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IEEE 12th International Symposium on autonomous decentralized systems (ISADS)

作者： Zheng, Lingxiang Zhou, Wencheng Tang, Weiwei Zheng, Xianchao Yang, Hui Pu, Shuxiang Li, Chenxiang Tang, Biyu Chen, Yinong Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ USA

ISBN: (纸本)9781479982615

We present a 3D indoor positioning system with foot mounted low cost MEMS sensors. The sensors includes the accelerometers, gyroscopes, and barometer. The output of accelerometers and gyroscopes are used to calculate the zero velocity update (ZUPT) and the movement of one step. The barometer is used to detect the altitude changes. A Kalman filter based framework is used to fusion the outputs of the sensors and estimate the non-linear errors of the position and heading, which increased over time. A particle filter is used to further reduce the errors. The test result shows that the designed system perform well.

关键词： indoor positioning ZUPT 3D Kalman filter particle filter

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Multispectral Stereoscopic robotic Head Calibration and Evaluation 2nd

Multispectral Stereoscopic Robotic Head Calibration and Eval...

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2nd International Workshop on Modelling and Simulation for autonomous systems (MESAS)

作者： Kocmanova, Petra Zalud, Ludek LTR Sro Brno Czech Republic Brno Univ Technol CEITEC CS-61090 Brno Czech Republic

ISBN: (纸本)9783319223834;9783319223827

The aim of the paper is to describe the data-fusion from optical sensors for mobile robotics reconnaissance and mapping. Data are acquired by stereo pair of CCD cameras, stereo pair of thermal imagers, and TOF (time-of-flight) range camera. The described calibration and data-fusion algorithms may be used for two purposes: visual telepresence (remote control) under extremely wide variety of visual conditions, like fog, smoke, darkness, etc., and for multispectral autonomous digital mapping of the robot's environment. The fusion is realized by means of spatial data from a TOF camera - the thermal and CCD camera data are comprised in one multispectral 3D model for mapping purposes or stereo image presented to a binocular, head-mounted display. The data acquisition is performed using a sensor head containing the mentioned 5 cameras, which is placed on 3 degrees-of-freedom (DOF) manipulator on Orpheus-X3 reconnaissance robot;both the head and the robot were developed by our working group. Although the fusion is used for two different tasks - automatic environment mapping and visual telepresence, the utilized calibration and fusion algorithms are, in principle, the same. Both geometrical calibration of each sensor, and the mutual positions of the sensors in 6-DOFs are calculated from calibration data acquired from newly developed multispectral calibration pattern. For the fusion the corresponding data from the CCD camera and the thermal imager are determined via homogeneous and perspective transformations. The result consists of image containing aligned data from the CCD camera and the thermal imager for each eye or a set of 3D points supplied by color and thermal information. Precision of data-fusion is determined both by calculation from mathematical model and experimental real-scenario evaluation. Precision of data-fusion and subsequently calibration is evaluated by real-environment measurements with help of newly developed multispectral targets.

关键词： Calibration Multispectral fusion Telepresence Mobile robot

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Bayesian modeling for decentralized UAV control and task allocation

Bayesian modeling for decentralized UAV control and task all...

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AIAA Infotech @ Aerospace 2015

作者： Hening, Sebastian Reguiro, Pedro Teodorescu, Mircea Rodriguez, Abel Nguyen, Nhan Ippolito, Corey Fladeland, Matthew Johnson, Matthew S. University of California Santa Cruz Santa CruzCA95064 United States NASA Ames Research Cente Moffett FieldCA94035 United States Department of Computer Engineering United States Department of Applied Mathematics and Statistics United States Advanced Control and Evolvable Systems Group United States Biospheric Science Branch Earth Science Division United States

ISBN: (数字)9781624103384

ISBN: (纸本)9781624103384

The current research represents a first step towards developing a decentralized network of small autonomous, intelligent and inexpensive unmanned aerial vehicles (UAV), which could be used for a variety of scientific missions where measurements from a distributed network of nodes could signiflcantly improve the assessment. The paper presents an adaptive airborne sensing platform (e.g., UAV) that could be used to fuse the information acquired through multiple sensing modalities (obtained from sensors located close to or inside the phenomena) to monitor environmental processes over space and time. The sensing platform is able to continuously reconflgure its trajectory according to the circumstances (e.g., continuously evolving scientific phenomena) to optimize the location of individual sensing waypoints. As proof of concept and validation, we will apply the proposed sensing approach to monitoring volcanic plumes. The paper focuses on (i) developing a Bayesian statistical method to create sensor fusion algorithms that is used to model the plume and guide the UAV in an optimal way (ii) implementing a distributed task allocation scheme which is able guide all the UAVs efficiently and optimize the UAV paths for various criteria. © 2015, American Institute of Aeronautics and Astronautics Inc. All rights received.

