Three dimensional path planning of UAV based on adaptive particle swarm optimization algorithm

Chen, Jie; Zhao, Hongchao; Wang, Lei

doi:10.1088/1742-6596/1846/1/012007

Cited by 19 publications

(12 citation statements)

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“…For the traditional deterministic system, the inertial motion produced by it will show some observable characteristics and eventually return to static. When the external system is excited by a deterministic rule, the response of the system fed back to the outside world should also be deterministic [24]. However, for chaotic systems, this phenomenon is not true, and it may produce unpredictable, irregular, and never-repeated chaotic phenomena after being stimulated by deterministic rules [25].…”

Section: General Analysis and Mathematical Modeling Of Optimization D...mentioning

confidence: 99%

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Zhang

2022

Journal of Function Spaces

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In order to find a chaotic trajectory sequence with strong global optimization ability to help the genetic selection of direction after the reversal of chemotaxis, an improved genetic algorithm based on chaos optimization is proposed by combining the characteristics of chaotic motion with the improved genetic algorithm. The optimal coverage problem in sensor networks can carry out fine optimization search on local areas. The results show that the overall trend of fitness and optimization efficiency is relatively stable. The optimization efficiency will be gradually improved with the continuous progress of time and genetics, and the error analysis will be reduced. This will greatly improve the impact of various adverse factors in the optimization process. In addition, the change rate of fitness is basically kept at a high change rate, which also reflects that the basic framework of the model is very excellent, and the whole algorithm structure and data processing are improved by 54%. The improved genetic algorithm proposed in this paper is used to adjust and optimize the controller parameters. When the uncertain parameters change greatly, the control system still has good control quality and strong robustness.

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Section: General Analysis and Mathematical Modeling Of Optimization D...mentioning

confidence: 99%

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Zhang

2022

Journal of Function Spaces

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“…(3) Weight constraints Because the logistics drone not only needs to send the parts but also the customer's delivery service during the delivery process [14] , it is necessary to accurately ensure that the real -time load of the drone is not greater than the maximum load. Suppose the drone just starts from the logistics point, the total quality is 𝑀, 𝑚 is the weight charged at the logistics office.…”

Section: 𝐻 ≤ ℎ ≤ 𝐻mentioning

confidence: 99%

Research on logistics UAV path planning

shen,

Jin,

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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This article is based on the logistics drone to deliver path planning problems in the urban environment. Combined with the flying characteristics of drones and the distribution location, considering the conditions such as the customer's collection and delivery needs, the logistics drone's delivery path planning model is established. Secondly, the model is processed. For the first time, the NSGA-II algorithm is used to solve the order of the distribution path of the logistics drone, and the second will be solved by improving the traditional algorithm. In the drone execution task, if the weight coefficient of the target functions is adjusted, different transportation sequences and paths can be obtained, and different planning results can be obtained by adjusting the attention of customer satisfaction. The analysis of the algorithm is available. Compared with the traditional A*algorithm, the algorithm transportation path adopted in this article is shorter and the delivery is faster.

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“…To get an optimal solution to reach the target point from the starting point, traditionally, optimization methods have been used in the path planning. Several studies using an A* [33], [34], a genetic algorithm [35], and a particle swarm optimization [36], [37], [38] have been proposed. Combining two optimization methods also has been studied [39].…”

Section: Path Planningmentioning

confidence: 99%

A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

Lee¹

2022

Preprint

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The aim of path planning is to reach the goal from starting point by searching for an agent's route. In the path planning, the routes may vary depending on the number of variables such that it is important for the agent to reach various goals. Numerous studies, however, have dealt with a single goal that is predefined by the user. In the present study, I propose a novel reinforcement learning framework for a fully controllable agent in the path planning. To do this, I propose a bi-directional memory editing to obtain various bi-directional trajectories of the agent, in which the agent's behavior and sub-goals are trained on the goal-conditioned RL. As for the agent's to move in various directions, I utilize the sub-goals dedicated network, separated from a policy network. Lastly, I present the reward shaping to shorten the number of steps for the agent to reach the goal. In the experimental result, the agent was able to reach the various goals that have never been visited by the agent in the training. We confirmed that the agent could perform difficult missions such as a round trip and the agent used the shorter route with the reward shaping.

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Three dimensional path planning of UAV based on adaptive particle swarm optimization algorithm

Cited by 19 publications

References 0 publications

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Research on logistics UAV path planning

A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

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