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International Conference on Advanced robotics (ICAR)

作者： Goran Popović Igor Cviić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665436854

Modern warehouses, equipped with an autonomous robots fleet, suffer from an efficiency deficit during human interventions in the shop floor area, since, during such interventions, all the robots must stop for safety reasons. This particularly affects large warehouses which could extremely benefit from a solution that would allow human-robot collaboration during such interventions. This solution should only stop the robots that are in the vicinity of the human and reroute the others, and it requires the following prerequisites: (i) reliable relative ranging of robots with respect to human workers that would ensure safety, and (ii) reliable localization of human workers in the warehouse. In this paper we propose a system for reliable human localization that is based on fusing cues from measured relative ranges with respect to moving robots and a camera system that is worn by the human worker. Since global locations of the robots in the warehouse are known, we use the relative ranges to robots in an indoor GPS-like fashion to provide global human location updates, while the wearable cameras provide visual odometry, i.e., the relative motion of the human, and another global cue by detecting ground fiducial markers used by robots. The fusion is performed within a graph-based optimization framework that also includes a visual odometry error model. We carried out an experimental analysis on three different datasets to validate the performance of the proposed approach and compared it to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results demonstrate that our approach yields reliable human location within warehouse like environments.

关键词： Location awareness Visualization Robot vision systems Cameras Distance measurement Safety Reliability

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Event-Based Kalman Filtering Exploiting Correlated Trigger Information

Event-Based Kalman Filtering Exploiting Correlated Trigger I...

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International Conference on Information Fusion

作者： Benjamin Noack Clemens Ö hl Uwe D. Hanebeck Autonomous Multisensor Systems Group Institute for Intelligent Cooperating Systems Otto von Guericke University Magdeburg Magdeburg Germany Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In order to tap the full potential of event-based scheduling, the design of transmission triggers and estimators need to be closely coupled while two directions are promising: First, the remote estimator can exploit the absence of transmissions and translate it into implicit information about the sensor data. Second, an intelligent trigger mechanism at the sensor that predicts future sensor readings can decrease transmission rates while rendering the implicit information more valuable. Such an intelligent trigger has been developed in a recent paper based on a Finite Impulse Response filter, which requires the sensor to transmit an additional estimate alongside the measurement. In the present paper, the communication demand is further reduced by only transmitting the estimate. The remote estimator exploits correlations to incorporate the received information. In doing so, the estimation quality is also improved, which is confirmed by simulations.

关键词： Finite impulse response filters Computational modeling Estimation Rendering (computer graphics) Information filters Time measurement Sensors

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Universal Adaptive Fault-Tolerant Control of a Multicopter UAV ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9340-9347页

作者： Duc-Tien Nguyen David Saussié Lahcen Saydy Mobile Robotics and Autonomous Systems Laboratory Polytechnique Montreal Canada

This paper presents a universal adaptive fault-tolerant control (FTC) design for multicopter unmanned aerial vehicles (UAVs). The proposed architecture consists of a two-loop control structure: a fault-tolerant controller generates normalized virtual control inputs to track the desired trajectory subject to actuator faults, and an adaptive augmentation controller deals with system uncertainties and also balances the design requirements for specific platform. The FTC approach is based on gain-scheduling control in the framework of structured H ∞ synthesis. In order to implement the overall control system on most types of multicopter UAVs, an adaptive mapping algorithm is proposed. High fidelity simulations and experimental results, performed on various multicopters with different payload and configuration, show the effectiveness and robustness of the proposed approach in accommodating different levels of actuator degradation including total failures of the rotors as well as unknown mass and inertia, all using a single controller with fixed coefficients.

