Low-Altitude and High-Speed Terrain Tracking Method for Lightweight AUVs

2018 ◽  
Vol 30 (6) ◽  
pp. 971-979 ◽  
Author(s):  
Toshihiro Maki ◽  
Yukiyasu Noguchi ◽  
Yoshinori Kuranaga ◽  
Kotohiro Masuda ◽  
Takashi Sakamaki ◽  
...  
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Autonomous Underwater Vehicles ◽  
Vertical Plane ◽  
Potential Method ◽  
Visual Observation ◽  
Doppler Velocity ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Constant Altitude

This paper proposes a new method for cruising-type autonomous underwater vehicles (AUVs) to track rough seafloors at low altitudes while also maintaining a high surge velocity. Low altitudes are required for visual observation of the seafloor. The operation of AUVs at low altitudes and high surge velocities permits rapid seafloor imaging over a wide area. This method works without high-grade sensors, such as inertial navigation systems (INS), Doppler velocity logs (DVL), or multi-beam sonars, and it can be implemented in lightweight AUVs. The seafloor position is estimated based on a reflection intensity map defined on a vertical plane, using measurements from scanning sonar and basic sensors of depth, attitude, and surge velocity. Then, based on the potential method, a reference pitch angle is generated that allows the AUV to follow the seafloor at a constant altitude. This method was implemented in the AUV HATTORI, and a series of sea experiments were carried out to evaluate its performance. HATTORI (Highly Agile Terrain Tracker for Ocean Research and Investigation) is a lightweight and low-cost testbed designed for rapid and efficient imaging of rugged seafloors, such as those containing coral reefs. The vehicle succeeded in following a rocky terrain at an altitude of approximately 2 m with a surge velocity of approximately 0.8 m/s. This paper also presents the results of sea trials conducted at Ishigaki Island in 2017, where the vehicle succeeded in surveying the irregular, coral-covered seafloor.

scholarly journals DEVELOPING AFFORDABLE BATHYMETRIC ANALYSIS TECHNIQUES USING NON-CONVENTIONAL PAYLOAD FOR CULTURAL HERITAGE INSPECTIONS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 807-811
Author(s):  
F. Mugnai ◽  
A. Ridolfi ◽  
M. Bianchi ◽  
M. Franchi ◽  
G. Tucci
Keyword(s):  
Cultural Heritage ◽  
Low Cost ◽  
Autonomous Underwater Vehicles ◽  
Cost Effective ◽  
Navigation Systems ◽  
New Techniques ◽  
Sea Floor ◽  
Analysis Techniques ◽  
Spatial Reconstruction ◽  
Underwater Navigation

<p><strong>Abstract.</strong> This paper focuses on the implementation of new techniques for bathymetric inspections. The scope is the exploitation of sensors, usually and commonly used for navigation, namely the altimeter and the Forward Looking Sonar (FLS), for identifying objects which are laying on the sea floor. In this particular framework, the low spatial resolution and coverage of these sensors have been enhanced through the application of classical computational geometry. The altimeter and the FLS are part of the most common underwater navigation systems, and they are vastly mounted on Autonomous Underwater Vehicles. Although they are not designed for this kind of accurate measurements and for 3D spatial reconstruction, they are quite cost-effective if compared with standard multibeam acoustic systems. Developing a technique for exploiting such an affordable low cost and widely used sensor will empower the Cultural Heritage community of users, giving a feasible opportunity to perform effective archaeological campaigns also within small funded projects.</p>

scholarly journals An acoustic-based approach for real-time deep-water navigation of an AUV

2018 ◽  
Author(s):  
A Tesei ◽  
M Micheli ◽  
A Vermeij ◽  
G Ferri ◽  
M Mazzi ◽  
...  
Keyword(s):  
Real Time ◽  
Deep Water ◽  
Autonomous Underwater Vehicles ◽  
Accurate Estimate ◽  
Doppler Velocity ◽  
Positioning System ◽  
Navigation Systems ◽  
June July ◽  
Proprioceptive Sensors ◽  
Acoustic Positioning

