A VR-Based Simulator Using Motion Feedback of a Real Powered Wheelchair for Evaluation of Autonomous Navigation Systems

2021 ◽  
Author(s):  
Hiroshi Yoshitake ◽  
Kazuto Futawatari ◽  
Motoki Shino
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems ◽  
Powered Wheelchair

Geophysical Support of Magnetometer Autonomous Navigation Systems

2020 ◽  
Author(s):  
Vladimir T. Minligareev ◽  
Elena N. Khotenko ◽  
Vadim V. Tregubov ◽  
Tatyana V. Sazonova ◽  
Vaclav L. Kravchenok
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems

scholarly journals A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Information ◽  
Dynamic Calibration ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Additional Information ◽  
Two Systems ◽  
Dynamic Calibration Method

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.

Accelerating GPU Message Communication for Autonomous Navigation Systems

2021 ◽  
Author(s):  
Hao Wu ◽  
Jiangming Jin ◽  
Jidong Zhai ◽  
Yifan Gong ◽  
Wei Liu
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems ◽  
Message Communication

scholarly journals The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System

Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 4 ◽  
Author(s):  
Viacheslav Moskalenko ◽  
Alona Moskalenko ◽  
Artem Korobov ◽  
Viktor Semashko
Keyword(s):  
Autonomous Navigation ◽  
Search Algorithm ◽  
Visual Navigation ◽  
Frame Rate ◽  
Raspberry Pi ◽  
Navigation Systems ◽  
Training Algorithms ◽  
Translation Error ◽  
Deep Model ◽  
And Training

Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.

scholarly journals Neural Network-Based Aircraft Conflict Prediction in Final Approach Maneuvers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1708
Author(s):  
Rafael Casado ◽  
Aurelio Bermúdez
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Autonomous Navigation ◽  
Decision Support Tool ◽  
Air Traffic ◽  
Navigation Systems ◽  
Support Tool ◽  
Final Approach ◽  
Aircraft Trajectories ◽  
Available Information

Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems.

scholarly journals Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1269 ◽  
Author(s):  
Kenan Liu ◽  
Wuyun Zhao ◽  
Bugong Sun ◽  
Pute Wu ◽  
Delan Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Sprinkler Irrigation ◽  
The Self ◽  
Maximum Deviation ◽  
Navigation Systems ◽  
Adaptive Kalman Filter ◽  
Average Deviation ◽  
Positioning Accuracy ◽  
Promising Development

Autonomous navigation for agricultural machinery has broad and promising development prospects. Kalman filter technology, which can improve positioning accuracy, is widely used in navigation systems in different fields. However, there has not been much research performed into navigation for sprinkler irrigation machines (SIMs). In this paper, firstly, a self-developed SIM is introduced. Secondly, the kinematics model is established on the platform of the self-developed SIM, and the updated Sage–Husa adaptive Kalman filter, which is an accurate and real-time self-adaptive filtering algorithm, is applied in the navigation of the SIM with the aim of improving the positioning accuracy. Finally, experiment verifications were carried out, and the results show that the self-developed SIM has good navigation performance. Besides this, the influence of abnormal observations on the positioning accuracy of the system can be restrained by using the updated Sage–Husa adaptive Kalman filter. After using the updated Sage–Husa adaptive Kalman filter for the SIM, the maximum deviation between the SIM and the predetermined path is 0.18 m, and the average deviation is 0.08 m; these deviations are within a reasonable range. This proves that the updated Sage–Husa adaptive Kalman filter is applicable for the navigation of sprinkler irrigation machines.

scholarly journals Application of microwave motion sensors in navigation systems for ground vehicle

2019 ◽  
Vol 30 ◽  
pp. 12004 ◽  
Author(s):  
Dmitrii Khablov
Keyword(s):  
Navigation System ◽  
Autonomous Navigation ◽  
Velocity Vector ◽  
Continuous Measurement ◽  
Navigation Systems ◽  
Motion Sensors ◽  
Ground Vehicle ◽  
Cumulative Error ◽  
System A ◽  
Transport Management

Effective land transport management in a controlled and unmanned mode is impossible without its accurate and continuous positioning. The paper discusses the possibility of increasing this accuracy in the absence or uncertain reception of signals from satellites of the global navigation system. Moreover, the use of an additional self-navigation inertial system to solve this problem in this case is not justified for reasons of accuracy and cost. Therefore, as an alternative autonomous navigation system, a solution based on radar Doppler sensors of modular type is proposed. The methods of measuring the velocity vector and the algorithm of direct continuous measurement of displacements are considered. It is shown that the latter measurement option can significantly reduce the cumulative error when positioning vehicles.

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