A new method of AGV navigation based on Kalman Filter and a magnetic nail localization

Song, Zhi; Wang, Xinyu; Xu, Tiantian; Sun, Jianquan; Gao, Qinshi; He, Yong

doi:10.1109/robio.2016.7866447

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…Usually, these methods rely on beacons or base stations. For example, the WiFi-based positioning requires to predeploy a set of APs in the environment [27], and the magnetic field-based positioning method requires to preembed magnetic nails on the floor [28,29]. At present, WiFi-based and Bluetoothbased methods mainly use the attenuation of signals to estimate distance, probably resulting in high positioning errors reaching 10 meters.…”

Section: Related Workmentioning

confidence: 99%

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Ding

et al. 2021

Mathematical Problems in Engineering

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In this paper, we seek to provide unmanned ground vehicles with positioning service using ultrawideband (UWB) technology, a high-accuracy positioning approach. UWB is chosen for two distinct reasons. First, it does not rely on global navigation satellite systems like GPS, making it able to be applied indoors or in an environment where GPS signal is unstable. Second, it is immune to interference from other signals and accurate enough to guide unmanned ground vehicles moving precisely in a complex environment within a narrow road. In this paper, three UWB base stations are aggregated as a group in a 2D space for localization. A large number of tests are performed with a UWB base station cluster in order to validate its positioning performance. Based on the experiment results, we further develop a dynamic particle swarm optimization-based algorithm and a genetic algorithm to deploy multiple clusters of UWB base stations to cover an area of interest. The performance of the proposed algorithms has been tested through a series of simulations. Finally, experiments using unmanned ground vehicles are carried out to validate the localization performance. The results confirm that the robots can follow complex paths accurately with the proposed UWB-based positioning system.

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Section: Related Workmentioning

confidence: 99%

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Ding

et al. 2021

Mathematical Problems in Engineering

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“…Using magnet sensors and magnet tape is another early development that has long been used for localization in mobile robot applications. The method 12 is one of the state-of-the-art methods for the localization and navigation of an unmanned mobile robot. The main contribution of that work is a magnetic nail based on a Kalman filter (KF) and a method to improve localization accuracy by considering the kinematics of the mobile robots on the state equation, angle, and position estimated by a gyroscope and an encoder.…”

Section: Related Workmentioning

confidence: 99%

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

et al. 2021

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SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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“…Localization of mobile robots refers to the process in which mobile robots estimate their positions and pose angles through sensors’ perception techniques [ 4 , 5 ]. Recent progress in autonomous vehicles and LiDAR devices has dramatically reduced the cost of LiDAR hardware and pushed the LiDAR-based SLAM to become the most promising approach for self-driving vehicles and robots with great flexibility [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

Wang

Chen

Ding³

et al. 2021

Sensors

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This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.

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A new method of AGV navigation based on Kalman Filter and a magnetic nail localization

Cited by 18 publications

References 11 publications

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

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