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arXiv 2025年

作者： Sautenkov, Oleg Yaqoot, Yasheerah Lykov, Artem Mustafa, Muhammad Ahsan Tadevosyan, Grik Akhmetkazy, Aibek Cabrera, Miguel Altamirano Martynov, Mikhail Karaf, Sausar Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology Russia

The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. The code is available here: https://***/sautenich/uav-vla © 2025, CC BY-NC-ND.

关键词： Visual languages

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Diffusion-Based Forecasting for Uncertainty-Aware Model Predictive Control

arXiv

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arXiv 2025年

作者： Zarifis, Stelios Kordonis, Ioannis Maragos, Petros Robotics Institute Athena Research Center Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

We propose Diffusion-Informed Model Predictive Control (D-I MPC), a generic framework for uncertainty-aware prediction and decision-making in partially observable stochastic systems by integrating diffusion-based time series forecasting models in Model Predictive Control algorithms. In our approach, a diffusion-based time series forecasting model is used to probabilistically estimate the evolution of the system’s stochastic components. These forecasts are then incorporated into MPC algorithms to estimate future trajectories and optimize action selection under the uncertainty of the future. We evaluate the framework on the task of energy arbitrage, where a Battery Energy Storage System participates in the day-ahead electricity market of the New York state. Experimental results indicate that our model-based approach with a diffusion-based forecaster significantly outperforms both implementations with classical forecasting methods and model-free reinforcement learning baselines. © 2025, CC BY.

关键词： Prediction models

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FlightAR: AR Flight Assistance Interface with Multiple Video Streams and Object Detection Aimed at Immersive Drone Control

FlightAR: AR Flight Assistance Interface with Multiple Video...

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IEEE International Conference on robotics and Biomimetics

作者： Oleg Sautenkov Selamawit Asfaw Yasheerah Yaqoot Muhammad Ahsan Mustafa Aleksey Fedoseev Daria Trinitatova Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates augmented reality (AR) overlays of UAV first-person view (FPV) and bottom camera feeds with head-mounted display (HMD) to enhance the pilot's situational awareness. Using FlightAR, the system provides pilots not only with a video stream from several UAV cameras simultaneously, but also the ability to observe their surroundings in real time. User evaluation with NASA-TLX and UEQ surveys showed low physical demand $(\mu=1.8,SD=0.8)$ and good performance $(\mu=3.4,\ SD=0.8)$ , proving better user assessments in comparison with baseline FPV goggles. Participants also rated the system highly for stimulation $(\mu=2.35,\ SD=0.9)$ , novelty $(\mu=2.1,\ SD=0.9)$ and attractiveness $(\mu=1.97,\ SD=\ 1)$ , indicating positive user experiences. These results demonstrate the potential of the system to improve UAV piloting experience through enhanced situational awareness and intuitive control. The code is available here: https://***/SautenichlFlightAR

关键词： Surveys Resists Object detection Streaming media Autonomous aerial vehicles Cameras Real-time systems Eye protection Standards Robots

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PID Steering Control for Lane Keeping Assist based on IMU Sensor and ROS 2

PID Steering Control for Lane Keeping Assist based on IMU Se...

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2nd IEEE International Conference on Electrical engineering, Computer and Information Technology, ICEECIT 2024

作者： Siagian, Marco Yosafat Salim, Taufik Ibnu Manurung, Philippians Firmansyah, Rendra Dwi Putrasari, Yanuandri Wahono, Bambang Wardana, Muhammad Khristamto Aditya Anam, Khairul Del Institute Of Technology North Sumatra Indonesia Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Faculty Del Institute Of Technology North Sumatra Indonesia Intelligent System and Robotics CDAST UNEJ Jember Indonesia

ISBN: (纸本)9798331504373

This study presents the development and implementation of a PID steering control system for lane-keeping assistance using an IMU sensor and ROS (Robot Operating System). The system leverages the capabilities of the Teensy 4.1 microcontroller and the HWT905-TTL IMU sensor to maintain precise steering adjustments, crucial for autonomous vehicle applications. The PID controller, designed to respond dynamically to real-time feedback, ensures that the motor reaches designated target angles, even under varying test conditions. The control system was tuned experimentally to achieve optimal response times, resulting in a rise time of approximately 2 seconds, a steady-state error within ±1-2 degrees, and a maximum overshoot of 4.09%. Performance metrics such as the Integral of Squared Error (ISE) and the Integral of Time-weighted Squared Error (ITSE) further confirmed system accuracy and stability over time. Results indicate that the proposed control system effectively maintains lane-keeping with minimal error, demonstrating potential for future deployment in real-world autonomous driving scenarios. Future work will focus on enhancing system adaptability through advanced tuning techniques and sensor fusion to improve robustness in dynamic environments. © 2024 IEEE.

关键词： Microcontrollers

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SDPL-SLAM: Introducing Lines in Dynamic Visual SLAM and Multi-Object Tracking

SDPL-SLAM: Introducing Lines in Dynamic Visual SLAM and Mult...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Argyris Manetas Panagiotis Mermigkas Petros Maragos Electrical and Computer Engineering National Technical University of Athens Athens Greece Robotics Institute Athena Research Center Athens Greece

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The need for a robust visual SLAM system operating in real human environments has led to the gradual abandonment of the static world assumption and to the creation of many dynamic SLAM algorithms. Even though there have been many dynamic SLAM proposals, the vast majority of them relied on point features. However, research in static SLAM systems has demonstrated that the use of more complex geometric shapes such as lines can improve performance. Motivated by this we have created a new dynamic SLAM system that estimates the camera poses and the motion of rigid objects, by exploiting both static and dynamic points and lines. Line segments have been incorporated in a novel way in every aspect of our algorithm, by improving their correspondences through optical flow refinement, and by introducing line error terms in both camera and object motion, and in batch optimization. Our proposal has been tested extensively in indoor and outdoor datasets and has achieved significant improvement compared to other state-of-the-art dynamic SLAM systems. Our results demonstrated that line segments enhanced the robustness, thus contributing towards a fully operational SLAM *** is publicly available * .

