scholarly journals A short-distance healthy route planning approach

2022 ◽  
Vol 24 ◽  
pp. 101314
Author(s):  
Li-Na Gao ◽  
Fei Tao ◽  
Pei-Long Ma ◽  
Chen-Yi Wang ◽  
Wei Kong ◽  
...  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xuzheng Zhang ◽  
Yifei Meng ◽  
Chenxiao Mao ◽  
Yaohua Xu ◽  
Na Bai

There are two primary defects in the existing UAV avoidance systems: the system is memoryless; airborne radars are used to detect long-distance barriers, which are unreliable and expensive. The paper adopts the deep learning algorithm and ADS-B communication system based on a satellite base station to solve the above problems. It divides the avoidance problem into two parts: short-distance obstacle avoidance and long-distance route planning. On the one hand, the system establishes the knowledge base storing the previous avoidance experience and the matching mechanism, realizing the correspondence between input and experience through a deep learning algorithm. They can dramatically improve the reaction speed and safety of UAVs. On the other hand, the system realizes the interconnection between UAV and the satellite base station through the ADS-B communication system to replace the radars, putting the task of route planning on the satellite platform. Therefore, the satellite can achieve large-scale and all-weather detection to improve the overall safety of UAVs depending on its high and long-range characteristics. The paper also illustrates the design elements of the RF baseband integrated ADS-B transceiver and the simulation performance of the short-distance avoidance system in the end, whose results show that the system can be applied to dense obstacle environments and significantly improve the security of UAVs in a complex domain.


Robotica ◽  
2020 ◽  
pp. 1-16
Author(s):  
Xiaofeng Liu ◽  
Jian Ma ◽  
Dashan Chen ◽  
Li-Ye Zhang

SUMMARY Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.


2021 ◽  
Vol 10 (8) ◽  
pp. 530
Author(s):  
Mohamed A. Damos ◽  
Jun Zhu ◽  
Weilian Li ◽  
Abubakr Hassan ◽  
Elhadi Khalifa

One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 39536-39547
Author(s):  
Yanmei Zhang ◽  
Linjie Jiao ◽  
Zhijie Yu ◽  
Zheng Lin ◽  
Mengjiao Gan

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