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丛书名： Lecture Notes in Computer Science

1000年

作者： Guido Herrmann Matthew Studley Martin Pearson Andrew Conn Chris Melhuish Mark Witkowski Jong-Hwan Kim Prahlad Vadakkepat

ISBN: (数字)9783642325274

ISBN: (纸本)9783642325267

This book constitutes the refereed proceedings of the 13th Conference on Towards autonomous Robotic Systems, TAROS 2012 and the 15th Robot World Congress, FIRA 2012, held as joint conference in Bristol, UK, in August 2012. The 36 revised full papers presented together with 25 extended abstracts were carefully reviewed and selected from 89 submissions. The papers cover various topics in the field of autonomous robotics.

关键词： Artificial Intelligence Image Processing and Computer Vision User Interfaces and Human Computer Interaction Data Mining and Knowledge Discovery

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Robust-Locomotion-by-Logic: Perturbation-Resilient Bipedal Locomotion via Signal Temporal Logic Guided Model Predictive Control

arXiv

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arXiv 2024年

作者： Gu, Zhaoyuan Zhao, Yuntian Chen, Yipu Guo, Rongming Leestma, Jennifer K. Sawicki, Gregory S. Zhao, Ye The Laboratory for Intelligent Decision and Autonomous Robots Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30313 United States The Physiology of Wearable Robotics Lab Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30313 United States

This study introduces a robust planning framework that utilizes a model predictive control (MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion, specifically designed to handle both translational and orientational perturbations. Existing recovery strategies often struggle with reasoning complex task logic and evaluating locomotion robustness systematically, making them susceptible to failures caused by inappropriate recovery strategies or lack of robustness. To address these issues, we design an analytical stability metric for bipedal locomotion and quantify this metric using STL specifications, which guide the generation of recovery trajectories to achieve maximum robustness degree. To enable safe and computational-efficient crossed-leg maneuver, we design data-driven self-leg-collision constraints that are 1000 times faster than the traditional inverse-kinematics-based approach. Our framework outperforms a state-of-the-art locomotion controller, a standard MPC without STL, and a linear-temporal-logic-based planner in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Additionally, the Cassie bipedal robot achieves robust performance under horizontal and orientational perturbations such as those observed in ship motions. These environments are validated in simulations and deployed on hardware. Furthermore, our proposed method demonstrates versatility on stepping stones and terrain-agnostic features on inclined terrains. © 2024, CC BY.

关键词： Inverse kinematics

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A distributed sensing system for vehicles state estimation

A distributed sensing system for vehicles state estimation

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2006 ASME International Mechanical Engineering Congress and Exposition, IMECE2006

作者： Bolandhemmat, Hamidreza Clark, Christopher M. Golnaraghi, M.F. Department of Mechanical Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada Lab. for Autonomous and Intelligent Robotics Department of Mechanical Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada Mechatronics and Smart Materials Systems Department of Mechanical Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada

ISBN: (纸本)0791837904

This paper presents the design of a distributed sensing system that uses an Extended Kalman Filter (EKF) to fuse measurements, so that automotive vehicle states can be estimated for use by a Semi-active Suspension control system. To improve ride comfort and handling quality, relative displacements and velocities of suspension systems are estimated. To control the stability of vehicles, roll, yaw, and pitch must also be determined. The designed (EKF) uses easily accessible measurements such as accelerations and body's angular velocities. These measurements are provided by 8 accelerometers and an Inertial Measurement Unit (IMU). The accelerometers are strategically mounted on the two ends of each individual shock absorber (damper). The IMU was mounted near the vehicle's center of gravity. Computer simulations and experiments were conducted for full vehicle state estimation of a 1993 Toyota Tercel equipped with the above mentioned sensor suite. Results show that except relative displacements, all states of the automobile's semi-active suspension systems can be estimated using this set of sensors. The designed EKF works well despite not knowing accurate information about road inputs, external disturbances and car characteristics such as moments of inertia, mass, and equivalent spring and damping coefficients. Both simulation results and experimental results show the effectiveness of the designed EKF in estimating the required states. Copyright © 2006 by ASME.

关键词： Remote sensing

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External localization system for mobile robotics

External localization system for mobile robotics

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International Conference on Advanced robotics (ICAR)

作者： Tomáš Krajník Matías Nitsche Jan Faigl Tom Duckett Marta Mejail Libor Přeučil University of Lincoln Lincoln Lincolnshire GB Lab. of Robotics and Emb. Systems Univ. of Buenos Aires Argentina Dpt. of Computer Science and Engineering FEE Czech Technical Univ. in Prague Lincoln Centre for Autonomous Systems Univ. of Lincoln United Kingdom Intelligent and Mobile Robotics Group FEE Czech Technical Univ. in Prague

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

关键词： Cameras Three-dimensional displays Robots Mathematical model Estimation Equations Detectors

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Active tactile object exploration with Gaussian processes

Active tactile object exploration with Gaussian processes

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Zhengkun Yi Roberto Calandra Filipe Veiga Herke van Hoof Tucker Hermans Yilei Zhang Jan Peters Intelligent Autonomous Systems Lab Department of Computer Science Technische Universitat Darmstadt Hochschulstr. 10 64289 Germany School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 University of Utah Robotics Center School of Computing University of Utah USA

ISBN: (纸本)9781509037636

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.

