Drone Helpline’s Post

99% of customers who used Jianfan Thunderstone series solid state lithium ion battery products say V.g.,  What's advantage about it? It will allow drones to increase endurance at most 35% longer than traditional lithium batteries. Are you looking for a lighter and smaller solid state lithium ion battery with longer endurance? Congratulations, you found Jianfan Technology.The research and development team of Jianfan Technology launched thunderstone high specific energy series 16000-30000mah solid lithium ion battery in 3 years, which can effectively prolong the flight time of UAV by 20%-35%. The series of solid state lithium batteries have been widely used in vTOL , multi-rotor UAV, and other industrial UAVs. Dr. Huang gives you an example: Our client XDZX (pseudonym) made up his mind to replace his original 25000mAh lithium polymer battery with Jianfan Thunderstone battery after carrying two sets of 30000mAh solid state lithium ion batteries in his six-rotor drone with an empty payload of 12kg for the first test flight. The reason for this is that flight time has been extended from 60 minutes (on lithium polymer batteries) to 83 minutes (on Jianfan Thunderstone batteries), which is about 38% better than before!If you are also looking for a battery replacement solution for UAV, please contact Jianfan Technology. You only need to provide the required battery size, UAV load and specifications, and the professional team of Jianfan Technology will provide you with an efficient and feasible solution for using Jianfan solid state lithium ion battery. At the same time, Jianfan technology always uphold the concept of resource sharing, open cooperation, welcome distributors/friends to contact Jianfan technology, in-depth understanding of our company's more competitive Thunderstone battery series, with a view to mutual benefit and win-win!78. #uav #multicopter #heavylift #drone #dronedelivery #dronesurvey #aviation #dronenews #endurance #solidbattery #uavbattery

Toronto Metropolitan University (TMU) researchers show how advanced robotic mapping techniques like fuzzy mapping and vSLAM improve drone landing in tough environments. In their research paper recently published in the special issue Perception, Decision-Making and Control of Intelligent Unmanned System in the journal Drones, TMU co-authors professor Farrokh Janabi-Sharifi & PhD candidate Niloufar Amiri explore an innovative perception-guided approach for landing zone detection and realization of Unmanned Aerial Vehicles (UAVs) operating in unstructured environments riddled with obstacles. National Research Council Canada / Conseil national de recherches Canada Faculty of Engineering and Architectural Science at Toronto Metropolitan University #research #innovation #stem #engineering #mechtronics #robotics

Investigation of Frequency-Domain-Based Vibration Signal Analysis for UAV Unbalance Fault Classification https://lnkd.in/dUPDzRDM Abstract The flying reliability of Unmanned Aerial Vehicles (UAVs) and flying robots, which directly determines the operational degree of safety, is becoming more important in recent intelligent decades. Reliability and a high level of safety are critical for autonomously controlled flying robots, especially in transportation and entertainment applications. Subsequently, monitoring UAV health is crucial and essential for system safety, cost savings, and excellent dependability. The development of numerous monitoring strategies has resulted from the requirement for a simple and accurate unbalance classification procedure. This paper provides an Unbalance Classification and Isolation (UCI) system for multirotor UAV propeller impairments. The technique is based on the processing of signal vectors from a vibration sensor positioned in the lines of the intersection of a modern-day drone's four propulsion units, which supply data for the Fast Fourier Transform (FFT) feature extraction. To identify and locate broken blades, characteristic fault signatures collected from vibration signals are employed and displayed in real-time on the programming platform. A noticeable maximum frequency shifting percentage value of 4.2% is acquired when deviating from a healthy state. The results reveal that identifying and isolating defective rotor states has high sensitivity and outperforms current studies in regard to unbalance classification of UAVs. The adopted technique is an efficient and low-cost solution that can be implemented in any multirotor UAV. Highlights: • Small UAV drone vibration signals are gathered utilizing the LabVIEW DAQ assistant. • Spectral analysis of Fast Fourier Transform signal processing technique in x, y, and z was performed. • Unbalance fault classification based on FFT peak frequencies of rolling, pitching, and yawing in a DJI mini 2 combo was used. • Real-time UAV health monitoring method was discussed and explained accordingly. Keywords: • Signal Processing • Vibration Signal Analysis • Fast Fourier Transform • UAV • Unbalance Classification Journal: https://lnkd.in/dgnvtdte Issue: https://lnkd.in/dVAN6w_5 Article: https://lnkd.in/eET-8-qG ETJ LinkedIn: https://lnkd.in/d_8SPqAt #Engineering_and_Technology_Journal #UOT #engineering #technology #etj

