Article
Robotics
Angel Romero, Robert Penicka, Davide Scaramuzza
Summary: In this letter, the challenge of flying a quadrotor using time-optimal control policies is addressed. The authors propose a sampling-based method for efficient generation of time-optimal paths and a Model Predictive Contouring Control approach for tracking the paths in real-time, ensuring adaptation to changes and unknown disturbances.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Aerospace
Jindou Jia, Kexin Guo, Xiang Yu, Lei Guo, Lihua Xie
Summary: This article develops an anti-disturbance agile flight control scheme for a maneuverable quadrotor unmanned aerial vehicle. A cascaded control framework and disturbance observers are used to handle disturbances such as aerodynamic drag, dynamic shift of center of gravity, and motor dynamics. The proposed control framework ensures agile ability, especially in the presence of multiple disturbances, as demonstrated by simulation and experimental studies.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Robotics
Sihao Sun, Angel Romero, Philipp Foehn, Elia Kaufmann, Davide Scaramuzza
Summary: This article compares two state-of-the-art control frameworks for accurate trajectory-tracking control of quadrotors. The study evaluates the nonlinear-model-predictive controller (NMPC) and the differential-flatness-based controller (DFBC) through simulations and real-world experiments. The findings demonstrate that the NMPC performs better in tracking dynamically infeasible trajectories, but at the expense of longer computation time and the risk of numerical convergence issues. The experiments also highlight the necessity of using an inner loop controller and aerodynamic drag model for agile trajectory tracking.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Review
Automation & Control Systems
Alessandro Saviolo, Giuseppe Loianno
Summary: This article reviews the advanced modeling and control techniques for aerial robots, focusing on quadrotor systems, and identifies future research directions in this field. It starts by discussing the pros and cons of classical physics-based dynamic modeling and control techniques. The article then highlights the challenges of integrating or replacing classical techniques with data-driven approaches to enhance flight precision, safety, adaptability, and agility.
ANNUAL REVIEWS IN CONTROL
(2023)
Article
Automation & Control Systems
Xun Gu, Bin Xian, Yinxin Wang
Summary: This article investigates the agile flight control design for a quadrotor unmanned aerial vehicle in a 3D environment. The proposed controller, designed in the rotation format, offers advantages in terms of easier implementation and robustness to uncertainties and disturbances. The stability and effectiveness of the controller are validated through real-time experiments, demonstrating higher flight control accuracy in agile flights.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Robotics
Ziyu Zhou, Gang Wang, Jian Sun, Jikai Wang, Jie Chen
Summary: This letter proposes a novel approach to computing time-optimal trajectories, which significantly accelerates trajectory planning by fixing nodes with waypoint constraints and adopting separate sampling intervals. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme, enhancing tracking accuracy and robustness.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Biology
Thomas Akam, Andy Lustig, James M. Rowland, Sampath K. T. Kapanaiah, Joan Esteve-Agraz, Mariangela Panniello, Cristina Marquez, Michael M. Kohl, Dennis Kaetzel, Rui M. Costa, Mark E. Walton
Summary: Laboratory behavioural tasks are important research tools, and the pyControl system enables faster, easier, and more cost-effective implementation of these tasks. The system features a highly readable task definition syntax and self-documenting features, which facilitate communication and reproducibility of behavioural experiments.
Article
Engineering, Civil
M. A. Bravo-Haro, X. Ding, A. Y. Elghazouli
Summary: This paper presents a sensing technique built from off-the-shelf electronic components, intended to serve as an open-source platform for seismic monitoring of structural systems. The evaluation shows that the proposed sensor is suitable for this application, exhibiting stable performance with low levels of self-noise.
ENGINEERING STRUCTURES
(2021)
Article
Robotics
Philipp Foehn, Angel Romero, Davide Scaramuzza
Summary: Quadrotors are known for their agility, but planning time-optimal trajectories through multiple waypoints has been a challenge. This study introduces a new method that simultaneously optimizes time allocation and trajectory to generate truly time-optimal trajectories, surpassing human expert drone pilots in a drone-racing task.
Article
Chemistry, Analytical
Petra Itterheimova, Petr Kuban
Summary: A fully automated, open source capillary electrophoresis (CE) autosampler has been developed, capable of handling up to 14 different samples as a modular component of any in-house built CE instrument. It is operated by an Arduino Mega microcontroller and allows fully programmable injection sequence.
