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IEEE International Conference on autonomous Robot systems and Competitions (ICARSC)

作者： Ricardo B. Sousa Cláudia D. Rocha João G. Martins João Pedro Costa João Tomás Padrão José Maria Sarmento José Pedro Carvalho Maria S. Lopes Paulo G. Costa António Paulo Moreira Faculty of Engineering University of Porto (FEUP) Porto Portugal CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Trás-os-Montes and Alto Douro (UTAD) Vila Real Portugal C2SR - Cyber-Physical Control Systems and Robotics SYSTEC - Research Center for Systems and Technologies Porto Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Robotic competitions stand as platforms to propel the forefront of robotics research while nurturing STEM education, serving as hubs of both applied research and scientific innovation. In Portugal, the Portuguese robotics Open (FNR) is an event with several robotic competitions, including the Robot@Factory 4.0 competition. This competition presents an example of deploying autonomous robots on a factory shop floor. Although the literature has works proposing frameworks for the original version of the Robot@Factory competition, none of them proposes a system framework for the Robot@Factory 4.0 version that presents the hardware, firmware, and software to complete the competition and achieve autonomous navigation. This paper proposes a complete robotic framework for the Robot@Factory 4.0 competition that is modular and open-access, enabling future participants to use and improve it in future editions. This work is the culmination of all the knowledge acquired by winning the 2022 and 2023 editions of the competition.

关键词： Technological innovation Education Propulsion Production facilities Software Hardware autonomous robots

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Extracting Strong Policies for robotics Tasks from Zero-Order Trajectory Optimizers 9

Extracting Strong Policies for Robotics Tasks from Zero-Orde...

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9th International Conference on Learning Representations, ICLR 2021

作者： Pinneri, Cristina Sawant, Shambhuraj Blaes, Sebastian Martius, Georg Autonomous Learning Group Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Max Planck ETH Center for Learning Systems Department of Engineering Cybernetics NTNU Trondheim Norway

Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance and are considered state-of-the-art in the model-based reinforcement learning community. However, this success comes at the cost of high computational complexity, being therefore not suitable for real-time control. In this paper, we propose a technique to jointly optimize the trajectory and distill a policy, which is essential for fast execution in real robotic systems. Our method builds upon standard approaches, like guidance cost and dataset aggregation, and introduces a novel adaptive factor which prevents the optimizer from collapsing to the learner's behavior at the beginning of the training. The extracted policies reach unprecedented performance on challenging tasks like making a humanoid stand up and opening a door without reward shaping. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Real time control

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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IEEE International Conference on autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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autonomous fire fighting with a UAV-UGV team at MBZIRC 2020

arXiv

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arXiv 2021年

作者： Quenzel, Jan Splietker, Malte Pavlichenko, Dmytro Christian Lenz, Daniel Schleich Schwarz, Max Schreiber, Michael Beul, Marius Behnke, Sven Institute for Computer Science VI Autonomous Intelligent Systems University of Bonn Friedrich-Hirzebruch-Allee 8 Bonn53115 Germany

Every day, burning buildings threaten the lives of occupants and first responders trying to save them. Quick action is of essence, but some areas might not be accessible or too dangerous to enter. Robotic systems have become a promising addition to firefighting, but at this stage, they are mostly manually controlled, which is error-prone and requires specially trained personal. We present two systems for autonomous firefighting from air and ground we developed for the Mohamed Bin Zayed International robotics Challenge (MBZIRC) 2020. The systems use LiDAR for reliable localization within narrow, potentially GNSS-restricted environments while maneuvering close to obstacles. Measurements from LiDAR and thermal cameras are fused to track fires, while relative navigation ensures successful extinguishing. We analyze and discuss our successful participation during the MBZIRC 2020, present further experiments, and provide insights into our lessons learned from the competition. Copyright © 2021, The Authors. All rights reserved.

关键词： robotics

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Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives

Integrated Bi-Manual Motion Generation and Control shaped fo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jonathan Vorndamme João Carvalho Riddhiman Laha Dorothea Koert Luis Figueredo Jan Peters Sami Haddadin Department of Computer Engineering Technische Universität München (TUM) TUM School of Computation Information and Technology (CIT) Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Intelligent Autonomous Systems group Technische Universitat Darmstadt (TUD) Germany Center for Cognitive Science TUD Germany

ISBN: (纸本)9798350309805

This work introduces a novel cooperative control framework that allows for real-time reactiveness and adaptation whilst satisfying implicit constraints stemming from proba-bilistic/stochastic trajectories. Stemming from task-oriented sampling and/or task-oriented demonstrations, e.g., learning based on motion primitives, such trajectories carry additional information often neglected during real-time control deployment. In particular, methods such as probabilistic movement primitives offer the advantage to capture the inherent stochasticity in human demonstrations - which in turn reflects human's understanding about task-variability and adaption possibilities. This information, however, is often poorly exploited and, mostly, used during offline trajectory planning stage. Our work instead introduces a novel real-time motion-generation strategy that explicitly exploits such information to improve trajectories according to changes in the environmental condition and robot task-space topology. The proposed solution is particularly well-suited for bi-manual and coordinated systems where the increased kinematic complexity, tightly-coupled constraints and reduced workspace have detrimental effects on the manipula-bility, joint-limits, and are even capable of causing unstable behavior and task-failure. Our methodology addresses these challenges, and improves performance and task-execution by taking the confidence range region explicitly into account whilst maneuvering towards better configurations. Furthermore, it can directly cope with different closed-chain kinematics and task-space topologies, resulting for instance from different grasps. Experimental evaluations on a bi-manual Franka panda robot show that the method can run in the inner control loop of the robot and enables successful execution of highly constrained tasks.

