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LiDAR Based Obstacle detection for Snow Groomers

IFAC-PapersOnLine 2020年第2期53卷 15469-15474页

作者： L. Onesto M. Corno L. Franceschetti E. Hokka S.M. Savaresi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano via G. Ponzio 34/5 20133 Milan Italy TTControl GmbH - Ensuring Reliable Networks FN: 246 710i Handelsgericht Wien

This paper presents an obstacle detection system for snow groomers. The system is based on a 2D solid-states LiDAR sensor mounted on the top of the cabin. The measurements describe the surrounding environment through an Occupancy Grid framework, which is extended for this particular case study. The proposed approach set the occupancy probability of the surrounding environment based on the expected height of the obstacle. The method is extensively analyzed through experimental test on a snow groomer.

关键词： Robot navigation estimation algorithms Navigation system Obstacle detection

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Prospects of simultaneous localization and mapping algorithms application in imagery intelligence 12

Prospects of simultaneous localization and mapping algorithm...

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12th Conference on Reconnaissance and Electronic Warfare Systems (CREWS)

作者： Slowak, Pawel Kaniewski, Piotr Mil Univ Technol Ul Gen Witolda Urbanowicza 2 PL-00908 Warsaw Poland

ISBN: (数字)9781510627864

ISBN: (纸本)9781510627864

The article contains an analysis of potential prospects of simultaneous localization and mapping (SLAM) algorithms application in imagery intelligence (IMINT). The first part of the paper presents a detailed description of the SLAM problem. Diverse solutions to the simultaneous localization and mapping problem and related research over the years are presented. The most promising of SLAM approaches are pointed out. To facilitate SLAM analysis, the problem is partitioned into three parts. First, various SLAM estimation techniques are characterized. A mathematical theory behind the usage of parametric filters, non-parametric filters, and least squares method is presented. Further, differences between SLAM algorithms are described in terms of various sensors used on-board SLAM platforms for the examination of the environment. The examination is commonly addressed as landmark extraction. A separate part of the paper discusses the image processing in SLAM. The last part of the SLAM analysis is dedicated to various approaches to map presentation. Further, the properties of SLAM techniques are characterized in terms of their potential benefits to IMINT. Prospects of increased efficiency, accuracy and safety of intelligence gathering process are discussed.

关键词： SLAM intelligence UAV image processing estimation algorithms

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Robot Localisation based on Phase Measures of backscattered UHF-RFID Signals

Robot Localisation based on Phase Measures of backscattered ...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Magnago, V Palopoli, L. Fontanelli, D. Macii, D. Motroni, A. Nepa, P. Buffi, A. Tellini, B. Univ Trento Dept Infonnat Engn & Comp Sci Trento Italy Univ Trento Dept Ind Engn Trento Italy Univ Pisa Dept Informat Engn Pisa Italy Univ Pisa Dept Energy Syst Terr & Construct Engn Pisa Italy

ISBN: (纸本)9781538634608

Indoor localisation is currently regarded as one of the most useful services offered to human beings and robotics agents, as it can support a variety of applications. Among all the possible sensing solutions developed to address this problem (which is usually made challenging by the complex, heterogeneous and crowded nature of indoor environments), RFID systems based on passive tags are very promising due to their relatively low cost and the ease of deployment. In this paper, a theoretical analysis of the localisation problem using Ultra High Frequency (UHF) RFID tags for mobile robots is considered. The feasibility of the proposed approach is demonstrated by analysing the local nonlinear observability of the system at hand, despite the inherent ambiguity of the phase of backscattered RF signals, which can be measured by a system installed on the moving agent. The validity of the analysis and the practicality of this localisation approach is further confirmed by using a position tracking estimator based on an Unscented Kalman Filter (UKF).

关键词： RFID localisation estimation algorithms observability analysis

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Computationally Efficient Pitch and Roll estimation Using a Unit Direction Vector

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2018年第2期67卷 459-465页

作者： Suh, Young Soo Univ Ulsan Dept Elect Engn Ulsan 44610 South Korea

This paper introduces a new attitude estimation algorithm for pitch and roll angles. Pitch and roll angles are represented by a unit vector, and its estimation error is estimated in the Kalman filter. The main theoretical contribution is that the error covariance equations are simplified to scalar equations. Thus, the proposed algorithm is computationally efficient. The proposed algorithm is also applied to vertical movement estimation. Simulation and experiment results show the effectiveness of the proposed method.

关键词： Attitude estimation algorithms inertial sensors Kalman filters orientation

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MMAE/LQR Yaw Control System of a Quadrotor for Constant Unknown Inertia 21

MMAE/LQR Yaw Control System of a Quadrotor for Constant Unkn...

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21st International-Federation-of-Automatic-Control (IFAC) Symposium on Automatic Control in Aerospace (ACA)

作者： Outeiro, Pedro Cardeira, Carlos Oliveira, Paulo Univ Lisbon Inst Super Teenieo IDMEC Lisbon Portugal

This paper presents a methodology for angular control of a quadrotor that transports a constant unknown load, given the estimates on both inertia and angular velocity, based on measurements from an indoor multi-camera motion capture system and a gyroscope. The proposed control method is an LQR controller and the proposed estimation method is a Multi-Model Adaptive Estimator (MMAE). The control system obtained is validated both in simulation and experimentally, resorting to an off-the-shelf commercially available quadrotor. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： Autonomous mobile robots estimation algorithms Parameter identification Sensor Fusion Embedded systems

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Pitch Dynamics Analysis for an Agricultural Tractor with Image Processing Validation through an Off-Board Camera 9th

Pitch Dynamics Analysis for an Agricultural Tractor with Ima...