关键词： Multicopter Fault-Tolerant Control Robust Adaptive Control Gain-Scheduling Structured H-Infinity Control

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Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

Sample Efficient Dynamics Learning for Symmetrical Legged Ro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jee-eun Lee Jaemin Lee Tirthankar Bandyopadhyay Luis Sentis Department of Aerospace Engineering and Engineering Mechanics Human Centered Robotics Laboratory The University of Texas at Austin USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Robotics and Autonomous Systems Group Data61 CSIRO QLD Australia Faculty of Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin USA

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Existing frameworks that represent all data in vector space fail to consider the structured information of the robot, such as leg symmetry, rotational symmetry, and physics invariance. As a result, these schemes require vast amounts of training data to learn the system's redundant elements because they are learned independently. Instead, we propose considering the geometric prior by representing the system in symmetrical object groups and designing neural network architecture to assess invariance and equivariance between the objects. Finally, we demonstrate the effectiveness of our approach by comparing the generalization to unseen data of the proposed model and the existing models. We also implement a controller of a climbing robot based on learned inverse dynamics models. The results show that our method generates accurate control inputs that help the robot reach the desired state while requiring less training data than existing methods.

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Distilling Tiny and Ultra-fast Deep Neural Networks for autonomous Navigation on Nano-UAVs

arXiv

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arXiv 2024年

作者： Lamberti, Lorenzo Bellone, Lorenzo Macan, Luka Natalizio, Enrico Conti, Francesco Palossi, Daniele Benini, Luca The Department of Electrical Electronic and Information Engineering University of Bologna Italy The Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates CNRS LORIA University of Lorraine France The Dalle Molle Institute for Artificial Intelligence USI-SUPSI Switzerland The Integrated Systems Laboratory ETH Zürich Switzerland

Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast navigation under very tight memory/computation constraints. The PULP-Dronet convolutional neural network (CNN) enables autonomous navigation running aboard a nano-UAV at 19 frame/s, at the cost of a large memory footprint of 320 kB - and with drone control in complex scenarios hindered by the disjoint training of collision avoidance and steering capabilities. In this work, we distill a novel family of CNNs with better capabilities than PULP-Dronet, but memory footprint reduced by up to 168× (down to 2.9 kB), achieving an inference rate of up to 139 frame/s;we collect a new open-source unified collision/steering 66 k images dataset for more robust navigation;and we perform a thorough in-field analysis of both PULP-Dronet and our tiny CNNs running on a commercially available nano-UAV. Our tiniest CNN, called Tiny-PULP-Dronet v3, navigates with a 100% success rate a challenging and never-seen-before path, composed of a narrow obstacle-populated corridor and a 180° turn, at a maximum target speed of 0.5 m/s. In the same scenario, the SoA PULP-Dronet consistently fails despite having 168× more parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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Probabilistic Multimodal Depth Estimation Based on Camera-LiDAR Sensor Fusion

arXiv

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arXiv 2023年

作者： Obando-Ceron, Johan S. Romero-Cano, Victor Monteiro, Sildomar Robotics and Autonomous Systems Laboratory Faculty of Engineering Universidad Autónoma de Occidente Valle del Cauca Cali Colombia Autonomy Research Division Aurora Flight Sciences CambridgeMA United States University of Montreal MontréalQC Canada

Multi-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There have been outstanding advances in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution, or LiDAR sensors, due to the precise geometric data they provide. However, each of these suffers from some inherent drawbacks, such as high sensitivity to changes in illumination conditions in the case of cameras and limited resolution for the LiDARs. Sensor fusion can be used to combine the merits and compensate for the downsides of these two kinds of sensors. Nevertheless, current fusion methods work at a high level. They process the sensor data streams independently and combine the high-level estimates obtained for each sensor. In this paper, we tackle the problem at a low level, fusing the raw sensor streams, thus obtaining depth estimates which are both dense and precise, and can be used as a unified multi-modal data source for higher level estimation problems. This work proposes a Conditional Random Field model with multiple geometry and appearance potentials. It seamlessly represents the problem of estimating dense depth maps from camera and LiDAR data. The model can be optimized efficiently using the Conjugate Gradient Squared algorithm. The proposed method was evaluated and compared with the state-of-the-art using the commonly used KITTI benchmark dataset. © 2023, CC BY.

