scholarly journals ROV Navigation in a Fish Cage with Laser-Camera Triangulation

2021 ◽  
Vol 9 (1) ◽  
pp. 79
Author(s):  
Magnus Bjerkeng ◽  
Trine Kirkhus ◽  
Walter Caharija ◽  
Jens T. Thielemann ◽  
Herman B. Amundsen ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Fish Farming ◽  
Doppler Velocity ◽  
Laser Triangulation ◽  
3D Data ◽  
Promising Solution ◽  
Active Fish

Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors.

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.

GPS-INS Integration Algorithm for Autonomous Vehicle Using Loosely Coupled Integration Architecture

2013 ◽  
Vol 390 ◽  
pp. 506-511
Author(s):  
Rashid Iqbal ◽  
Zhong Jian Li ◽  
Khan Badshah
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Initial Alignment ◽  
Integration Algorithm ◽  
Loosely Coupled ◽  
Integration Architecture

Inertial measurement unit (IMU) has been widely used for autonomous vehicles navigation. The accuracy of IMU specifies the performance of the inertial navigation system (INS).The errors in the INS are mainly due to the IMU inaccuracies, initial alignment, computational errors and approximations in the system equations. These errors are further integrated over time due to the dead-reckoning nature of the INS, which leads to unacceptable results. These errors need an accurate estimation for high precision navigation. INS is integrated with Global Positioning System (GPS) to estimate the errors and enhance the navigation capability of the INS. Linearized Kalman Filter (LKF) is proposed for estimating the errors in the low cost INS using Loosely Coupled integration approach, which is opted for its simplicity and robustness. Prediction part of the LKF is used during the GPS lag for errors estimation, which is found very effective for low cost sensors. The resulting GPS-INS integration algorithm is evaluated on simulated Autonomous vehicle trajectory, generated from 6-DOF model. The integrated system limits the attitude errors less than 0.1 deg and velocity errors of the order of 0.003 meter per second. Furthermore, it provides an optimal navigation solution than can be achieved from individual systems.

SwiVR-Car-Seat

2021 ◽  
Vol 5 (4) ◽  
pp. 1-26
Author(s):  
Mark Colley ◽  
Pascal Jansen ◽  
Enrico Rukzio ◽  
Jan Gugenheimer
Keyword(s):  
Interface Design ◽  
Autonomous Vehicles ◽  
User Study ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
User Input ◽  
The Road ◽  
Car Seat ◽  
Perceived Realism ◽  
Vehicle Motion

Autonomous vehicles provide new input modalities to improve interaction with in-vehicle information systems. However, due to the road and driving conditions, the user input can be perturbed, resulting in reduced interaction quality. One challenge is assessing the vehicle motion effects on the interaction without an expensive high-fidelity simulator or a real vehicle. This work presents SwiVR-Car-Seat, a low-cost swivel seat to simulate vehicle motion using rotation. In an exploratory user study (N=18), participants sat in a virtual autonomous vehicle and performed interaction tasks using the input modalities touch, gesture, gaze, or speech. Results show that the simulation increased the perceived realism of vehicle motion in virtual reality and the feeling of presence. Task performance was not influenced uniformly across modalities; gesture and gaze were negatively affected while there was little impact on touch and speech. The findings can advise automotive user interface design to mitigate the adverse effects of vehicle motion on the interaction.

scholarly journals Development and nonlinear mathematical modeling of a hybrid land autonomous vehicle that applies agrochemicals to ornamental plants

2020 ◽  
pp. 9-14
Author(s):  
Carlos HERNÁNDEZ ◽  
Nicandro FARÍAS ◽  
Noel GARCÍA ◽  
J. Reyes BENAVIDES
Keyword(s):  
Mathematical Modeling ◽  
Information And Communication Technologies ◽  
Precision Agriculture ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Ornamental Plants ◽  
Lateral Velocity ◽  
Agricultural Industry

The development of electronics, information and communication technologies have favored the dissemination of precision agriculture. While in other countries important scientific innovations are carried out and Hybrid Land Autonomous Vehicles (VATH) are used in various agricultural activities, in our country there is very little development in this regard and some of these production activities are still manual. This research presents the development and non-linear mathematical modeling of VATH for the automated application of agrochemicals in ornamental plants, for modeling the dynamics of longitudinal velocity, lateral velocity and angular velocity of turn or yaw that were simulated in SciLab are observed with the ISO 3888 standard. For the construction of this vehicle, we took an ATV and other low-cost electronic components that make it more affordable in the national agricultural industry will be considered as a motor base, compared to that proposed in other similar developments.

Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance

2021 ◽  
pp. 036119812098523
Author(s):  
Qing Tang ◽  
Yanqiu Cheng ◽  
Xianbiao Hu ◽  
Chenxi Chen ◽  
Yang Song ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Academic Research ◽  
Field Testing ◽  
Evaluation Methodology ◽  
The Public ◽  
Testing Procedures ◽  
Analysis Methodology ◽  
Highway Transportation ◽  
Promising Solution

Mobile and slow-moving operations, such as striping, sweeping, bridge flushing, and pothole patching, are critical for efficient and safe operation of a highway transportation system. However, reducing hazards for roadway workers and achieving a safer environment for both roadway maintenance operators and the public is a challenging problem. In 2017 alone, a total of 158,000 vehicle crashes occurred in work zones in the U.S.A., accounting for 61,000 injuries. The autonomous truck-mounted attenuator (ATMA) vehicle, sometimes referred to as an autonomous impact protection vehicle (AIPV), offers a promising solution to eliminate injuries to roadway maintenance workers and the public. This paper presents the evaluation methodology for the ATMA system, as well as the outcomes of field testing in Sedalia, Missouri. To the best of the authors’ knowledge, this is the first academic research to focus on ATMA. The ATMA system is first reviewed, followed by an introduction to the field testing procedures that includes descriptions of test cases and data collected, and their format. An analysis methodology is then proposed to quantitatively evaluate the system’s performance, and statistical models and hypothesis testing procedures are developed and presented. The numerical analysis results from real-world field testing under a controlled environment are presented, and the ATMA system’s performance is summarized. This paper can serve as a reference for transportation agencies that are interested in deploying similar technologies or for academic researchers to assess characteristics of autonomous vehicles and to apply knowledge gained in transportation modeling and simulation practices.

scholarly journals Multi-agent Approach to Resource Allocation in Autonomous Vehicle Fleets

2021 ◽  
Author(s):  
Alaa Daoud
Keyword(s):  
Autonomous Vehicles ◽  
Optimization Problems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Communication Technologies ◽  
Optimization Techniques ◽  
Distributed Constraint Optimization ◽  
V2v Communication ◽  
Solution Methods ◽  
Multi Agent

The development of autonomous vehicles, capable of peer-to-peer communication, as well as the interest in on-demand solutions, are the primary motivations for this study. In the absence of central control, we are interested in forming a fleet of autonomous vehicles capable of responding to city-scale travel demands. Typically, this problem is solved centrally; this implies that the vehicles have continuous access to a dispatching portal. However, such access to such a global switching infrastructure (for data collection and order delivery) is costly and represents a critical bottleneck. The idea is to use low-cost vehicle-to-vehicle (V2V) communication technologies to coordinate vehicles without a global communication infrastructure. We propose to model the different aspects of decision and optimization problems related to this more general problem. After modeling these problems, the question arises as to the choice of centralized and decentralized solution methods. Methodologically, we explore the directions and compare the performance of distributed constraint optimization techniques (DCOP), self-organized multi-agent techniques, market-based approaches, and centralized operations research solutions.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals An Anti-Counterfeiting Architecture for Traceability System Based on Modified Two-Level Quick Response Codes

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 320
Author(s):  
Shundao Xie ◽  
Hong-Zhou Tan
Keyword(s):  
Product Life Cycle ◽  
Low Cost ◽  
Prototype System ◽  
Qr Code ◽  
Quick Response ◽  
Copy Detection ◽  
Traceability System ◽  
Scanned Image ◽  
Promising Solution ◽  
Product Package

Traceability is considered a promising solution for product safety. However, the data in the traceability system is only a claim rather than a fact. Therefore, the quality and safety of the product cannot be guaranteed since we cannot ensure the authenticity of products (aka counterfeit detection) in the real world. In this paper, we focus on counterfeit detection for the traceability system. The risk of counterfeiting throughout a typical product life cycle in the supply chain is analyzed, and the corresponding requirements for the tags, packages, and traceability system are given to eliminate these risks. Based on the analysis, an anti-counterfeiting architecture for traceability system based on two-level quick response codes (2LQR codes) is proposed, where the problem of counterfeit detection for a product is transformed into the problem of copy detection for the 2LQR code tag. According to the characteristics of the traceability system, the generation progress of the 2LQR code is modified, and there is a corresponding improved algorithm to estimate the actual location of patterns in the scanned image of the modified 2LQR code tag to improve the performance of copy detection. A prototype system based on the proposed architecture is implemented, where the consumers can perform traceability information queries by scanning the 2LQR code on the product package with any QR code reader. They can also scan the 2LQR code with a home-scanner or office-scanner, and send the scanned image to the system to perform counterfeit detection. Compared with other anti-counterfeiting solutions, the proposed architecture has advantages of low cost, generality, and good performance. Therefore, it is a promising solution to replace the existing anti-counterfeiting system.

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