Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Raivi, Asif Mahmud; Huda, S. M. Asiful; Arafat, Muhammad Yeasir; Moh, Sangman

doi:10.3390/s23031463

Cited by 23 publications

(12 citation statements)

References 81 publications

(144 reference statements)

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“…The trajectory planning of a UAV in UAV-aided edge computing is an emerging topology, and many studies have focused on it because without proper trajectory planning, it is not possible to achieve the main purpose of edge computing [ 55 ]. The trajectory is the path followed by the UAV while processing computational tasks.…”

Section: Background Studymentioning

confidence: 99%

“…The trajectory is the path followed by the UAV while processing computational tasks. Trajectory planning is a strategic trajectory designed to optimize data processing, acquisition, and communication [ 55 , 56 ]. It plays a crucial role in strategically positioning the UAV to collect relevant information at key points along its flight path.…”

Section: Background Studymentioning

confidence: 99%

“…Obstacle avoidance: This involves navigating UAVs around physical obstacles, such as trees, buildings, other aircraft, and other structures in the environment [35]. Obstacle avoidance strategies in trajectory planning are extremely crucial for ensuring the integrity and safety of UAV operations [82]. By dynamically and intelligently navigating around obstacles, trajectory planning can optimize the path of the UAV, minimize the consumed energy and risk of collisions, and enhance the overall system reliability.…”

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confidence: 99%

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Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

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Section: Background Studymentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

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confidence: 99%

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Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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“…UAV detection can also help the airport to monitor and manage UAV activities around the airport and maintain the order and safety of the airspace [4]. In areas with high confidentiality, such as government agencies or military bases, UAV detection can help government agencies prevent UAV snooping or threats to important people or occasions and protect the interests and security of the country [5]. Therefore, 2 of 20 UAV detection is a key technology that can help us find, locate, track, and manage UAVs to protect the interests and security of society and the country [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Fusion Uncrewed Aerial Vehicle Detection Based on RT-DETR

Zhu,

Kong

2024

Electronics

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With the rapid development of science and technology, uncrewed aerial vehicle (UAV) technology has shown a wide range of application prospects in various fields. The accuracy and real-time performance of UAV target detection play a vital role in ensuring safety and improving the work efficiency of UAVs. Aimed at the challenges faced by the current UAV detection field, this paper proposes the Gathering Cascaded Dilated DETR (GCD-DETR) model, which aims to improve the accuracy and efficiency of UAV target detection. The main innovations of this paper are as follows: (1) The Dilated Re-param Block is creatively applied to the dilatation-wise Residual module, which uses the large kernel convolution and the parallel small kernel convolution together and fuses the feature maps generated by multi-scale perception, greatly improving the feature extraction ability, thereby improving the accuracy of UAV detection. (2) The Gather-and-Distribute mechanism is introduced to effectively enhance the ability of multi-scale feature fusion so that the model can make full use of the feature information extracted from the backbone network and further improve the detection performance. (3) The Cascaded Group Attention mechanism is innovatively introduced, which not only saves the computational cost but also improves the diversity of attention by dividing the attention head in different ways, thus enhancing the ability of the model to process complex scenes. In order to verify the effectiveness of the proposed model, this paper conducts experiments on multiple UAV datasets of complex scenes. The experimental results show that the accuracy of the improved RT-DETR model proposed in this paper on the two UAV datasets reaches 0.956 and 0.978, respectively, which is 2% and 1.1% higher than that of the original RT-DETR model. At the same time, the FPS of the model is also improved by 10 frames per second, which achieves an effective balance between accuracy and speed.

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“…Trajectory planning for multicopters has been studied extensively in recent years due to the increasing demand of drones in many fields, from entertainment and daily life to sophisticated professional applications [1]. Therefore, it is a very important task to plan a feasible trajectory along which a drone can fly safely while executing a mission.…”

Section: Introductionmentioning

confidence: 99%

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Gelli,

Bigazzi,

Boni

et al. 2024

Drones

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In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.

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Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Cited by 23 publications

References 81 publications

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Multi-Scale Fusion Uncrewed Aerial Vehicle Detection Based on RT-DETR

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

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