A Study on Path Planning of Unmanned Aerial Vehicle Based on Improved Genetic Algorithm

Tao, Jihua; Zhong, Chaoliang; Gao, Li; Deng, Hao

doi:10.1109/ihmsc.2016.182

Cited by 12 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tao et al improved the GA by designing a temporary path based on the encoding vector, with each individual guidance including not only the guide point location information but also the status variables in [72]. Thus, it keeps track of whether the guiding point is feasible if it meets the constraint condition, and whether the path between the connecting point and the next guide points had the lowest performance cost.…”

Section: Startmentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learningbased approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

show abstract

Section: Startmentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The authors utilize PSO in [80], ACO in [81], GA in [82], [83] respectively to allocate tasks for the cooperative UAVs. And also, there are many modified and improved versions of these intelligent algorithms further optimizing the performance of the path planning [84], [85] and task allocation [86], [87].…”

Section: (C) Intelligent Algorithmsmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

168

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

show abstract

“…GA can rapidly obtain any solution but it could result in local optimum solution if the algorithm operates in an improperly-defined fitness function [38] as the convergence speed will reduce when it approaches the optimal solution [3], thus making GA computationally expensive and practically incomplete [28]. Algorithm improvement in [42] shows that their method can boost the global search ability of genetic algorithm, as well as improving the quality and accuracy of UAV flight path. GA also being combined with PRM in [43] to solve mobile robot path because as compared to other methods, GA has the potential to look for optimal solutions in a larger search space.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Review on path planning algorithm for unmanned aerial vehicles

Ibrahim

Saparudin

2021

IJEECS

View full text Add to dashboard Cite

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

show abstract

A Study on Path Planning of Unmanned Aerial Vehicle Based on Improved Genetic Algorithm

Cited by 12 publications

References 5 publications

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Review on path planning algorithm for unmanned aerial vehicles

Contact Info

Product

Resources

About