DeltaQuad’s Post

🚀 Excited to Share My Latest Research and Development Milestone! 📚 I'm thrilled to announce the publication of my second journal article in my PhD journey, focusing on applied research with implications for industry work! 🎉 📝 Title: "A Comprehensive Study on the Aerodynamic Influence of Stationary and Moving Obstacles on an Isolated Phantom DJI 3 UAV Propeller" 📌 Journal: Journal of Engineering In this research, we've delved into the practical implications of aerodynamics, particularly concerning the impact of moving obstacles on the performance of an isolated Phantom DJI 3 drone propeller. Our findings hold significant relevance in the field of UAVs, providing insights to enhance maneuverability and safety in dynamic environments. 🔍 DOI: https://lnkd.in/eSNGSGFi I'm grateful to my supervisors, El-Hassane AGLZIM and Tonino Sophy, for their invaluable guidance and support throughout this industry-focused endeavor. 🙏 #ResearchandDevelopment #UAVs #Aerodynamics #IndustryInsights #Engineering

🚀 Excited to share that my first paper has been published! 📝 Delving into the fascinating realm of Unmanned Aerial Vehicles (UAVs) and Machine Learning, our paper explores the intricacies of "Optimized Route Planning and Precise Circle Detection in UAVs with Machine Learning." 🛸 In this research, we tackle the challenge of optimizing route planning for UAVs while incorporating precise circle detection using advanced machine learning techniques. Our findings shed light on innovative methodologies to enhance UAV operations, paving the way for more efficient and reliable aerial missions. 🎯 I'm incredibly grateful to my co-authors and mentors for their invaluable support throughout this journey. This milestone marks the beginning of an exciting exploration into the intersection of UAVs, Machine Learning, and optimization. 🌐 I invite you to dive into our paper and join the conversation as we continue to push the boundaries of technology and innovation in the field of aerial robotics. Let's soar to new heights together! 🚁💡 #UAV #MachineLearning #Research #Innovation #Optimization

Are you curious about our most recent research findings? 💡 Check out this article about our new method to plan minimum-energy paths for multiple UAVs, allowing them to efficiently cover environments as a team during missions! 🔗 Article link: https://lnkd.in/gFV9RB2Y Our method, featured in IEEE Robotics and Automation Letters, ensures efficient coverage of environments while minimizing energy consumption and optimizing flight speed. 📈 🔗 Check the paper here: https://lnkd.in/dGjFZfSH

📃Scientific paper: Energy-Efficient UAV Swarm Assisted MEC with Dynamic Clustering and Scheduling Abstract: In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts. Continued on ES/IODE ➡️ https://etcse.fr/NQa ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Compared to individual UAVs, UAV teams could cover wider geographical areas, capturing more footage of specific scenes or completing desired missions faster. Many roboticists and computer scientists have thus been working on new technologies that could facilitate the simultaneous deployment and coordination of multiple UAVs. Researchers at Czech Technical University in Prague recently introduced a new method to plan minimum-energy paths for UAVs in a team, allowing them to efficiently cover environments as a team during missions. This method, introduced in IEEE Robotics and Automation Letters, could both minimize the energy consumption of UAV teams, considering the battery capacity of drones when planning their paths and optimize the speed of flight. #uav #drones #robotics Pravo Consulting ,Campaign Catapult

Does a future in drones sound cool to you? Hear how Droneversity is providing an introduction to drones, safety culture, coding, and STEM concepts. Prepare for a panoramic view of aerospace careers: https://bit.ly/3y3C7Bi.

Meet the Quad Squad! As part of the Robotarium at Georgia Institute of Technology, our team Prajwal Bhaskar Bharadwaj, Yash Srivastava, and I, with Dr. Sean Wilson as our advisor are working on building the Quadrotarium: a remotely accessible testbed for quadrotor swarm operations. We understand that UAV research involves a lot of resources, investment, and manpower. We believe that anyone with new ideas should get access to resources to bring those ideas into reality, taking a first step towards that goal in UAV research, we want to open up access to resources through Quadrotarium. Our vision is to let researchers, roboticists, and academicians worldwide send us their experiments. We'll run them on our hardware and share the experiment results and videos. We're also developing a Python simulator to let users validate their experiments before running on the hardware. We're about halfway there and looking to beta test in the next few months. Check out our demo video and poster, and feel free to DM me for more details or questions. Poster link: https://lnkd.in/eWM5ZmVs For more details on Robotoarium, please visit https://lnkd.in/eZ2Ben4B Thank you! #robotics #swarmintelligence #quadcopter #drone #crazyflie #crazyswarm #ros2

"What I cannot create, I do not understand." – Richard Feynman Inspired by this quote, I set out to implement MPC from scratch to gain a deeper understanding of how it balances reaching the goal vs energy efficiency. And as well as how the horizon period helps in achieving optimality.  This is my MPC in action on one of our UAVs. #MAARSLab, where applied robotics research happens!

The Robotarium is a remote-access testbed for swarm robots. It currently houses 20+ ground robots. Users from across the globe can submit experiments to be run on these robots. Once completed, the experiment results are sent back to them. Our team at the Robotarium is currently working on developing systems to run quadrotors along with ground robots, enabling remote users to run hybrid (ground + aerial) robot experiments. Below is a quick update on where we are currently and what we are headed towards.

Pushing the Boundaries of UAV Research: Two Exciting Contributions! I'm excited to share some major milestones from our recent work in UAV research 🚀 1️⃣ ICCES 2024 Presentation: I had the privilege of presenting my paper, "Synergistic Approach for UAV Target Tracking: A Voronoi-GWO-Reinforcement Learning Framework" at the 30th International Conference on Computational & Experimental Engineering and Sciences (ICCES 2024) in Singapore. This research tackles a pivotal challenge in contemporary UAV technology: achieving precision and reliability in target tracking within complex UAV swarm operations. We introduced an advanced framework named Volf-RL (Voronoi-based Grey Wolf Optimization-Reinforcement Learning). This integrative approach not only refines the accuracy of target tracking but also fortifies the system's adaptability and resilience in dynamic, unpredictable environments. Huge thanks to my co-authors Preethika Ajaykumar, Pranamya Bhat, and Neeraj Sudheer for their incredible contributions, and deep gratitude to our mentors Arti Arya and Richa S. for their continued guidance throughout this project. 2️⃣ SN Computer Science Journal Publication: I'm thrilled to announce that we have published another paper titled "UAV Swarm Objectives: A Critical Analysis and Comprehensive Review" in the SN Computer Science journal. This survey paper provides a comprehensive study of the advancements in UAV swarm technology, covering crucial objectives such as Maximal Area Coverage, Path Planning, Intra-swarm Collision Avoidance, Obstacle Avoidance, Swarm Formation, Target Tracking, and Optimal Resource Allocation. Our study spans nearly two decades of research, from 2005 to 2024, and highlights the unique approaches used to achieve these objectives. This survey marks a significant contribution to understanding the evolution and future prospects of UAV swarm technology. You can access the full paper here: https://lnkd.in/g7insH-M These projects represent significant strides in UAV research, and I'm incredibly proud of the collaborative effort and innovative spirit that made them possible. The journey doesn’t stop here—there’s so much more to explore, and I’m excited for what the future holds! Thank you to everyone who has supported us along the way. Onward and upward! #UAV #Research #ICCES2024 #SNComputerScience #ReinforcementLearning #TargetTracking #Optimization #Innovation #MachineLearning #ArtificialIntelligence #Engineering