ANALYTICA CHIMICA ACTA
(2023)
Article
Automation & Control Systems
Mou Chen, Shixun Xiong, Qingxian Wu
Summary: In this paper, a tracking flight control scheme based on a disturbance observer is proposed for a quadrotor with external disturbances. The scheme involves estimating unknown disturbances and developing flight controllers to track given signals. Experimental results demonstrate the effectiveness of the control strategy.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Lucas Vago Santana, Alexandre Santos Brandao, Mario Sarcinelli-Filho
Summary: This article discusses the development and experimental validation of a workbench supporting outdoor flights using affordable commercial quadrotor AR.Drone 2.0 and a personal computer. Results from experimental flights show that the system is efficient in guiding the vehicle to accomplish different tasks, making it a valuable open-source workbench for researchers in aerial robotics.
IEEE SYSTEMS JOURNAL
(2021)
Article
Automation & Control Systems
Fulin Song, Zhan Li, Sichen Yang, Juan J. Rodriguez-Andina
Summary: The goal of this article is to design a feedforward compensator based on deep reinforcement learning (DRL) for cooperative quadrotors in close crossing flight. Both value based and policy based compensator algorithms are proposed and analyzed using Lyapunov stability criteria. The effectiveness and advantages of the compensator are demonstrated through simulation and experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Environmental Sciences
Yinghao Zhao, Li Yan, Yu Chen, Jicheng Dai, Yuxuan Liu
Summary: This paper introduces a robust and efficient trajectory replanning method based on the guiding path for UAV path planning in unknown cluttered environments. By generating a safe guiding path, designing a guided kinodynamic path searching method, and proposing an adaptive optimization function, the proposed method significantly improves the quality and success rate of path planning.
Review
Computer Science, Information Systems
Jonathan Alvarez Ariza, Joshua M. M. Pearce
Summary: This study employs a systematic literature review (SLR) to analyze and describe the utilization of free and open-source hardware (OSHW) and open software (OSS) in the design, development, and deployment of low-cost assistive technologies (ATs). The findings reveal the potential of ATs designed with open source technologies in enhancing the quality of life for disabled people across different types of disabilities. However, there are technical, policy, and social participation challenges that need to be addressed in the application of these technologies.
Article
Multidisciplinary Sciences
Christian Pfeiffer, Simon Wengeler, Antonio Loquercio, Davide Scaramuzza
Summary: This study investigates whether neural networks can imitate human eye gaze behavior and attention to improve performance in drone racing tasks, and the experimental results demonstrate that human visual attention prediction can enhance the performance of autonomous drone racing.
Article
Robotics
Julio L. Paneque, Jose Ramiro Martinez-de Dios, Anibal Ollero, Drew Hanover, Sihao Sun, Angel Romero, Davide Scaramuzza
Summary: This letter presents a novel perching trajectory generation framework that computes perception-aware, collision-free, and dynamically-feasible maneuvers to guide the robot to perch on powerlines. The method considers the robot's dynamic model, collision avoidance, and maximizing the visibility of the powerline, and enables online execution on resource-constrained hardware.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Robert Penicka, Davide Scaramuzza
Summary: The method utilizes a hierarchical, sampling-based approach with increasingly complex quadrotor models to plan the minimum-time trajectory for a quadrotor over specified waypoints in the presence of obstacles. It first finds paths in different topologies and then uses an asymptotically-optimal, kinodynamic sampling-based method to find a time-optimal feasible trajectory based on the full quadrotor model.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Angel Romero, Robert Penicka, Davide Scaramuzza
Summary: In this letter, the challenge of flying a quadrotor using time-optimal control policies is addressed. The authors propose a sampling-based method for efficient generation of time-optimal paths and a Model Predictive Contouring Control approach for tracking the paths in real-time, ensuring adaptation to changes and unknown disturbances.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Robert Penicka, Yunlong Song, Elia Kaufmann, Davide Scaramuzza
Summary: This study addresses the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles. By leveraging deep reinforcement learning and classical topological path planning, a robust neural-network controller is trained to achieve better performance and higher robustness compared to state-of-the-art methods.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Sihao Sun, Angel Romero, Philipp Foehn, Elia Kaufmann, Davide Scaramuzza