关键词： Trajectory planning Robot kinematics Humanoid robots Kinematics Probabilistic logic Real-time systems Trajectory

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autonomous Wall Building with a UGV- UAV Team at MBZIRC 2020

Autonomous Wall Building with a UGV- UAV Team at MBZIRC 2020

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Christian Lenz Max Schwarz Andre Rochow Jan Razlaw Arul Selvam Periyasamy Michael Schreiber Sven Behnke Institute for Computer Science VI Autonomous Intelligent Systems University of Bonn Bonn Germany

ISBN: (数字)9781665403900

ISBN: (纸本)9781665403917

Constructing large structures with robots is a challenging task with many potential applications that requires mobile manipulation capabilities. We present two systems for autonomous wall building that we developed for the Mohamed Bin Zayed International robotics Challenge 2020. Both systems autonomously perceive their environment, find bricks, and build a predefined wall structure. While the UGV uses a 3D LiDAR-based perception system which measures brick poses with high precision, the UAV employs a real-time camera-based system for visual servoing. We report results and insights from our successful participation at the MBZIRC 2020 Finals, additional lab experiments, and discuss the lessons learned from the competition.

关键词： Robots Task analysis Robot kinematics Manipulators Grippers Buildings Laser radar

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visually Guided Balloon Popping with an autonomous MAV at MBZIRC 2020

Visually Guided Balloon Popping with an Autonomous MAV at MB...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Marius Beul Simon Bultmann Andre Rochow Radu Alexandru Rosu Daniel Schleich Malte Splietker Sven Behnke Institute for Computer Science VI Autonomous Intelligent Systems University of Bonn Bonn Germany

ISBN: (数字)9781665403900

ISBN: (纸本)9781665403917

visually guided control of micro aerial vehicles (MAV) demands for robust real-time perception, fast trajectory generation, and a capable flight platform. We present a fully autonomous MAV that is able to pop balloons, relying only on onboard sensing and computing. The system is evaluated with real robot experiments during the Mohamed Bin Zayed International robotics Challenge (MBZIRC) 2020 where it showed its resilience and speed. In all three competition runs we were able to pop all five balloons in less than two minutes flight time with a single MAV.

关键词： Trajectory Robots Three-dimensional displays Cameras Real-time systems Global navigation satellite system Hardware

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Trajectory Generation with Fast Lidar-based 3D Collision Avoidance for Agile MAVs

Trajectory Generation with Fast Lidar-based 3D Collision Avo...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Marius Beul Sven Behnke Institute for Computer Science VI Autonomous Intelligent Systems University of Bonn Bonn Germany

ISBN: (数字)9781665403900

ISBN: (纸本)9781665403917

Micro aerial vehicles (MAVs), are frequently used for exploration, examination, and surveillance during search and rescue missions. Manually piloting these robots under stressful conditions provokes pilot errors and can result in crashes with disastrous consequences. Also, during fully autonomous flight, planned high-level trajectories can be erroneous and steer the robot into obstacles. In this work, we propose an approach to efficiently compute smooth, time-optimal trajectories MAVs that avoid obstacles. Our method first computes a trajectory from the start to an arbitrary target state, including position, velocity, and acceleration. It respects input- and state-constraints and is thus dynamically feasible. Afterward, we efficiently check the trajectory for collisions in the 3D-point cloud, recorded with the onboard lidar. We exploit the piecewise polynomial formulation of our trajectories to analytically compute axis-aligned bounding boxes (AABB) to speed up the collision checking process. If collisions occur, we generate a set of alternative trajectories in real-time. Alternative trajectories bring the MAV in a safe state, while still pursuing the original goal. Subsequently, we choose and execute the best collision-free alternative trajectory based on a distance metric. The evaluation in simulation and during a real firefighting exercise shows the capability of our method.

关键词： Trajectory Three-dimensional displays Collision avoidance Laser radar Robot sensing systems Computational modeling Cameras

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Fast Whole-Body Motion Control of Humanoid Robots with Inertia Constraints

Fast Whole-Body Motion Control of Humanoid Robots with Inert...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Grzegorz Ficht Sven Behnke Autonomous Intelligent Systems (AIS) Group Computer Science Institute VI University of Bonn Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We introduce a new, analytical method for generating whole-body motions for humanoid robots, which approximate the desired Composite Rigid Body (CRB) inertia. Our approach uses a reduced five mass model, where four of the masses are attributed to the limbs and one is used for the trunk. This compact formulation allows for finding an analytical solution that combines the kinematics with mass distribution and inertial properties of a humanoid robot. The positioning of the masses in Cartesian space is then directly used to obtain joint angles with relations based on simple geometry. Motions are achieved through the time evolution of poses generated through the desired foot positioning and CRB inertia properties. As a result, we achieve short computation times in the order of tens of microseconds. This makes the method suited for applications with limited computation resources, or leaving them to be spent on higher-layer tasks such as model predictive control. The approach is evaluated by performing a dynamic kicking motion with an igus ® Humanoid Open Platform robot.

关键词： Legged locomotion Foot Kinematics Humanoid robots Hip Computational modeling Task analysis

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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