AUTOASSESS’ Post

The use of data from different sources can be critical in emergencies, where accurate models are needed to make real-time decisions, but high-fidelity representations and detailed information are unavailable. Recently, I have been developing some preliminary data assimilation solutions with my colleague Jacopo Bonari and the support of Daniel Lichte and Prof. Alexander Popp. I uploaded the ArXiv pre-print and will present the work at the 16th Symposium Sensor Data Fusion. This current computational prototype presents a data assimilation framework based on an ensemble Kalman filter that sequentially exploits and improves an advection-diffusion model of an airborne contaminant dispersion problem over a chemical plant. An autonomous aerial drone is routed according to the available knowledge at each decision time and sequentially observes the actual contaminant concentration in a small fraction of the non-simply connected domain, orders of magnitude smaller than the total domain area. Such observations are synchronized with the data assimilation framework, iteratively adapting the simulation. From an erroneous initial model based on approximated assumptions that represent the limited initial knowledge available during emergency scenarios, results show how the proposed framework sequentially improves its belief about the dispersion dynamics, thus providing a reliable contaminant concentration map. Several improvements are still needed in the framework components to be able to do more real testing, and we are working on them right now. link: https://lnkd.in/ejr4k2g3

An overactuated aerial robot based on cooperative quadrotors attached through passive universal joints: Modeling, control and 6-DoF trajectory tracking Science Direct https://lnkd.in/g2Gd4Tpk

🌟 Breaking Barriers in Multi-Robot Exploration! 🌟 I’m thrilled to share the outcomes of my latest project: Multi-Robot Modified Potential Field Lévy Walk-Based Exploration with Collision Avoidance and Map Merging. 🚀 This exciting project leverages advanced robotics techniques to address challenges in cooperative exploration and mapping of unknown environments. 🧠 Key Highlights: 🤖 Multi-Robot System: Designed and simulated a team of autonomous robots equipped with LIDAR sensors to collaboratively explore and map a 26x27 grid environment. 🧩 Advanced Exploration Strategy: Combined Lévy Walk for efficient random exploration with potential fields to ensure wide area coverage while maintaining safe distances between robots. 💡 Collision Avoidance & Map Merging: Implemented potential field-based repulsion forces and dynamic occupancy grid merging, enabling collision-free navigation and seamless integration of local maps into a unified global map. 📊 Results: Achieved over 90% area coverage, validated collision-free operation, and demonstrated the system’s ability to dynamically detect and mark detected objects on the global map. 🚀 Challenges Overcome: Integrating multiple robot paths into a unified global map while maintaining real-time collision-free operations. Ensuring efficiency in dynamic environments with limited computational resources. Dynamically balancing exploration and area coverage without robot overlap. 🌐 Relevance & Applications: This project showcases the immense potential of multi-robot systems in solving complex real-world challenges, including: Search and Rescue Operations: Autonomous exploration in hazardous or disaster-stricken areas. Environmental Monitoring: Mapping ecosystems or industrial zones for safety and analysis. Space Exploration: Cooperative mapping and navigation in extraterrestrial environments. 💡 Future Opportunities: This project lays the groundwork for scaling multi-robot systems to larger, more complex environments and testing them on physical platforms. Building on this, the integration of hardware-based implementations and adaptive AI-driven strategies could take this to the next level. 🙏 Acknowledgments: I am deeply grateful to Professor Spring Berman her invaluable guidance and insights throughout this project. Her expertise and support provided a strong foundation for the methodologies and approaches I implemented, playing a key role in the project’s success. #Robotics #AI #AutonomousSystems #MultiRobotExploration #LIDAR #Mapping #MATLAB #Automation

Our latest research paper, "Enhancing accuracy in field mobile robot state estimation with GNSS and encoders," has been published in Measurement. Read the full article here: https://lnkd.in/ga88JgGq Agus Hasan Ivan Adi K yul yunazwin

Looking beyond cameras 🚀 I had the privilege of working under Dr. Shashi Shekhar Jha on an exciting project developing an automated drone landing system without cameras, using acoustic sensors. Cameras often struggle with low visibility, bad weather, and accuracy issues, making them less reliable for certain applications. Our innovative approach involved designing sophisticated acoustic sensor systems that can effectively detect and process sound signals to guide drones safely to their landing pads. We also developed advanced algorithms to analyze the acoustic data, ensuring precise drone positioning and orientation. For simulation purposes, I created custom signals in MATLAB using chirp spread spectrum, incorporating various attenuations and noise to test the system's robustness. This hands-on experience has deepened my understanding of sensor technology and algorithm development, and I am incredibly grateful for the mentorship and the opportunity to work on such a cutting-edge project! Check out the project signal code on GitHub: https://lnkd.in/g_khyUeV #Research #DroneTechnology #Innovation #AcousticSensors #MATLAB #Mentorship #Engineering #TechInnovation #DroneSafety #AutonomousSystems #SignalProcessing #STEM #TechForGood #WeatherResistantTech #AI #MachineLearning

