Path planning in unmanned aerial vehicles: An optimistic overview

Shahid, Noor; Abrar, Muhammad; Ajmal, Ushna; Masroor, Roha; Amjad, Shehzad; Jeelani, Mubashir

doi:10.1002/dac.5090

Cited by 19 publications

(22 citation statements)

References 78 publications

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“…Nowadays, new unmanned aerial vehicles are widely used in various industries and fields, and the performance of internal systems is constantly optimized. This paper is based on the new UAV and then studies the UAV mission and path planning [15]. Among them, in the path planning of UAV, adding optimization algorithm to the UAV system can make a simple judgment on the path more accurately [16].…”

Section: Development Status Of Reinforcement Learning Uav In Various ...mentioning

confidence: 99%

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Gao

Liu

et al. 2023

Journal of Function Spaces

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With the rapid development of information technology, various products used in information technology are also constantly optimized. Among them, the task and path planning of UAV in the high-end robot industry has always been the focus of relevant researchers. In the high-end robot industry, in addition to the research and development of UAVs, they also continue to learn and strengthen the task and path planning of UAVs. Nowadays, using unmanned aerial vehicles for real-time shooting has become the trend of this era. Drones have brought great convenience to people’s lives, and more and more people are willing to use drones. Based on the above situation, this paper studies the task and path planning of UAV based on reinforcement learning in dynamic environment. In the case of unpredictable scene parameters, reinforcement learning method can be established by value function. Thus, a more reasonable path can be given to realize the reconnaissance and detection of points of interest. MATLAB simulation experiments show that the algorithm can effectively detect targets in complex terrain composed of terrain restricted areas, and return to the designated end point to complete communication. Firstly, the development of unmanned aerial vehicles in various countries and the social status of unmanned aerial vehicles are discussed. By making UAV build threat model and task allocation in dynamic environment. The path planning and optimization of UAV in dynamic environment is studied, and the path planning algorithm and Hungarian algorithm are added. The optimized UAV has the fastest data transmission and calculation speed, while the other two types of UAVs have slower data transmission and calculation speed. In particular, ordinary UAVs also have data transmission failures, resulting in incomplete experimental results. The results show that the optimized UAV system is better in data calculation and transmission, which also shows that the UAV can quickly plan and process flight paths, which is suitable for practical applications.

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Section: Development Status Of Reinforcement Learning Uav In Various ...mentioning

confidence: 99%

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Gao

Liu

et al. 2023

Journal of Function Spaces

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“…UAVs are becoming more popular, due to technological advances in recent years and practical advantages: high mobility, real-time monitoring and coordination (heavily dependent on system architectures and communication technologies), load transport, access to places that humans cannot reach. Given this wide range of functionalities, a variety of applications can be made possible through IoD: smart agriculture, goods delivery, search and rescue, surveillance systems, data and image collection, telecommunica- Path planning and IoD Shahid et al [24] Comprehensive study of UAV path planning, discussing: environment, dimensions, obstacles, type and number of UAVs and optimization techniques used.…”

Section: Internet Of Dronesmentioning

confidence: 99%

Toward UAV Path Planning Problem Optimization Considering the Internet of Drones

Shirabayashi,

Ruiz

2023

IEEE Access

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The use of Unmanned Aerial Vehicles (UAVs) has become popular in recent years, especially for their potential in various practical applications, but for their use to become a reality this context, it is necessary to study about it. One of the main problems involving UAVs, regardless of the application to which it will be used, is path planning, which is crucial to ensure safety, economy, and effectiveness. In this study we present a literature review on the optimization problem path planning and the methods used to solve it. To this end, we seek to explore the existing papers in literature on this topic, identifying mathematical models, analyzing characteristics of the objective function, types of obstacles, number of UAVs considered, the nature of the solution adopted and deployments and integration in the Internet of Drones (IoD). A comparative analysis of the works analyzed was presented in the form of tables for each path planning technique considered. In addition, some advantages and safety of the methods were also listed. We furthermore present a set of open research challenges, high-level insights, and future research directions related to the UAV path planning problem in the context of IoD. This study contributes deeply with the advancement of state of art regarding the path planning strategies on the Internet of Drones since we provide a thorough analysis of characteristics of the mathematical models used in the UAV path planning problem reviewing papers published in relevant journals and conferences in the last 4 years (2018 to 2022), highlighting the advantages and disadvantages of each method as well as the possibilities of implementation and integration with IoD.INDEX TERMS Internet of drones, Optimization, Path Planning techniques, Unmanned Aerial Vehicles (UAV), IoD architecture. I. INTRODUCTIOND RONES, as known as Unmanned Aerial Vehicles (UAVs), is the most usual way to refer to an aircraft that does not carry a crew. Several terms are used to refer to "drones". Besides UAV, the most common are: unmanned aircraft systems (UAS), remotely piloted vehicles (RPVs), remotely piloted aircraft (RPAs). In recent years, we have observed the increasing popularity and use of UAVs, in different handy applications, such as delivery, transportation, agriculture, monitoring, medical assistance, image capturing, among others [1]-[9]. In the past, the UAVs were mainly used in military applications where one or more drones were used for weather monitoring, soldier recognition, search and rescue. These applications remain and as listed above, several others have emerged, bringing new challenges and the need for more research about them.Currently, there are several studies related to delivery applications with UAVs. Large and well-known companies such as Amazon [10], Google, iFood [11], are investing in research and possibilities to make their deliveries via drones. However, for such implementation to be possible and feasible in the future, it is necessary to organize and coordinate the airspace and drones, since se...

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“…In actual post-disaster mountain rescue scenarios, UAVs encounter various complex problems, such as the complexity of the rescue terrain and environment, obstacle recognition and avoidance, operational feasibility, and safety considerations. These challenges impose stringent requirements on the algorithm's optimization capability and convergence efficiency [31]. Therefore, when UAVs fly in mountainous terrain, it is necessary to consider not only the threats posed by the environment to the UAV but also the performance constraints of the UAV itself and the efficiency of task completion.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

Han,

Yu,

Jiang

et al. 2024

Applied Sciences

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The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.

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Path planning in unmanned aerial vehicles: An optimistic overview

Cited by 19 publications

References 78 publications

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Toward UAV Path Planning Problem Optimization Considering the Internet of Drones

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

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