The drone industry is booming, and we must equip learners with the right skills to excel in it. Scientech Drone Labs provide a customizable, interdisciplinary, and industry-aligned learning experience—empowering students and professionals with the expertise to excel in UAV technology. Why Choose Scientech? ✅ Customizable Solutions: Tailored to meet the unique needs of institutions. ✅ Interdisciplinary Approach: Covers fields like Mechanical Engineering, Avionics, Electronics, and Wireless Communication. ✅ Proven Expertise: Trusted by colleges, research institutions, RPTOs, CoEs, and defence organizations. ✅ Future-Ready Skills: Hands-on training in UAV assembly, GIS applications, payload integration, and manufacturing. As drones transform industries from agriculture to defence, the need for skilled professionals has never been greater across the board. Scientech is bridging this gap with cutting-edge labs that bring learning to life. Are you ready to shape the future of UAV technology? Let’s build the next generation of drone experts—together. #drones #dronelabs #UAV #emergingtechnologies #skills #skilldevelopment #training #practicaltraining Satish Thakare Ambrish Kela Uday Bhole Nikhil Kela Satyendra Pawar Dr. Chaitanya Dev Singh Sisodiya Tanvi Kela Dronefluence National Skills Network - NSN

🚁 𝗗𝗿𝗼𝗻𝗲 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻: 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗣𝗮𝘁𝗵-𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗶𝗻 𝗚𝗮𝘇𝗲𝗯𝗼 I’m thrilled to share the results of my latest UAV simulation! Using 𝗣𝗫𝟰 𝗦𝗜𝗧𝗟, 𝗠𝗔𝗩𝗥𝗢𝗦, and 𝗚𝗮𝘇𝗲𝗯𝗼, I developed a system for a drone to execute a smooth circular trajectory, guided by a 𝗴𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿. The results? Take a look at the attached video! It showcases the drone’s precise path-following capabilities, visualized in 𝗥𝗩𝗶𝘇, with every movement adhering beautifully to the planned trajectory. 𝗧𝗵𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲𝘀: 𝗣𝗫𝟰 𝗙𝗶𝗿𝗺𝘄𝗮𝗿𝗲 for flight dynamics and control. 𝗠𝗔𝗩𝗥𝗢𝗦 for seamless ROS integration and command communication. 𝗥𝗩𝗶𝘇 for real-time trajectory visualization. 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: 𝗧𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Sub-centimeter deviation from the planned circular path. 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗶𝗱𝗲𝗹𝗶𝘁𝘆: Achieved high stability under simulated environmental conditions. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Efficient data exchange between MAVROS and PX4 ensured robust control feedback. This project highlights the importance of simulation tools in UAV development, allowing extensive testing of control algorithms in a safe and repeatable environment. The video attached demonstrates the drone's smooth adherence to the circular path, showcasing the potential for precise autonomous flight. If you're working on UAV simulations or have insights into improving control performance, I’d love to discuss and exchange ideas! #PX4 #MAVROS #UAVSimulation #GeometricControl #Gazebo #RViz #PathPlanning #DroneEngineering

Magnetic levitation (maglev) technology in UAV propeller motors is a groundbreaking concept that aims to reduce mechanical friction and wear by suspending the rotor using magnetic fields instead of traditional bearings. By eliminating direct contact between rotating parts, maglev motors offer smoother operation, quieter performance, and longer lifespans, which are particularly advantageous in UAV applications where precision, efficiency, and reliability are crucial. How Maglev Works in UAV Propeller Motors In a maglev motor: • Permanent Magnets (Symbol: N-S poles on a circle) are arranged on the rotor. • Electromagnets (Symbol: coiled lines with an “M” representing magnet) are placed on the stator. • These magnets create a Magnetic Field (Symbol: curved lines with arrows), which generates a lifting force that suspends the rotor within the stator. • An Active Control System adjusts the magnetic field in real-time, keeping the rotor stable and centered. Advantages of Maglev in UAV Motors 1. Reduced Friction and Wear: No physical contact means less energy lost to friction, leading to higher efficiency and less wear, extending the motor’s lifespan. 2. High Precision and Stability: The absence of mechanical bearings results in smoother operation, crucial for stabilizing UAV flight in windy or challenging conditions. 3. Lower Noise: With no physical contact between parts, maglev motors are quieter, beneficial for reconnaissance and stealth UAV applications. 4. Enhanced Durability: Maglev motors are highly resistant to mechanical stress, making them suitable for high-speed and high-endurance UAVs. #MaglevTechnology #UAVMotor #PropellerInnovation #MagneticLevitation #AdvancedAviation #DroneTech #QuietOperation #HighEfficiency #FrictionlessMotor #AdvancedUAV

