autonomous mobile robots need accurate localization techniques to perform assigned task. Radio Frequency Identification Technology (RFID) has become one of the main means to construct a real-time localization system. ...
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autonomous mobile robots need accurate localization techniques to perform assigned task. Radio Frequency Identification Technology (RFID) has become one of the main means to construct a real-time localization system. Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. This paper proposes an auto-localization system for indoor mobile robot using passive RFID. The proposed system reads any three different RFID tags which have a known location. The current location can be estimated using the Time Difference of Arrival (TDOA) scheme. In order to improve the system accuracy, the proposed system fuses the TDOA scheme for the three tags. A Kalman filter is used to minimize the estimated error and predict the next location. The simulation results validate the proposed framework.
A smart fire detection system is a necessary part of a smart manufacturing facility. autonomous robots may be deployed to seek out a potential source of fire in the industrial environment, approach, investigate and de...
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A smart fire detection system is a necessary part of a smart manufacturing facility. autonomous robots may be deployed to seek out a potential source of fire in the industrial environment, approach, investigate and declare the presence or absence of fire based on several sensorfusion techniques. A novel approach of the above mentioned topic is described in this paper. A new technique, introduced as "Modified Voting Logic" is explained. The robot uses sensor readings comparison to approach the source and a smart obstacle avoidance system to avoid obstacles together with Modified Voting Logic to declare a fire threat. (C) 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
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 ...
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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.
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-o...
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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.
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 ...
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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&...
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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.
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 allow...
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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.
This article describes an approach to autonomousrobotic for agricultural applications. Technological setup aims at stable navigation based on estimation through Extended Kalman filtering (EKF), to enforce robust Skid...
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This article describes an approach to autonomousrobotic 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.
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 t...
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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.
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 con...
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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.
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