  
Yu Wen Li ◽  
Lei Che
Keyword(s):  
Web Services ◽  
Shortest Path ◽  
Feasible Solution ◽  
Optimal Path ◽  
Shortest Path Problem ◽  
Dijkstra Algorithm ◽  
Relevant Theory ◽  
Composite Web Services

Services composing have played an important role in the industry and academia in recent years. Based on the relevant theory and experience of the shortest path problem in a DAG, we propose the method of dijkstra algorithm implementing services composing way selection. Then we provide the pseudo code description of the algorithm implementing the optimal path selecting process and test the correctness of algorithm through the contrast experiment to offer a feasible solution for services composing way selection.

scholarly journals Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147827-147838 ◽  
Author(s):  
Min Luo ◽  
Xiaorong Hou ◽  
Jing Yang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Dijkstra Algorithm ◽  
Optimal Path Planning

Research on Dynamic Path Selection Improved Dijkstra Algorithm

2014 ◽  
Vol 1006-1007 ◽  
pp. 1121-1124
Author(s):  
Xiu Li Gao ◽  
Tian Jun Hu ◽  
Jia Zheng
Keyword(s):  
Path Planning ◽  
Intelligent Transportation System ◽  
Optimal Path ◽  
Path Selection ◽  
Practical Significance ◽  
Dijkstra Algorithm ◽  
Time Dynamic ◽  
Dynamic Path ◽  
Information Dynamic ◽  
Improved Algorithm

Dynamic path selection algorithm is one of the most important researches in Intelligent Transportation System (ITS). After comparing in this article, we select Dijkstra algorithm as the optimal path algorithm. The traditional algorithm applies only to the static road network, the improved algorithm obtained optimal path planning program in the dynamic path selection. Finally, the combination of a numerical example, the improved algorithm for real-time, dynamic, effectiveness is verified through computer simulation. The accuracy of the prediction of the traffic flow plays a key role in the path planning, and the fusion forecast information dynamic path selection problem has important practical significance.

scholarly journals Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

2021 ◽  
Author(s):  
Subhan Khan ◽  
Jose Guivant
Keyword(s):  
Nonlinear Model ◽  
Optimal Path ◽  
Unmanned Ground Vehicle ◽  
Dijkstra Algorithm ◽  
Nonlinear Disturbance Observer ◽  
Ground Vehicle ◽  
Simulation Based ◽  
Processing Resources ◽  
Path Planner ◽  
And Control

Abstract This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.

scholarly journals UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jong-Jin Shin ◽  
Hyochoong Bang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Computation Time ◽  
Pso Algorithm ◽  
Cost Evaluation ◽  
Processing Unit ◽  
Dijkstra Algorithm ◽  
Flight Direction ◽  
Map Generation ◽  
The Cost

This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The t -test results show that the suggested method is statistically better than existing methods.

A NOVEL ALGORITHM FOR EFFECTIVE BALL TRACKING

2010 ◽  
Vol 24 (03) ◽  
pp. 359-379 ◽  
Author(s):  
XIAOFENG TONG ◽  
TAO WANG ◽  
WENLONG LI ◽  
YIMIN ZHANG
Keyword(s):  
Real Time ◽  
Spline Interpolation ◽  
Optimal Path ◽  
Ground Truth ◽  
Distance Graph ◽  
Dijkstra Algorithm ◽  
Wide Range ◽  
Motion Speed ◽  
Ball Tracking ◽  
Object Level

A novel method is proposed to achieve robust and real-time ball tracking in broadcast soccer videos. In sports video, the soccer ball is small, often occluded, and with high motion speed. Thus, it is difficult to detect the sole ball in a single frame. To solve this problem, rather than locate the ball in one of several frames through detection or tracking, we find the ball through optimizing its motion trajectory in successive frames. The proposed method includes three level processes: object level, intra-trajectory level, and inter-trajectory level processing. In object level, multiple objects instead of a single ball are detected and all of them are taken as ball candidates through shape and color features identification. Then at intra-trajectory level, each ball candidate is tracked by a Kalman filter and verified by detection in successive frames, which results in lots of initial short trajectories in a video shot. These trajectories are thereafter scored and filtered according to their length and spatial-temporal relationship in a time-line model. With these trajectories, we construct a distance graph, in which a node represents a trajectory, and an edge means distance between two trajectories. We then get the optimal path using the Dijkstra algorithm in the graph at the inter-trajectory level. The optimal path is composed by a sequence of initial trajectories which make the whole route smooth and long in duration. To get a complete and reasonable path, we finally apply cubic spline interpolation to bridge the gap between adjacent trajectories (the duration corresponding to when the ball is occluded). We select three representative real FIFA2006 soccer video clips (containing a total of 16,500 frames) and manually elaborately labeling each frame in it, and take it as ground-truth to evaluate the algorithm. The average F-score is 80.59%. The algorithm was used in our soccer analysis system and tested on a wide range of real soccer videos, and all the results are satisfied. The algorithm is effective and its whole speed far exceeds real-time, 35.6 fps on mpeg2 data on the Intel Conroe platform.

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