Competitive Self-Organizing Neural Network Based UAV Path Planning

Gao, Mingsheng; Wei, Pengfei; Liu, Yuxiang

doi:10.1109/iccc51575.2020.9344904

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional classical algorithms include: RRT method 3 , A* algorithm 4 , PRM method 5 , D* method 6 , Artificial Potential Field (APF) 7 8 and so on. Modern intelligent algorithms include: genetic algorithm 9 , ant colony algorithm 10 11 , neural network 12 and so on. Ant colony algorithm is easy to implement, meanwhile it can be combined with other algorithms 13 .…”

Section: Introductionmentioning

confidence: 99%

A dense formation control method for UAVs based on improved ant colony algorithm

Gao¹,

Zeng²

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

View full text Add to dashboard Cite

The classical Ant Colony Optimization (ACO) has some problems such as slow convergence speed and easy to fall into local optimum, a control method for dense formation of UAVs is proposed in this thesis. Firstly, cubic mapping is used to initialize the ACO distribution for making full use of map information and avoiding falling into local optimum; Secondly, the pheromone concentration updated by reward and punishment mechanism. Adaptive pheromone volatility factor is used to balance the global search and local search, as well as accelerate the convergence of the algorithm; Then, the artificial potential field is combined with the ACO to optimize the obstacle avoidance path. Finally, the state information from the dense formation of UAVs is corrected according to the consistency control theory, and the formation consistency control model is designed to realize the dense formation control. The experimental results shows that the proposed algorithm can achieve obstacle avoidance while maintaining tight formation, the formation can converge to the desired formation quickly after the obstacle avoidance is completed.

show abstract

Section: Introductionmentioning

confidence: 99%

A dense formation control method for UAVs based on improved ant colony algorithm

Gao¹,

Zeng²

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

View full text Add to dashboard Cite

show abstract

An improved whale optimization algorithm for UAV swarm trajectory planning

Li,

Gao,

et al. 2024

Adv Cont Discr Mod

View full text Add to dashboard Cite

The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning

Kang

Chen

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In order to improve the obstacle avoidance and endurance capability of quadrotor UAVs performing tasks such as forest inspection and rescue search, this paper proposes improvements to address the problems of too many traversed nodes, too many redundant corners, too-large turning angles and unsmooth generated paths in the traditional A* algorithm in path planning. The traditional A* algorithm is improved by using a segmented evaluation function with dynamic heuristic and weighting processing, a steering cost heuristic function based on heading angle deviation control, a redundant turning points removal strategy, and a quasi-uniform cubic b-spline. Through the test comparison of different complexity map scenarios, it is found that the improved A* algorithm reduces the number of traversed nodes by 64.87% on average, the total turning angle by 54.53% on average, the path search time by 49.64% on average, and the path length by 12.52% on average compared to the traditional A* algorithm, and there is no obvious turning point in the path. The real-world applicability of the improved A* algorithm is confirmed by comparing the effect of different algorithms on obstacle avoidance in a map of a real plantation forest environment.

show abstract

Competitive Self-Organizing Neural Network Based UAV Path Planning

Cited by 4 publications

References 14 publications

A dense formation control method for UAVs based on improved ant colony algorithm

A dense formation control method for UAVs based on improved ant colony algorithm

An improved whale optimization algorithm for UAV swarm trajectory planning

The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning

Contact Info

Product

Resources

About