How do #UAVs in a swarm collaborate to solve problems considering reliable computation and communication? https://lnkd.in/gbC_3iTa Paper: Reliability-Critical Computation Offloading in UAV Swarms Abstract: The rapid advancement of #autonomous and #heterogeneous #UnmannedAerialVehicle (UAV) swarms necessitates efficient #ComputationOffloading (CO) strategies to optimize their performance in industries, e.g., disaster management, surveillance, and environmental monitoring. UAVs face constraints such as limited #energy, #latency requirements, and failure risks, making #robust CO approaches essential. Current CO methods often fall short due to high energy consumption, increased latency, and #reliability issues in challenging conditions. This work introduces a novel collaborative CO strategy to address these deficiencies. Our approach utilizes a #BayesianNetwork for failure mode effect analysis, considering #communication bit error probabilities among #MultiAntenna UAVs. We further employ rating-based #FederatedDeepLearning to optimize decision-making, determining the best CO destination for each UAV based on factors like positions and resource capacities. Our strategy significantly outperforms existing benchmarks and state-of-the-art methods. It decreases the average probability of critical task #failure by 43% and reduces #energy consumption by 15% on average ensuring UAV swarms can meet strict constraints in harsh environments. These improvements demonstrate the utility of our approach in enhancing the operational reliability and efficiency of UAV swarms across diverse applications. Authors: Dr. Dadmehr Rahbari, Dr. Foisal Ahmed, Prof. Maksim Jenihhin, Prof. Muhammad Mahtab Alam, Prof. Yannick Le Moullec Funders: * Horizon 2020 European Research Aera (ERA)-chair Grant ‘COgnitive ELectronics (COEL) * TalTech – Tallinn University of Technology Development Program 2016-2022 (2014-2020.4.01.16-0032) * ETAG - Estonian Research Council PRG1467 CRASHLESS project Publisher: IEEE Systems Journal

Dear Colleagues and Friends, It is my great pleasure to share with you on this latest publication from our research group. B. Yang, E. Yang, L. Yu and C. Niu, "Sensor Fusion-Based UAV Localization System for Low-Cost Autonomous Inspection in Extremely Confined Environments," in IEEE Sensors Journal, vol. 24, no. 14, pp. 22144-22155, 15 July15, 2024, doi: 10.1109/JSEN.2024.3355103. Abstract: Utilizing the unmanned aerial vehicle (UAV) for autonomous inspection in extremely confined environments has become a much sought research area, due to the pressing industrial needs. To provide the high-accuracy position information of UAV in such environments, the ultrawideband (UWB)-based localization technology has been one of the ideal candidates. However, the unpredictable propagation condition and the geomagnetic disturbances for inertial measurement unit (IMU) will all have huge impact on the positioning performance with the single UWB and the IMU/UWB-based loosely coupled (LC) sensor fusion approach. Therefore, a tightly coupled adaptive extended Kalman filter (TC-AEKF)-based sensor fusion UAV localization system is proposed and developed in this article for autonomous inspection in extremely confined environments. The proposed system can attain high-accuracy localization of UAV with 0.097-m median error, 0.167-m 95th percentile error, and 0.039-m average standard deviation (STD). The drift led by the geomagnetic disturbances for IMU is greatly reduced with the estimation error of the roll, pitch, and yaw angle to be 2.15°, 1.54°, and 4.58°, respectively. Finally, an autonomous inspection experiment has been conducted to prove the effectiveness of the proposed system and algorithm for actual application, and the video from it has been attached in https://lnkd.in/ety_i8af. keywords: Autonomous aerial vehicles;Sensor fusion;Inspection;Sensors; Covariance matrices; Costs; Distance measurement; Autonomous inspection;extremely confined environments;inertial measurement unit (IMU);tightly coupled sensor fusion; ultrawideband (UWB); unmanned aerial vehicle (UAV) Many thanks indeed for your attention and interests! Kind regards, Erfu

