Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

Savkin, Andrey V.; Verma, Satish Chandra; Ni, Wei

doi:10.1016/j.dt.2023.03.020

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Another interesting topic for future research is applying optimization methods based on the dynamic programming/Bellman optimality principle [149,150] for SUAV navigation. This approach is especially promising in various problems of joint navigation/sensor data transmission control that are similar to the problems studied in [125,145].…”

Section: Machine Learning Techniquesmentioning

confidence: 98%

“…This allows UAVs to conduct efficient surveillance on uneven terrain. In [125], the proposed method enhances data transmission efficiency over uneven terrain, reduces energy consumption and time delays, and takes into account time limits of ground sensors and data-gathering concerns. Further research could be conducted considering SUAVs are deployed over uneven terrain, where RIS technologies might offer significant assistance.…”

Section: Suavs On Uneven Terrainmentioning

confidence: 99%

See 1 more Smart Citation

Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review

Li,

Fang,

Verma

et al. 2024

Drones

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Unmanned aerial systems and renewable energy are two research areas that have developed rapidly over the last few decades. Solar-powered unmanned aerial vehicles (SUAVs) are likely to become dominant in the near future. They have the advantage of low cost and safe operation features that mitigate the barriers to their use in various environments. Developing effective algorithms for navigating and deploying SUAVs is essential for implementing this technology in real-life applications. Effective navigation and deployment algorithms also ensure the safety and efficiency of SUAV operations. This comprehensive review paper summarizes some state-of-the-art SUAV applications and provides an overview of the navigation and deployment algorithms for SUAVs. Some commonly used energy-harvesting models are described as well. Finally, some interesting and promising directions for future SUAV research are suggested.

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Section: Machine Learning Techniquesmentioning

confidence: 98%

Section: Suavs On Uneven Terrainmentioning

confidence: 99%

Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review

Li,

Fang,

Verma

et al. 2024

Drones

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their classification of the optimization parameters either as primary, i.e., path planning, or secondary, i.e., altitude and UAV speed, is key to their process. Savkin et al [21] present an autonomous UAV in a 3D trajectory optimization and transmission scheduling on uneven terrains. The work considers collision avoidance with multiple cooperating UAVs.…”

Section: Related Work Reviewmentioning

confidence: 99%

Deployment of an autonomous fleet of UAVs for Assessing the NDVI of Regenerative Farming

Almalki,

Angelides

2023

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)

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F aris A. Al m al ki 1, M ari os C. A n g eli d es 2 1 D e p art m e nt of C o m p ut er E n gi n e eri n g, C oll e g e of C o m p ut ers a n d I nf or m ati o n T e c h n ol o g y , T aif U ni v ersit y , T aif, S a u di Ar a bi a E m ail: m.f aris @t u. e d u.s a 2 Br u n el D esi g n S c h o ol, C oll e g e of E n gi n e eri n g, D esi g n a n d P h ysi c al S ci e n c es , Br u n el U ni v ersit y L o n d o n , U x bri d g e, U nit e d Ki n g d o m E m ail: m ari os. a n g eli d es @ br u n el. a c. u k

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“…However, existing research methods exhibit numerous flaws and limitations. For instance, in the detection of anomalous interactions, traditional approaches might not effectively differentiate between normal fluctuations and actual anomalies, leading to false positives or negatives [13][14][15][16]. Additionally, current data transmission optimization techniques often overlook the unique data characteristics of online education, such as the high temporal relevance of interaction data.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

Jinjin Wang

2024

Int. J. Interact. Mob. Technol.

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The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.

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Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

Cited by 5 publications

References 12 publications

Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review

Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review

Deployment of an autonomous fleet of UAVs for Assessing the NDVI of Regenerative Farming

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

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