Summary: This article compares two state-of-the-art control frameworks for accurate trajectory-tracking control of quadrotors. The study evaluates the nonlinear-model-predictive controller (NMPC) and the differential-flatness-based controller (DFBC) through simulations and real-world experiments. The findings demonstrate that the NMPC performs better in tracking dynamically infeasible trajectories, but at the expense of longer computation time and the risk of numerical convergence issues. The experiments also highlight the necessity of using an inner loop controller and aerodynamic drag model for agile trajectory tracking.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Lintong Zhang, Michael Helmberger, Lanke Frank Tarimo Fu, David Wisth, Marco Camurri, Davide Scaramuzza, Maurice Fallon
Summary: To drive the advancement of SLAM systems, we created the Hilti-Oxford Dataset, which includes various challenges to test the performance of SLAM algorithms in different scenarios. We implemented a novel ground truth collection method to accurately measure pose errors with millimeter accuracy. The dataset attracted a large number of researchers to participate in the Hilti SLAM challenge.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Automation & Control Systems
Tomas Baca, Robert Penicka, Petr Stepan, Matej Petrlik, Vojtech Spurny, Daniel Hert, Martin Saska
Summary: This paper presents a system for autonomous cooperative wall building with a team of Unmanned Aerial Vehicles (UAVs). The system uses initial scanning to find the locations of bricks and wall structure, assigns UAVs to place bricks, and coordinates multiple UAVs for precise grasping and placement. The developed CTU-UPenn-NYU approach achieved the best performance in the MBZIRC competition by correctly placing a high number of bricks to collect the highest number of points.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2023)
Article
Robotics
Tim Salzmann, Elia Kaufmann, Jon Arrizabalaga, Marco Pavone, Davide Scaramuzza, Markus Ryll
Summary: Model Predictive Control (MPC) is a popular framework in embedded control for high-performance autonomous systems. However, the use of accurate dynamics models is crucial for achieving good control performance. This study presents Real-time Neural MPC, a framework that efficiently integrates large neural network architectures as dynamics models within a model-predictive control pipeline. The experiments conducted in simulation and on a quadrotor platform show the capabilities of the system to run learned models with large modeling capacity using gradient-based online optimization MPC, resulting in significant improvements compared to state-of-the-art MPC approaches.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Giovanni Cioffi, Leonard Bauersfeld, Elia Kaufmann, Davide Scaramuzza
Summary: Inertial odometry is a promising solution for state estimation in agile quadrotor flight. However, relying solely on inertial measurements leads to drift in pose estimates. This study proposes a learning-based odometry algorithm using an IMU as the only sensor for autonomous drone racing. The algorithm combines a model-based filter driven by inertial measurements with a learning-based module that uses thrust measurements. The results show the superiority of this inertial odometry algorithm in pose estimation compared to other visual-inertial odometry methods, indicating its potential in agile quadrotor flight.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Construction & Building Technology
Basel Elkhapery, Robert Penicka, Michal Nemec, Mohsin Siddiqui
Summary: This paper presents a wall construction planner for UAVs that utilizes a GRASP metaheuristic to generate near-optimal building plans quickly. It addresses the time-consuming and labor-intensive task of wall construction while reducing safety risks. The planner outperforms other planning approaches and demonstrates the potential of UAVs and optimization algorithms in improving construction efficiency and safety.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Daniel Hert, Tomas Baca, Pavel Petracek, Vit Kratky, Robert Penicka, Vojtech Spurny, Matej Petrlik, Matous Vrba, David Zaitlik, Pavel Stoudek, Viktor Walter, Petr Stepan, Jiri Horyna, Vaclav Pritzl, Martin Sramek, Afzal Ahmad, Giuseppe Silano, Daniel Bonilla Licea, Petr Stibinger, Tiago Nascimento, Martin Saska
Summary: This paper introduces a modular autonomous UAV platform called the Multi-robot System (MRS) Drone that is suitable for indoor and outdoor applications. The MRS Drone features unique modular changes in actuators, frames, and sensory configuration, allowing smooth deployment of multiple aerial robots and outperforming other platforms. The platform is easy to assemble and modify, and it includes a realistic simulator for pre-flight testing and real-world experiments. The manuscript presents mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system, and demonstrates the full capabilities and unique modularity of the MRS Drone in various real-world applications.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2023)
Review
Multidisciplinary Sciences
Leonard Bauersfeld, Angel Romero, Manasi Muglikar, Davide Scaramuzza
Summary: In this study, a transformer-based neural network architecture is proposed to attribute an anonymous manuscript to an author using only the text content and author names. The largest authorship-identification dataset to date was created by leveraging over 2 million publicly available research papers on arXiv. The method achieves an unprecedented authorship attribution accuracy, correctly attributing up to 73% of papers in subsets with up to 2,000 different authors.