关键词： Gaussian processes Touch tactile sensors grid point

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Hybrid control trajectory optimization under uncertainty

arXiv

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arXiv 2017年

作者： Pajarinen, Joni Kyrki, Ville Koval, Michael Srinivasa, Siddhartha Peters, Jan Neumann, Gerhard labs TU Darmstadt Germany Department of Electrical Engineering and Automation Aalto University Finland Robotics Institute Carnegie Mellon University United States IAS lab TU Darmstadt Germany Max Planck Institute for Intelligent Systems Tuebingen Germany Lincoln Centre for Autonomous Systems University of Lincoln United Kingdom

— Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable. Copyright © 2017, The Authors. All rights reserved.

关键词： Aerodynamics

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Modelling mobile robot navigation in 3D environments: camera-based stairs recognition in Gazebo

Modelling mobile robot navigation in 3D environments: camera...

引用

Moscow Workshop on Electronic and Networking Technologies (MWENT)

作者： Maksim Mustafin Tatyana Tsoy Edgar A. Martí nez-Garcí a Roman Meshcheryakov Evgeni Magid Intelligent Robotics Department Kazan Federal University Kazan Russia Industrial Engineering and Manufacturing Dept. Autonomous University of Ciudad Juarez Chihuahua Mexico Cyber-physical Lab V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia HSE University Moscow Russia

The task of sensory-based autonomous navigation of mobile robots requires data fusion from multiple sources in order to properly detect and recognize environmental obstacles. One of important issues mobile robots deal in a typical multi-level indoor environment is a stair well detection and negotiation. This paper presents ROS-based stairs detection implementation using onboard cameras of the Russian mobile crawler robot Servosila Engineer. Virtual experiments were performed in Gazebo environment with a single camera and a stereo camera.

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Multi-Robot System for autonomous Cooperative Counter-UAS Missions: Design, Integration, and Field Testing

Multi-Robot System for Autonomous Cooperative Counter-UAS Mi...

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

作者： Antonella Barisic Marlan Ball Noah Jackson Riley McCarthy Nasib Naimi Luca Strässle Jonathan Becker Maurice Brunner Julius Fricke Lovro Markovic Isaac Seslar David Novick Jonathan Salton Roland Siegwart Stjepan Bogdan Rafael Fierro Laboratory for Robotics and Intelligent Control Systems (LAR-ICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA ETH Zurich Autonomous Systems Lab (ASL) Zurich Switzerland Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (纸本)9781665456814

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS.

关键词： Scalability Robot vision systems autonomous aerial vehicles Cameras Software Safety Security

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NeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields

arXiv

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arXiv 2022年

作者： Sun, Jiankai Xu, Yan Ding, Mingyu Yi, Hongwei Wang, Chen Wang, Jingdong Zhang, Liangjun Schwager, Mac The School of Engineering Stanford University StanfordCA94305 United States The Chinese University of Hong Kong Hong Kong UC Berkeley United States Max Planck Institute for Intelligent Systems Tübingen Germany Baidu Inc China Robotics and Autonomous Driving Lab of Baidu Research SunnyvaleCA94089 United States

Neural Radiance Fields (NeRFs) have become a widely-applied scene representation technique in recent years, showing advantages for robot navigation and manipulation tasks. To further advance the utility of NeRFs for robotics, we propose a transformer-based framework, NeRF-Loc, to extract 3D bounding boxes of objects in NeRF scenes. NeRF-Loc takes a pre-trained NeRF model and camera view as input and produces labeled, oriented 3D bounding boxes of objects as output. Using current NeRF training tools, a robot can train a NeRF environment model in real-time and, using our algorithm, identify 3D bounding boxes of objects of interest within the NeRF for downstream navigation or manipulation tasks. Concretely, we design a pair of paralleled transformer encoder branches, namely the coarse stream and the fine stream, to encode both the context and details of target objects. The encoded features are then fused together with attention layers to alleviate ambiguities for accurate object localization. We have compared our method with conventional RGB(-D) based methods that take rendered RGB images and depths from NeRFs as inputs. Our method is better than the baselines. © 2022, CC BY.

关键词： Object detection

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Inertial parameters identification and joint torques estimation with proximal force/torque sensing

Inertial parameters identification and joint torques estimat...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Silvio Traversaro Andrea Del Prete Serena Ivaldi Francesco Nori Robotics Brain and Cognitive Sciences Department Istituto Italiano di Tecnologia Genoa Italy LAAS/CNRS Toulouse France Inria Villers-lès-Nancy France Intelligent Autonomous Systems Lab. TU Darmstadt Germany CNRS Loria UMR n.7503 and Université de Lorraine Loria France

ISBN: (纸本)9781479969241

Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.

关键词： Joints Force Torque Estimation Robot sensing systems

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