Very glad to announce that our recent paper, entitled "Stability and Accuracy-Aware Learning for Task Offloading in UAV-MEC-assisted Smart Farms," has been accepted for publication in #IEEE Transactions on Network and Service Management. Special thanks to Prof. Melike Erol-Kantarci for her assistance in conducting this research and handling the review process. I'm proud to collaborate with a great research group from #Nokia Bell Lab in Ottawa, including Dr. Sean Kennedy and Dr. Aisha Syed. The manuscript can be found via this link: https://lnkd.in/gdyNYEXE Abstract This study introduces an intelligent task offloading framework designed for smart farms that seamlessly integrates IoT devices, Unmanned Aerial Vehicles (UAVs), and Mobile Edge Computing (MEC) technology. At the heart of our methodology is the utilization of multi-agent reinforcement learning (RL) and Lyapunov theory to optimize task offloading decisions. Our primary contributions are ensuring system stability, task inference accuracy, and minimizing both task loss and delay. System stability is rigorously assessed by formalizing it based on the queue lengths of the MEC server, UAVs, and IoT devices. Furthermore, diverse machine learning (ML) models with varying complexities, such as different numbers of neurons or hidden layers, are deployed by the MEC server, UAVs, and IoT devices. This approach allows us to define task losses in relation to the inference error tolerance of the ML models. Our ultimate goal is to minimize UAV costs, taking into account energy requirements and error tolerance, all the while ensuring system stability. The growing stochastic optimization challenge, particularly with the increasing number of IoT devices, is addressed through the application of Lyapunov optimization theory and a stochastic game framework. However, due to the dynamic nature of the network environment, addressing the game proves to be a challenge, prompting us to develop and implement a multi-agent deep RL (DRL) approach. Our derived intelligent offloading scheme decreases the delay per task by about 35% with respect to the intelligent algorithm without jeopardizing the inference accuracy.

✈ Did you realise that flying began with paper planes? 🛩 Those simple toys, known as paper planes, played a pivotal role in the advancement of modern aviation. 🔷 They were an instrument for learning about aerodynamics. 🔷 Inspire pioneers of flight, such as the Wright brothers. 🔷 Contributed to improving actual aircraft designs. 🔷 Inspired future generations of engineers with a love of aviation and creativity. A basic paper plane is always the beginning of the passion with flying, from those initial folds to modern aircraft. ✈️ 📌Knowledge not shared is knowledge lost! Follow me and Ring it 🔔 on my profile for more technical and education content!💪🏆 #innovation #creativity #technology #letsconnect #future #education #knowledge #innovationvoice #innovative #amazing #robotics #AI #automation #AITips #engineering #research #science #design #physics #materials #project #civil #construction #architecture #structural #Aviation #eVTOL #Drones #Airports #aerodynamic #simulation

🌱Exciting Research Alert! 🌱 I am thrilled to share our latest research paper titled "Toward Virtual Testing of Unmanned Aerial Spraying Systems Operating in Vineyards" published in Drones MDPI. This work is the result of a collaboration with Nicoletta Bloise, Giorgio Guglieri, and Domenic D'Ambrosio from the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. Abstract: This work focuses on analyzing the impact of nozzle position and liquid sloshing on droplet deposition through numerical modeling. To achieve this, the paper presents a novel six degrees of freedom numerical model of a DJI Matrice 600 equipped with a spray system. The spray is modeled using Lagrangian particles and the liquid sloshing is modeled with an interface-capturing method known as Volume of Fluid (VOF) approach. The model is tested in a spraying operation at a constant velocity of 2 m/s in a virtual vineyard. The maneuver is achieved using a PID controller that drives the angular rates of the rotors. This spraying mission simulator was used to obtain insights into optimal nozzle selection and positioning by quantifying the amount of droplet deposition. 📊 Highlights: -Body-Force method to replicate flow under the rotors, Lagrangian particles for spray modeling, and Volume of Fluid (VOF) approach for liquid sloshing inside the tank all embedded in a CFD framework, Simcenter STAR-CCM+. -PID controller to achieve a constant flight velocity at a given altitude over the vine row. -Analyze optimal nozzle positioning to enhance spraying efficiency and PID controller tuning to obtain an adequate response. 🌍 This research contributes to the sustainable advancement of precision agriculture, helping to reduce the environmental footprint while ensuring effective plant protection products application. Furthermore, it shows the potential of multi-physics simulations to replicate complex UAS missions and help the preparation of future flight testing campaigns and the design of experiments. Check out the full paper here: https://lnkd.in/dZwMvzPx I look forward to your thoughts and feedback! #UASS #PrecisionAgriculture #Drones #AgricultureInnovation

Are you interested in understanding what happens when Robotics meets Wireless Communications? This convergence is examined by the robotics and wireless communication communities through the lenses of communications-assisted robotics (CaR) and robotics-assisted communications (RaC), respectively. However, in existing literature, the challenges arising from this convergence often involve oversimplifying either the robotics or communications aspects, restricting exploration in this promising research area. In our tutorial (led by Daniel Bonilla Licea), featured in the Proceedings of the IEEE, we introduce critical modeling tools necessary for addressing these challenges from an interdisciplinary perspective, thereby unlocking the full potential of this emerging field. As a case study, we delve into the problem of communication-aware trajectory planning. Check out our paper at https://lnkd.in/eEuisFVr The preprint version is available at https://lnkd.in/eSMRvA_e TICLab@UIR UIR College of Engineering & Architecture UM6P College of Computing

This paper first formulate the #backhaul-#and-#coverage-#aware #drone #deployment (#BoaRD) problem as #integer #linear #programming (#ILP). However, the problem is NP-hard and, therefore, they propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Their simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches—Backhaul-aware Greedy and random algorithm, respectively, by up to 17% and 95% in utilizing fewer #unmanned #aerial #vehicles (#UAVs) while ensuring 100% ground-user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting. ---- Javad Sabzehali, Vijay K. Shah, Qiang Fan, Biplav Choudhury, Lingjia Liu, Jeffrey H. Reed More details can be found at this link: https://lnkd.in/e2uffnsU