Accurately predicting the trajectories of agile aerial targets in 3D space (e.g. for interception tasks) is one of the most challenging tasks in robotics. A cornerstone of this process is the precise and reliable prediction of the target’s future motion. At the Robotics and Internet of Things Lab, Prince Sultan University, we have been working on the #iHunter system—a fully autonomous multirotor UAV designed for interception missions. This sophisticated system integrates real-time perception, multi-target state estimation, AI-driven trajectory prediction, and advanced interception strategies. I am thrilled to share our recently published work on one of iHunter’s primary components, which focuses on accurate, real-time 3D UAV trajectory prediction. Titled: VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction Highlights of VECTOR: ✅ High Accuracy: Achieves a remarkable 0.4m RMSE in real-world outdoor tests. ✅ Real-Time Performance: Performs inference at over 100Hz on NVIDIA Jetson edge devices. ✅ Sim2Real Transfer: Trained using both open-source UAV datasets and our own dataset of 5,000 synthetic trajectories, ensuring robust and reliable performance across scenarios. This work is a testament to the incredible collaboration and innovation of my team at the Robotics and Internet of Things Lab at Prince Sultan University. Here is a demo video : https://lnkd.in/dbdKD3FV   Resources for the Community: 📂 Dataset: A comprehensive dataset of 5,000 3D UAV trajectories: [https://lnkd.in/dSY7dvyD] 🛠 Visualization Tool: Drone trajectory processing and visualization: [https://lnkd.in/d6_d6CrA] 🛠 ROS2 Integration: A ROS2 package for seamless UAV system integration: [https://lnkd.in/dSFgKE4e] 📃 Research Paper: Read our full study here: [https://lnkd.in/dxDZSjxt] We are also excited to announce the release of an open-source framework for automatic UAV dataset generation, recently presented at #SMARTECH2024. Stay tuned for more details in an upcoming post! I am deeply grateful to my co-authors and collaborators OMER NACAR - M.Sc., Dr. Lahouari Ghouti, Khaled Gabr, Abdulrahman S. Al-Batati, Anis Koubaa for their hard work and contributions. #UAV #AI #TrajectoryPrediction #Robotics #Drones #VECTOR #ResearchInnovation #PrinceSultanUniversity #RIOTU

𝗚𝗹𝗼𝗯𝗮𝗹 𝐒𝐨𝐥𝐚𝐫-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐔𝐀𝐕 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐩𝐨𝐫𝐭 (𝟐𝟎𝟐𝟒-𝟐𝟎𝟑𝟐) The global solar-powered UAV market size reached US$ 368.1 Million in 2023. Looking forward, IMARC Group expects the market to reach US$ 814.4 Million by 2032, exhibiting a growth rate (CAGR) of 8.95% during 2024-2032. The increasing demand for drone services for various commercial applications, the recent development of UAV drones for asset management, tracking enemies’ movements and performing dangerous battlefield surveillance operations in the defense sector, and extensive research and development (R&D) activities represent some of the key factors driving the market. 𝐆𝐫𝐚𝐛 𝐚 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅: https://lnkd.in/gUVfWriu 𝐌𝐚𝐫𝐤𝐞𝐭 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬: ● 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐁𝐨𝐨𝐬𝐭𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲: Integration of advanced AI and machine learning in autonomous flight systems for solar-powered UAVs is driving market growth. This enables complex missions with minimal human input, ideal for hazardous environments. ● 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 & 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Autonomous solar-powered UAVs benefit from AI-powered route planning and energy management. This optimizes solar power usage, extending mission durations and improving overall efficiency. ● 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: The ability of autonomous solar-powered UAVs to adapt to changing weather and solar intensity through AI enhances reliability and performance, fueling market growth. 𝐓𝐨𝐩 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐒𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐆𝐥𝐨𝐛𝐚𝐥 𝐒𝐨𝐥𝐚𝐫-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐔𝐀𝐕 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲: BAE Systems Plc Barnard Microsystems Ltd EOS Technologie Sunlight Aerospace Inc. UAV Instruments S.L XSun 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/gGj9-HnM #solarpowereduavmarket #marketresearch #business #marketanalysis #markettrends #researchreport #marketreport #marketforecast #marketgrowth #imarcgroup #businessgrowth #investmentopportunity #industryanalysis