Navigation of Autonomous Underwater Vehicles (AUVs) remains a challenge due to the impossibility to use radio frequency signals and Global Positioning System (GPS). Navigation systems usually integrate different proprioceptive sensors to estimate the asset and the speed of the vehicle. In particular, the Doppler Velocity Log (DVL) is fundamental during the navigation to have an accurate estimate of the vehicle’s speed. This work addresses the enhancement of the navigation performance of an AUV through the development of a Deep Water Navigation Filter (DWNF). The DWNF is able to work in those scenarios where traditional navigation sensors show their limits: e.g., deep waters where DVL bottom lock cannot be achieved, or areas where the use of traditionally used static and dedicated beacons is incompatible with the mission requirements. The proposed approach exploits the concept of using a network of vehicles cooperatively supporting each other for their navigation. Several types of measurements coming from the different nodes (i.e. acoustic positioning system such as ship-mounted SSBL acoustic positioning system, USBL, range measurements from the different nodes) are fused in an Extended Kalman Filter framework with the odometry data. DWNF pushes forward the idea of using a network of robotic assets as a provider of navigation services allowing more flexible and robust operations of the deployed system. The approach has been tested at sea during several experiments. We report here results from DWNF running successfully in real-time on the NATO STO-Centre for Maritime Research and Experimentation (CMRE) vehicles during the Dynamic Mongoose’17 experimentation off the South coast of Iceland (June-July 2017). 

Adaptive Anti-Disturbance Method for Magnetometer and INS Integration in a Road Vehicle

2019 ◽  
Vol 72 (06) ◽  
pp. 1513-1532
Author(s):  
Zongkai Wu ◽  
Wei Wang
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Navigation Systems ◽  
Extend Kalman Filter ◽  
Inertial Navigation Systems ◽  
Road Vehicle ◽  
Practical Applications ◽  
Short And Long Term ◽  
Magnetic Disturbances

The integration of magnetometers and Inertial Navigation Systems (INS) is widely used in low-cost navigation systems. However, even if the system has been calibrated, random magnetic disturbances still appear in practical applications, which lead to large heading errors. To solve this problem, an adaptive anti-disturbance method to overcome random magnetic disturbance is proposed. First, disturbances are classified and analysed in detail based on actual road vehicle driving data. Then an Adaptive Robust Extend Kalman Filter (AREKF) is designed to resist sudden disturbances. However, an AREKF may accumulate errors slowly when a long-term disturbance exists. Considering this situation, this paper proposes that AREKF is used to maintain accuracy in the early stages, at the same time as the magnetometer is quickly calibrated with a Kalman filter. Then, the new magnetometer parameters are put into the AREKF to suppress long-term disturbances. Finally, cascading these two modules, not only the sudden disturbance can be overcome, but the situation of long-term disturbances can be suppressed. The results of simulation and an actual driving test show that the proposed method can effectively overcome random magnetic disturbances in both the short and long term.

scholarly journals Particle-Filter-Based WiFi-Aided Reduced Inertial Sensors Navigation System for Indoor and GPS-Denied Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
M. M. Atia ◽  
M. J. Korenberg ◽  
A. Noureldin
Keyword(s):  
Particle Filter ◽  
Inertial Sensors ◽  
Low Cost ◽  
Indoor Navigation ◽  
Feature Reduction ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Error Accumulation ◽  
Non Gaussian ◽  
Multipath Errors

Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be used as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On the other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning indoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and noisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced Inertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts. Second, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy power to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state estimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and the scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1 m accuracy 70% of the time outperforming each system individually.

scholarly journals Robust Special Strategies Re Sampling for Mobile Inertial Navigation Systems

2019 ◽  
Vol 9 (2) ◽  
pp. 3196-3204
Keyword(s):  
Particle Filtering ◽  
Inertial Sensor ◽  
Low Cost ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Computing Platform ◽  
Mobile Navigation ◽  
Global Positioning ◽  
Particle Measurement ◽  
Special Strategy

The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

scholarly journals Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

2016 ◽  
Author(s):  
Wei Shi ◽  
Yang Wang ◽  
Yuanxin Wu
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Inequality Constraint ◽  
Time Range ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
External Assistance ◽  
Constraint Method

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems&rsquo; position over the existing spherical constraint.

Sign in / Sign up

Export Citation Format

Share Document