Autonomous Intelligent Robotic Navigation System Architecture With Mobility Service for IoT

2017 ◽  
Vol 5 (2) ◽  
pp. 32-48
Author(s):  
Subbulakshmi T. ◽  
Balaji N.
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Mobile Apps ◽  
Human Life ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Ground Vehicles ◽  
Vehicle Architecture ◽  
Laser Sensors ◽  
Mobility Service

This article presents the platform for autonomous vehicle architecture, navigation optimization and mobility services. The basic approach is to develop an intelligent agent to create a safety journey and redefine the world of transportation. The goal is to eliminate human driving errors and save human life from accidents. AI robots are a concept of future transportation with full automation and self-learning. Velodyne laser sensors are used for obstacle detection and autonomous navigation of ground vehicles and to create 3D images of the surround so that navigation and controls are optimized. In this article, existing system accessibility will be optimized by multiple features. The agent accessibility is improved, and users can access the vehicles through different ways like mobile apps, speech recognition and gestures. This article concentrates on the mobility services of autonomous vehicles.

Autonomous Intelligent Robotic Navigation System Architecture With Mobility Service for IoT

2020 ◽  
pp. 382-399
Author(s):  
Subbulakshmi T. ◽  
Balaji N.
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Mobile Apps ◽  
Human Life ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Ground Vehicles ◽  
Vehicle Architecture ◽  
Laser Sensors ◽  
Mobility Service

This article presents the platform for autonomous vehicle architecture, navigation optimization and mobility services. The basic approach is to develop an intelligent agent to create a safety journey and redefine the world of transportation. The goal is to eliminate human driving errors and save human life from accidents. AI robots are a concept of future transportation with full automation and self-learning. Velodyne laser sensors are used for obstacle detection and autonomous navigation of ground vehicles and to create 3D images of the surround so that navigation and controls are optimized. In this article, existing system accessibility will be optimized by multiple features. The agent accessibility is improved, and users can access the vehicles through different ways like mobile apps, speech recognition and gestures. This article concentrates on the mobility services of autonomous vehicles.

scholarly journals Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 297
Author(s):  
Ali Marzoughi ◽  
Andrey V. Savkin
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Sufficient Conditions ◽  
Real Life ◽  
Unmanned Surface Vehicles ◽  
Necessary And Sufficient ◽  
See Region

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 67 ◽  
Author(s):  
Aakash Soni ◽  
Huosheng Hu
Keyword(s):  
Autonomous Vehicles ◽  
Formation Control ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Ground Vehicles ◽  
Implementation Challenges ◽  
Autonomous Ground Vehicles ◽  
Vehicle Platooning ◽  
And Control

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

Comparative Analysis Between Recurrent Convolutional and Convolutional Neural Networks for Horizon Detection

2020 ◽  
Vol 17 (9) ◽  
pp. 4364-4367
Author(s):  
Shreya Srinarasi ◽  
Seema Jahagirdar ◽  
Charan Renganathan ◽  
H. Mallika
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Semantic Segmentation ◽  
Unmanned Vehicles ◽  
Obstacle Detection ◽  
Ground Vehicles ◽  
Safe Navigation ◽  
Horizon Line ◽  
Flight Parameters

The preliminary step in the navigation of Unmanned Vehicles is to detect and identify the horizon line. One method to locate the horizon and obstacles in an image is through a supervised learning, semantic segmentation algorithm using Neural Networks. Unmanned Aerial Vehicles (UAVs) are rapidly gaining prominence in military, commercial and civilian applications. For the safe navigation of UAVs, there poses a requirement for an accurate and efficient obstacle detection and avoidance. The position of the horizon and obstacles can also be used for adjusting flight parameters and estimating altitude. It can also be used for the navigation of Unmanned Ground Vehicles (UGV), by neglecting the part of the image above the horizon to reduce the processing time. Locating the horizon and identifying the various obstacles in an image can help in minimizing collisions and high costs due to failure of UAVs and UGVs. To achieve a robust and accurate system to aid navigation of autonomous vehicles, the efficiency and accuracy of Convolutional Neural Networks (CNN) and Recurrent-CNNs (RCNN) are analysed. It is observed via experimentation that the RCNN model classifies test images with higher accuracy.

MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties

2019 ◽  
Author(s):  
Sai Rajeev Devaragudi ◽  
Bo Chen
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Constant Time ◽  
Longitudinal Motion ◽  
Driver Assistance Systems

Abstract This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.

scholarly journals Alteration in modal share due to autonomous vehicle-based mobility services

2021 ◽  
Author(s):  
Dávid Földes ◽  
Csaba Csiszár
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Random Variable ◽  
Shared Services ◽  
Modal Shift ◽  
Car Use ◽  
On Demand ◽  
Mobility Service ◽  
Small Capacity

Alteration in road-based mobility services in cities is expected due to introduction of autonomous vehicles (AVs). On-demand and shared services based on small capacity AVs emerge, which influence the modal share. The alteration has been estimated by simulation of scenarios; the travellers’ willingness-to-shift to an AV-based mobility service has been considered as a random variable in studies. In our developed modal share estimation method, the travellers’ current mobility habits and willingness-to-shift are considered. To determine the value of variables, a questionnaire survey was elaborated. The method was applied to calculate the modal shift in Budapest, Hungary. According to the results, willingness-to-shift is the highest among car users and the lowest among bikers. Based on the stated preferences, individual car use can be reduced by shared, on-demand, AV-based mobility services. Our method is applicable to determine the impacts of AVs.

scholarly journals Hybrid Multi-Sensor Integration for Static or Dynamic Obstacle Detection, Tracking and Classification for Autonomous Vehicle

2021 ◽  
Vol 23 (06) ◽  
pp. 1288-1293
Author(s):  
Dr. S. Rajkumar ◽  
◽  
Aklilu Teklemariam ◽  
Addisalem Mekonnen ◽  
◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Multiple Sensors ◽  
Environment Perception ◽  
Hybrid Sensors ◽  
The Individual

Autonomous Vehicles (AV) reduces human intervention by perceiving the vehicle’s location with respect to the environment. In this regard, utilization of multiple sensors corresponding to various features of environment perception yields not only detection but also enables tracking and classification of the object leading to high security and reliability. Therefore, we propose to deploy hybrid multi-sensors such as Radar, LiDAR, and camera sensors. However, the data acquired with these hybrid sensors overlaps with the wide viewing angles of the individual sensors, and hence convolutional neural network and Kalman Filter (KF) based data fusion framework was implemented with a goal to facilitate a robust object detection system to avoid collisions inroads. The complete system tested over 1000 road scenarios for real-time environment perception showed that our hardware and software configurations outperformed numerous other conventional systems. Hence, this system could potentially find its application in object detection, tracking, and classification in a real-time environment.

scholarly journals Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4357 ◽  
Author(s):  
Babak Shahian Jahromi ◽  
Theja Tulabandhula ◽  
Sabri Cetin
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Detection Accuracy ◽  
System Solution ◽  
Environment Perception ◽  
Fusion Framework

There are many sensor fusion frameworks proposed in the literature using different sensors and fusion methods combinations and configurations. More focus has been on improving the accuracy performance; however, the implementation feasibility of these frameworks in an autonomous vehicle is less explored. Some fusion architectures can perform very well in lab conditions using powerful computational resources; however, in real-world applications, they cannot be implemented in an embedded edge computer due to their high cost and computational need. We propose a new hybrid multi-sensor fusion pipeline configuration that performs environment perception for autonomous vehicles such as road segmentation, obstacle detection, and tracking. This fusion framework uses a proposed encoder-decoder based Fully Convolutional Neural Network (FCNx) and a traditional Extended Kalman Filter (EKF) nonlinear state estimator method. It also uses a configuration of camera, LiDAR, and radar sensors that are best suited for each fusion method. The goal of this hybrid framework is to provide a cost-effective, lightweight, modular, and robust (in case of a sensor failure) fusion system solution. It uses FCNx algorithm that improve road detection accuracy compared to benchmark models while maintaining real-time efficiency that can be used in an autonomous vehicle embedded computer. Tested on over 3K road scenes, our fusion algorithm shows better performance in various environment scenarios compared to baseline benchmark networks. Moreover, the algorithm is implemented in a vehicle and tested using actual sensor data collected from a vehicle, performing real-time environment perception.

scholarly journals A Review on Autonomous Driving Systems

2018 ◽  
Vol 5 (1) ◽  
pp. 1-16
Author(s):  
Salma Yaakub ◽  
Mohammed Hayyan Alsibai
Keyword(s):  
Autonomous Vehicles ◽  
Power Efficiency ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Planning System ◽  
Ground Vehicles ◽  
Traffic Crashes ◽  
Traffic System ◽  
Surrounding Environment ◽  
Analysis System

Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair

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