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 ...
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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.
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 descripti...
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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.
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...
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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).
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 theoret...
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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.
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...
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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.
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 proce...
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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.
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 ...
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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.
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...
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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.
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 moti...
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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.
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 possib...
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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.
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