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9th IFAC International Symposium on Advances in Automotive Control (AAC)

作者： Onesto, L. Corno, M. Savaresi, S. Politecn Milan Dipartimento Elettron Informaz & Bioingn Via G Ponzio 34-5 I-20133 Milan Italy

This work proposes an analysis of the pitch dynamics of a heavy-duty vehicle, namely an agricultural tractor. Considering maneuvers performed on a flat-asphalt surface, the analysis is performed through an image processing approach. The analysis focuses on the cabin displacement and on the vehicle body displacement. Moreover, the tires compression and the vehicle longitudinal slip are evaluated. The analysis shows how the cabin and the body displacements change in function of the vehicle longitudinal acceleration and how, due to the tires compression, the cabin and the body can oscillate, at the end of a braking maneuver. The results are used to evaluate the feasibility of a road gradient estimator based on the inertial measurement of a mono axial accelerometer installed in the cabin. In particular, the cabin displacement needs to be considered and an additional sensor which measures the cabin speed is required to avoid a drop of performance. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Vehicle dynamics Pitch estimation algorithms Kalman Filters Image Processing

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Multi-agent Localization of A Common Reference Coordinate Frame: An Extrinsic Approach 8th

Multi-agent Localization of A Common Reference Coordinate Fr...

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8th IFAC Workshop on Distributed estimation and Control in Networked Systems (NECSYS)

作者： Quoc Van Tran Ahn, Hyo-Sung Gwangju Inst Sci & Technol Sch Mech Engn Gwangju South Korea

This paper studies the problem of multi-agent cooperative localization of a common reference coordinate frame in R-3. Each agent in a system maintains a body-fixed coordinate frame and its actual frame transformation (translation and rotation) from the global coordinate system is unknown. The mobile agents aim to determine their trajectories of rigid-body motions (or the frame transformations, i.e., rotations and translations) with respect to the global coordinate frame up to a common frame transformation by using local measurements and information exchanged with neighbors. We present two frame localization schemes which compute the rigid-body motions of the agents with asymptotic stability and finite-time stability properties, respectively. Under both localization laws, the estimates of the frame transformations of the agents converge to the actual frame transformations almost globally and up to an unknown constant transformation bias. Finally, simulation results are provided. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Frame Localization Distributed computation estimation algorithms Sensor networks Measurement and instrumentation

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On the Effect of Stabilization Methods for Quaternion Invariants on the Uncertainty in Optimization-based estimation

On the Effect of Stabilization Methods for Quaternion Invari...

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Joint Meeting of the 2nd IFAC Workshop on Linear Parameter Varying Systems (LPVS) / 9th IFAC Symposium on Robust Control Design (ROCOND)

作者： Girrbach, Fabian Hol, Jeroen D. Zandbergen, Raymond Verschueren, Robin Bellusci, Giovanni Diehl, Moritz Xsens Technol BV NL-7521 PR Enschede Netherlands Univ Freiburg Dept Microsyst Engn IMTEK D-79110 Freiburg Germany

The handling of 3D orientations is a common element in many problems that arise in the estimation and control of dynamic systems. Over-parametrizations such as unit quaternions are commonly used to avoid singularities but come with the property of an invariant which needs to be preserved. By using numerical optimization methods, these invariants are subject to numeric errors and require stabilization. In this work, we adopt methods known from optimal control for the problem of state estimation. We present an optimization-based attitude estimator using the measurements of an inertial measurement unit and evaluate the performance of a first order stabilization of the invariant by modifying the dynamics. The uncertainties of the estimator are analyzed for different configurations of the proposed stabilization. Finally, we show how the stabilization affects the estimation of parameters and justify the use of an additional equality constraint for the invariant to yield more robust and consistent results. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： estimation algorithms optimal estimation invariants attitude algorithms

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Model Ship Control and estimation of Full-scale Propeller Torque in Wind and Waves

Model Ship Control and Estimation of Full-scale Propeller To...

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11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS)

作者： Ueno, Michio Suzuki, Ryosuke Tsukada, Yoshiaki Natl Maritime Res Inst Mitaka Tokyo 1810004 Japan

This paper presents free-running model ship test data estimating full-scale propeller torque fluctuating in wind and waves. The model ship control using the auxiliary thruster ensures the similarity of model ship motion to full scale that leads the similar propeller situation. The wind load simulator controls six duct fans on the model ship for simulating the wind loads. The estimation algorithm converts the measured model propeller torque to full scale assuming no scale effect on the wave component in the effective inflow velocity to propeller. The analysis reveals the differences of the fluctuating propeller torque between model and full-scale ships. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Model ship control Scale effect Wind and waves Propeller torque estimation algorithms

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Particle Gaussian mixture filters-I

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AUTOMATICA 2018年 98卷 331-340页

作者： Raihan, Dilshad Chakravorty, Suman Texas A&M Univ Dept Aerosp Engn College Stn TX 77840 USA

In this paper, we propose a particle based Gaussian mixture filtering approach for nonlinear estimation that is free of the particle depletion problem inherent to most particle filters. We employ an ensemble of possible state realizations for the propagation of state probability density. A Gaussian mixture model (GMM) of the propagated uncertainty is then recovered by clustering the ensemble. The posterior density is obtained subsequently through a Kalman measurement update of the mixture modes. We prove the convergence in probability of the resultant density to the true filter density assuming exponential forgetting of initial conditions. The performance of the proposed filtering approach is demonstrated through several test cases and is extensively compared to other nonlinear filters. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： estimation algorithms Nonlinear filters Gaussian mixture models Multimodality Particle filtering State estimation Machine learning Curse of dimensionality Kalman filters

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