关键词： Cameras

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autonomous navigation of a tracked unmanned ground vehicle*

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IFAC-PapersOnLine 2022年第14期55卷 120-125页

作者： Marija Seder Anđela Jurić Ana Šelek Filip Marić Marija Lovrić Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) Zagreb Croatia University of Zagreb Croatian Military Academy Dr. Franjo Tuđman Zagreb Croatia

This work proposes a complete autonomous navigation system for a tracked vehicle. The system enables a complete autonomous execution of waypoint and patrolling tasks selected by the user. It also enables user-vehicle shared autonomy, switching between the user teleoperation and the vehicle autonomous operation. Our navigation system uses the model predictive control scheme based on a navigation function. We propose the navigation function which takes into account changing environments, any-shape footprint, and non-holonomic motion of the tracked vehicle. Besides the waypoint and patrolling tasks, we implemented a fail-safe scenario in case of the user-vehicle communication loss, in which the vehicle returns autonomously to the previously visited goal where the communication was stable. The efficiency of the proposed system is validated by experimental results on the Komodo tracked vehicle.

关键词： Robot navigation path planning obstacle detection avoidance motion control map building

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Depth from Mono Accuracy Analysis by Changing Camera Parameters in the CARLA Simulator

Depth from Mono Accuracy Analysis by Changing Camera Paramet...

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Proceedings of the International Convention MIPRO

作者： Zvonimir Gršković Juraj Peršić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) Zagreb Croatia

Depth estimation is an important task in robotics and autonomous driving. By estimating depth and relying only on a single camera, it is no longer necessary to add and calibrate additional sensors - usually a second camera. However, such an approach requires training on extensive datasets and obtaining real-world datasets is time consuming and costly. Given that, using photorealistic simulators can be beneficial, since a multitude of varoius scenes can be created. In this paper we present an approach to training a deep neural network based on the ResNet architecture for estimating depth from a single camera. We target road vehicle scenes and use the CARLA simulator. We evaluate the trained network on the real-world KITTI dataset images and in the CARLA simulator. In the simulated experiments, we compare the performance with respect to the changes in camera intrinsic and extrinsic calibration parameters with respect to the ego vehicle frame.

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A Sim-to-Real Deep Learning-based Framework for autonomous Nano-drone Racing

arXiv

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arXiv 2023年

作者： Lamberti, Lorenzo Cereda, Elia Abbate, Gabriele Bellone, Lorenzo Morinigo, Victor Javier Kartsch Barcis, Michal Barcis, Agata Giusti, Alessandro Conti, Francesco Palossi, Daniele Department of Electrical Electronic and Information Engineering University of Bologna Italy Dalle Molle Institute for Artificial Intelligence USI-SUPSI Switzerland Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates Integrated Systems Laboratory ETH Zürich Switzerland

autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles’ perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ∼10 cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the "IMAV 2022 Nanocopter AI Challenge." We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal. Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments. Our system ranked 1st among seven competing teams at the competition. In our best attempt, we scored 115 m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles. Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Adaptive Kalman-based hybrid car following strategy using TD3 and CACC

arXiv

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arXiv 2023年

作者： Zheng, Yuqi Yan, Ruidong Jia, Bin Jiang, Rui Tapus, Adriana Chen, Xiaojing Zheng, Shiteng Shang, Ying Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Systems Science Beijing Jiaotong University Beijing100044 China Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Palaiseau91120 France School of Economics and Management Beihang University Beijing100044 China

In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and significantly improve the performance of car following. However, it is challenging for the traditional hybrid strategy based on fixed coefficients to adapt to mixed traffic flow scenarios, which may decrease the performance and even lead to accidents. To address the above problems, a hybrid car following strategy based on an adaptive Kalman Filter is proposed by regarding CACC and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. Different from traditional hybrid strategy based on fixed coefficients, the Kalman gain H, using as an adaptive coefficient, is derived from multi-timestep predictions and Monte Carlo Tree Search. At the end of study, simulation results with 4157745 timesteps indicate that, compared with the TD3 and HCFS algorithms, the proposed algorithm in this study can substantially enhance the safety of car following in mixed traffic flow without compromising the comfort and efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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