关键词： Visualization Simultaneous localization and mapping Tracking Heuristic algorithms Dynamics Cameras Robustness Trajectory Proposals Optimization

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Gas distribution mapping based on integrating system with mobile robot and sensor network 41

Gas distribution mapping based on integrating system with mo...

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第41届中国控制会议

作者： Zhirui Xu Lei Cheng Daiwei Tang Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The monitoring of industrial environmental gas quality becomes an increasingly important topic due to its significant impact on the health of the producers and the safety of the production ***,there are two main approaches to this task:first,using mobile robots equipped with sensors to patrol and monitor the environment,the prediction accuracy of this method is relatively poor;second,using a sensor network distributed in the environment for a long term monitoring,however this method lefts spatial relations and non-detection areas *** solve the above problems,this paper proposes a method combining the spatial map constructed by the mobile robot with fixed monitoring *** this method,the concentration of gas is collected by a fixed gas sensor and predicted by a deep neural *** the same time,a mobile robot equipped with lidar and a gas sensor is used to model the spatial construction of the environment as well as verify the *** embedding the predicted concentration map with the spatial map,a gas concentration map with high accuracy and spatial information is *** using anhydrous ethanol odor sources in a closed and windless room showed high prediction accuracy(98%) with this method.

关键词： Gas distribution mapping Deep neural networks spatial-temporal distribution simultaneous Localization and Mapping(SLAM)

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Real-Time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes

Real-Time Motion Generation and Data Augmentation for Graspi...

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IEEE/SICE International Symposium on System Integration

作者： Kenjiro Yamamoto Hiroshi Ito Hideyuki Ichiwara Hiroki Mori Tetsuya Ogata Robotics Research Department Connective Automation Innovation Center R&D Group Hitachi Ltd. Institute for AI and Robotics Future Robotics Organization Faculty of Science and Engineering Waseda University

While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due to their dynamic properties, requires learning a wide range of factors such as the object's position, movement speed, and grasping timing. We propose a data augmentation method for enabling a robot to grasp moving objects with different speeds and grasping timings at low cost. Specifically, the robot is taught to grasp an object moving at low speed using teleoperation, and multiple data with different speeds and grasping timings are generated by down-sampling and padding the robot sensor data in the time-series direction. By learning multiple sensor data in a time series, the robot can generate motions while adjusting the grasping timing for unlearned movement speeds and sudden speed changes. We have shown using a real robot that this data augmentation method facilitates learning the relationship between object position and velocity and enables the robot to perform robust grasping motions for unlearned positions and objects with dynamically changing positions and velocities.

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BLE beacon-based floor detection for mobile robots in a multi-floor automation laboratory

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Transportation Safety and Environment 2024年第2期6卷 114-121页

作者： Haiping Wu Hui Liu Thomas Roddelkopf Kerstin Thurow Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangshaHunan 410075China Center of Life Science Automation(celisca) 18119 Rostoc kGerman y Institute of Automation(IAT) University Rostock18119 RostockGermany

As an important task of multi-floor localization,floor detection has elicited great *** infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor ***,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor *** fact,the floor detection task can be greatly simplified with proper data collection system *** this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is *** features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection *** time delay and floor detection accuracy under different settings are ***,floor detection is evaluated on the H20 multi-floor transportation *** sensor nodes are installed on the robot at different *** floor detection performance with different installation heights is *** experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.

关键词： BLE beacon floor detection multi-floor automation transportation robot

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Smart Home Appliance Control in the IoT Era

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2022年 129卷 392-397页

作者： Khalfalla, Osman Abdalla Ali, Suleiman Abdullahi Altrjman, Chadi Mubarak, Auwalu Saleh Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Turkey Center for Research and Development Jamhuriya University of Science and Technology Mogadishu Somalia

Many nations are trying to develop smart cities. In these cities, all appliances will be connected through a communication network, the main challenge is building a system that will be flexible and efficient at the same time. In this study a mobile application was developed to control home appliances through ESP32 controller. The mobile application was developed in such a way that the user can use fingerprint, voice control, and switch buttons control. The mobile application was tested on several phones from a different manufacturer and it performs greatly based on the performance criteria specified in this work. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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BLE beacons for sample position estimation in a life science automation laboratory

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Transportation Safety and Environment 2024年第3期6卷 111-123页

作者： Haiping Wu Steffen Junginger Thomas Roddelkopf Hui Liu Kerstin Thurow Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075HunanChina Institute of Automation(IAT) University of Rostock18119 RostockGermany Center of Life Science Automation(celisca) University of Rostock18119 RostockGermany

Estimation of the sample position is essential for working process monitoring and management in the life science automation lab*** low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated *** fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)***,most of the research depends on intensive beacon *** estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated *** complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation *** this paper,BLE sensing devices for sample position estimation are *** BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data *** experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing *** Kalman filter brings benefits to stable distance *** from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

关键词： BLE beacon position estimation proximity estimation received signal strength(RSS) support vector regression(SVR) life science automation laboratory

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