Autonomous Intelligent Robotic Navigation System Architecture With Mobility Service for IoT

Autonomous Intelligent Robotic Navigation System Architecture With Mobility Service for IoT

Subbulakshmi T. (Vellore Institute of Technology, Chennai, India) and Balaji N. (Vellore Institute of Technology, Chennai, India)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/IJRAT.2017070103

Abstract

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.
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To reduce difficulty in handling automatic cars, Stanley (Ward-Bailey, 2016), and CA-RINA (Fernandes et al., 2014) have proposed autonomous vehicles (Bradley 2016). Easy navigation is provided to the vehicles in a more sophisticated manner. Autonomous vehicles have successfully completed the DARPA Grand Challenge (Wikipedia, 2016b) dessert challenge. But the problem in using the autonomous vehicles is that at times it is complicated for the users to give the longitude and latitude coordination for decision-making. The optimal way of easily access the autonomous vehicle is through mobile applications. The vehicle can be accessed through calls, messages and speech. The autonomous intelligent robotic machine responds to the user need by hand gesture signals, understanding them and communicating them to the autonomous robotic system (Wynn et al., 2014).

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