关键词： Unmanned aerial vehicles (UAV)

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Swarm Underwater Acoustic 3D Localization: Kalman vs Monte Carlo

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INTERNATIONAL JOURNAL OF ADVANCED robotic systems 2015年第7期12卷 102-102页

作者： Taraglio, Sergio Fratichini, Fabio ENEA Ctr Ric Casaccia Rome Italy Univ Roma Tor Vergata Dept Elect Engn Rome Italy

Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given.

关键词： Distributed control systems Mobile Robots and Intelligent autonomous systems Underwater Robots sensor fusion Swarm Localization

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Data fusion Algorithm for the Altitude and Vertical Speed Estimation of the VTOL Platform

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JOURNAL OF INTELLIGENT & robotic systems 2014年第1-2期74卷 413-420页

作者： Blachuta, Marian Czyba, Roman Janusz, Wojciech Szafranski, Grzegorz Silesian Tech Univ PL-44100 Gliwice Poland

An approach to the autonomous of the realization VTOL platform take-off and landing significantly simplifies the operator labor to control such device. At the same time, implementation of these control scenarios allows to perform these tasks under failure conditions (for example communication breakdowns). One condition of proper operation of the vertical movement control system is the ability to provide reliable information about the altitude of the controlled platform. In this paper one of the solutions for obtaining estimate of the altitude based on sensor data fusion is presented. Proposed scheme uses information obtained from pressure sensor, inertial measurement unit, ultrasonic sensor and GPS, all of these instruments are nowadays very often mounted on VTOL platforms.

关键词： Altitude measurement Data fusion Complementary filter Kalman filter

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Fuzzy gain scheduled EKF for model-based Skid-Steered Mobile Robot

Fuzzy gain scheduled EKF for model-based Skid-Steered Mobile...

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International Conference on autonomous Robots and Agents, ICARA

作者： Bourhane Kadmiry Callaghan Innovation Auckland

This article describes an approach to autonomous robotic for agricultural applications. Technological setup aims at stable navigation based on estimation through Extended Kalman filtering (EKF), to enforce robust Skid-Steered Mobile Robot (SSMR) navigation. The scientific contribution is the implementation of two model-based estimators, using EKF algorithms, one on a nonlinear model, and one on a piece-wise linearized robot model. The later is a Fuzzy Gain Scheduled (FGS)-based development. The process is taking into account tire-road modelling of friction forces in order to improve model performance. State estimation and correction using sensor data fusion (Odometry-IMU-GPS) is considered, to improve the SSMR control in critical motions, reducing inherent drifts due to skid-steer properties; for the purpose of better regulation and tracking control designs. Whilst the experimental results demonstrated the usefulness of FGS approach for optimal EKF estimation, further modelling and live testing are required to determine robot ability to cope with different scenarios in naturally varying environment.

关键词： Wheels Robot sensing systems Mobile robots Friction Mathematical model Kinematics

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An attitude estimation system for amphibious spherical robots

An attitude estimation system for amphibious spherical robot...

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IEEE International Conference on Mechatronics and Automation

作者： Liwei Shi Rui Xiao Shuxiang Guo Ping Guo Shaowu Pan Yanlin He Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Infor-motion Technology. Beiiino Institute ot Technoloov Beiiino China Key Laboratory Biomim tic RobotsSystems Ministry of Education Beijing Institute of Technology China Faculty of Engineering Kagawa University Takamatsu Kagawa Japan Beijing Institute of Technology Beijing Beijing CN

ISBN: (纸本)9781479970995

As one of the most effective tools for exploring the ocean, automatic underwater vehicles have attracted a lot of attentions for years. But some key problems have not been solved properly. It is especially difficult to design underwater vehicles in small size. In this paper, three inertial sensors were adopted to fabricate an attitude estimation system, which provided posture information for our amphibious spherical robot to realize motion control and autonomous navigation. The pitch, roll and heading angel were acquired from current robot attitude matrix, which was calculated from the quaternion algorithm. And the attitude was corrected by the fusion of accelerometer and magnetic sensor. Experimental results verified the validation and precision of the robotic attitude estimation system. It has manifested that the system is effective to realize the robot control and navigation.

