Path Planning for Aircraft Based on MAKLINK Graph Theory and Multi Colony Ant Algorithm

Hao, Yue; Zhi-feng, Shen; Zhao, Yuxin

doi:10.1109/cso.2009.315

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…DA was widely used in the free space methods to find the connected domain from the starting point to the end point. 16,[22][23][24][25][26][27][28] . In DA-based methods, usually the midpoints of two free links are connected and the length of the connected line is set as the weight to choose the better connected domain.…”

Section: Comparison With the Other Free Space Methodsmentioning

confidence: 99%

“…22 After that, various optimization algorithms have been used to optimize the path obtained by DA, like particle swarm optimization (PSO), simulated annealing algorithm, ant colony algorithm, genetic algorithm (GA), and so on. 16,[23][24][25][26][27][28] These above DA-based methods search feasible path just in one of the connected domains; however, the global optimal path is not necessarily right in this connected domain. There is such a situation that a local optimal path is obtained in a certain connected domain while the global optimal one is in another domain.…”

Section: Introductionmentioning

confidence: 99%

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A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

Zhang

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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Free space algorithms are kind of graphics-based methods for path planning. With previously known map information, graphics-based methods have high computational efficiency in providing a feasible path. However, the existing free space algorithms do not guarantee the global optimality because they always search in one connected domain but not all the possible connected domains. To overcome this drawback, this article presents an improved free space algorithm based on map decomposition with multiple connected domains and artificial bee colony algorithm. First, a decomposition algorithm of single-connected concave polygon is introduced based on the principle of concave polygon convex decomposition. Any map without obstacle is taken as single-connected concave polygon (the convex polygon map can be seen as already decomposed and will not be discussed here). Single concave polygon can be decomposed into convex polygons by connecting concave points with their visible vertex. Second, decomposition algorithm for multi-connected concave polygon (any map with obstacles) is designed. It can be converted into single-connected concave polygon by excluding obstacles using virtual links. The map can be decomposed into several convex polygons which form multiple connected domains. Third, artificial bee colony algorithm is used to search the optimal path in all the connected domains so as to avoid falling into the local minimum. Numerical simulations and comparisons with existing free space algorithm and rapidly exploring random tree star algorithm are carried out to evaluate the performance of the proposed method. The results show that this method is able to find the optimal path with high computational efficiency and accuracy. It has advantages especially for complex maps. Furthermore, parameter sensitivity analysis is provided and the suggested values for parameters are given.

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Section: Comparison With the Other Free Space Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

Zhang

Liu

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…The MAKLINK graph method is one of the better methods for this function. 20,21 The following assumptions are made to simplify the problem of establishing the planning space.…”

Section: Free Space Model Based On the Maklink Graph Methodsmentioning

confidence: 99%

A novel small-world algorithm incorporating chaos optimization and its application to robot path planning

Yuan

Wang

et al. 2012

Proceedings of the Institution of Mechanical Engineers, Part I:

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By making use of the characteristics of ergodicity, randomicity and regularity of chaotic variables and information entropy, a novel chaotic small-world algorithm is presented to improve the optimization performance of the simple small-world algorithm. Compared with the corresponding simple small-world algorithm and the modified genetic algorithm approach, the optimization results of selected complex functions indicate that the proposed chaotic small-world algorithm is characterized by a strong search capability and a quick convergence speed. A study of parameter performance of the chaotic small-world algorithm aids in further improvement of its optimization capability. Additionally, the chaotic small-world algorithm is applied to mobile robot path planning, and the global path is optimized by the chaotic small-world algorithm based on a MAKLINK graph. Finally, experimental results verify the validity of the chaotic smallworld algorithm for robot path planning.

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“…UAV path planning is to plan a flight path that conforms to the constraints of UAV maneuverability and minimizes the comprehensive cost under the premise of comprehensively considering time, distance, energy consumption and other factors. Because of the complexity of the actual environment, planning an optimal flight path from the start to the end can effectively ensure the safety of the UAV and improve the efficiency of the task execution [3][4] .…”

Section: Introductionmentioning

confidence: 99%

UAV path planning based on improved ant colony algorithm

2023

Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023)

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The traditional ant colony algorithm is inefficient to search, easy to fall into algorithm stagnation and local optimization problems in UAV path planning. To ensure that the UAV can avoid obstacles and fly safely, the ant colony algorithm is improved and optimized. First, the target planning area of the UAV is modeled in three dimensions using raster method. Secondly, the update rules of pheromones are improved, and the weight factors of pheromones and heuristics are adjusted. An unmanned aerial path planning algorithm based on improved ant colony algorithm is proposed to plan a safe and optimal path for the unmanned aerial vehicle. Finally, the simulation results show that the improved algorithm has a better flight path than the traditional algorithm.

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Path Planning for Aircraft Based on MAKLINK Graph Theory and Multi Colony Ant Algorithm

Cited by 10 publications

References 4 publications

A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

A novel small-world algorithm incorporating chaos optimization and its application to robot path planning

UAV path planning based on improved ant colony algorithm

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