关键词： Robot sensing systems Mathematical model Magnetic sensors sensor systems Quaternions

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Map API - scalable decentralized map building for robots

Map API - scalable decentralized map building for robots

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IEEE International Conference on robotics and Automation (ICRA)

作者： Titus Cieslewski Simon Lynen Marcin Dymczyk Stéphane Magnenat Roland Siegwart ETH Zurich Autonomous Systems Lab

Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most existing approaches use a central entity that collects and manages the information from all agents. Often, the raw sensor data of one robot needs to be made available to processing algorithms on other agents due to the lack of computational resources on that robot. Unfortunately, network latency and low bandwidth in the field limit the generality of such an approach and make multi-robot map building a tedious task. In this paper, we present a distributed and decentralized back-end for concurrent and consistent robotic mapping. We propose a set of novel approaches that reduce the bandwidth usage and increase the effectiveness of inter-robot communication for distributed mapping. Instead of locking access to the map during operations, we define a version control system which allows concurrent and consistent access to the map data. Updates to the map are then shared asynchronously with agents which previously registered notifications. A technique for data lookup is provided by state-of-the-art algorithms from distributed computing. We validate our approach on real-world datasets and demonstrate the effectiveness of the proposed algorithms.

关键词： Distributed databases Spatial indexes Robot sensing systems Buildings Bandwidth Protocols

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Toward controlling Perturbations in robotic sensor Networks

Toward Controlling Perturbations in Robotic Sensor Networks

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Conference on Next-Generation Robots and systems

作者： Banerjee, Ashis Gopal Majumder, Saikat Ray Gen Elect Global Res Niskayuna NY 12309 USA

ISBN: (纸本)9781628410532

robotic sensor networks (RSNs), which consist of networks of sensors placed on mobile robots, are being increasingly used for environment monitoring applications. In particular, a lot of work has been done on simultaneous localization and mapping of the robots, and optimal sensor placement for environment state estimation(1). The deployment of RSNs, however, remains challenging in harsh environments where the RSNs have to deal with significant perturbations in the forms of wind gusts, turbulent water flows, sand storms, or blizzards that disrupt inter-robot communication and individual robot stability. Hence, there is a need to be able to control such perturbations and bring the networks to desirable states with stable nodes (robots) and minimal operational performance (environment sensing). Recent work has demonstrated the feasibility of controlling the non-linear dynamics in other communication networks like emergency management systems and power grids by introducing compensatory perturbations to restore network stability and operation(2). In this paper, we develop a computational framework to investigate the usefulness of this approach for RSNs in marine environments. Preliminary analysis shows promising performance and identifies bounds on the original perturbations within which it is possible to control the networks.

关键词： sensor networks autonomous underwater vehicles acoustic communication decentralized control non-linear dynamics

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sensor Data fusion Using Unscented Kalman Filter for VOR-Based Vision Tracking System for Mobile Robots

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14th Annual Conference on Towards autonomous robotic systems (TAROS)

作者： Anjum, Muhammad Latif Ahmad, Omar Bona, Basilio Cho, Dong-il Dan Politecn Torino Dept Mech & Aerosp Engn DIMEAS Corso Duca Abruzzi 24 I-10129 Turin Italy Politecn Torino Dept Control & Comp Engn DAUIN I-10129 Turin Italy Seoul Natl Univ Dept Elect Engn & Comp Sci ASRI ISRC Seoul 151742 South Korea

ISBN: (纸本)9783662436455;9783662436448

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope, and encoders to calculate robot motion information. The Unscented Kalman Filter is used as an efficient sensor fusion algorithm. The UKF is an advanced filtering technique which outperforms widely used Extended Kalman Filter (EKF) in many applications. The system is able to compensate for the slip errors by switching between two different UKF models built for slip and no-slip cases. Since the accelerometer error accumulates with time because of the double integration, the system uses accelerometer data only for the slip case UKF model. Using sensor fusion by UKF, the position and orientation of the robot is estimated and is used to rotate the camera mounted on top of the robot towards a fixed target. This concept is derived from the vestibulo-ocular reflex (VOR) of the human eye. The experimental results show that the system is able to track the fixed target in various robot motion scenarios including the scenario when an intentional slip is generated during robot navigation.

关键词： sensor fusion Unscented Kalman filter VOR Vision tracking systems

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