Robots, Drones, UAVs and UGVs for Operation and Maintenance

Diego Galar, Uday Kumar, Dammika Seneviratne

• Publisher: CRC Press
• Year: 2020
• Print ISBN: 9781138322110
• eBook ISBN: 9780429839177
• Topic: Tecnologia e ingegneria
• Subtopic: Design industriale

Industrial assets (such as railway lines, roads, pipelines) are usually huge, span long distances, and can be divided into clusters or segments that provide different levels of functionality subject to different loads, degradations and environmental conditions, and their efficient management is necessary. The aim of the book is to give comprehensive understanding about the use of autonomous vehicles (context of robotics) for the utilization of inspection and maintenance activities in industrial asset management in different accessibility and hazard levels. The usability of deploying inspection vehicles in an autonomous manner is explained with the emphasis on integrating the total process.

Key Features

• Aims for solutions for maintenance and inspection problems provided by robotics, drones, unmanned air vehicles and unmanned ground vehicles
• Discusses integration of autonomous vehicles for inspection and maintenance of industrial assets
• Covers the industrial approach to inspection needs and presents what is needed from the infrastructure end
• Presents the requirements for robot designers to design an autonomous inspection and maintenance system
• Includes practical case studies from industries

1

Introduction

1.1 Autonomous Vehicles

Autonomous vehicle (AV) technology will fundamentally change transportation. Technological advancements are creating a continuum between conventional, fully human-driven vehicles and AVs which partially or fully drive themselves and may ultimately require no driver at all. Within this continuum are technologies that enable a vehicle to assist and make decisions for a human driver. Such technologies include crash warning systems, adaptive cruise control (ACC), lane keeping systems, and self-parking technology.

Equipping cars and light vehicles with this technology will reduce crashes, energy consumption, and pollution—and reduce the costs of congestion.

This technology is most easily conceptualized using a five-level hierarchy suggested by the National Highway Traffic Safety Administration (NHTSA) with different benefits realized at different levels of automation:

• Level 0 (no automation): The driver is in complete and sole control of the primary vehicle functions (brake, steering, throttle, and motive power) at all times and is solely responsible for monitoring the roadway and for safe vehicle operation.
• Level 1 (function-specific automation): Automation at this level involves one or more specific control functions. If multiple functions are automated, they can operate independently of each other. In this case, the driver has overall control and is solely responsible for safe operation but can choose to cede limited authority over a primary control (as in ACC). Alternatively, the vehicle can automatically assume limited authority over a primary control (as in electronic stability control), or the automated system can provide added control to aid the driver in certain normal driving or crash-imminent situations (e.g., dynamic brake support in emergencies).
• Level 2 (combined function automation): This level involves automation of at least two primary control functions designed to work in unison to relieve the driver of controlling those functions. Vehicles at this level of automation can share authority when the driver cedes active primary control in certain limited driving situations. The driver is still responsible for monitoring the roadway and safe operation and is expected to be available for control at all times and on short notice. The system can relinquish control with no advance warning, and the driver must be ready to control the vehicle safely.
• Level 3 (limited self-driving automation): At this level of automation, the driver can cede full control of all safety-critical functions under certain traffic or environmental conditions and rely heavily on the vehicle to monitor changes in those conditions requiring transition back to driver control. The driver is expected to be available for occasional control but with sufficiently comfortable transition time.
• Level 4 (full self-driving automation): The vehicle is designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip. Such a design anticipates that the driver will provide destination or navigation input but is not expected to be available for control at any time during the trip. This includes both occupied and unoccupied vehicles.

The type and magnitude of the potential benefits of AV technology will depend on the level of automation achieved. For example, some of the safety benefits of AV technology may be achieved from function-specific automation (e.g., automatic braking), while the land use and environmental benefits are likely to be realized only by full automation (Level 4) (Anderson et al., 2016).

1.1.1 Brief History and Current State of Autonomous Vehicles

For decades, futurists have envisioned vehicles that drive themselves, and research into AV technology can be divided into three phases.

1.1.1.1 Phase 1: Foundational Research

From approximately 1980 to 2003, university research centers worked on two visions of vehicle automation. As one thrust, researchers pursued the development of automated highway systems (AHS), in which vehicles depended significantly on the highway infrastructure to guide them (Anderson et al., 2016).

One of the first major demonstrations of such a system took place in 1997 over a 7.6-mile stretch of California’s I-15 highway near San Diego. Led by the California Partners for Advanced Transit and Highways (PATH) program, the “DEMO 97” program demonstrated the platooning of eight AVs guided by magnets embedded in the highway and coordinated with vehicle-to-vehicle (V2V) communication (Ioannou, 1998).

A second research thrust was to develop both semiautonomous and AVs that depended little, if at all, on highway infrastructure. In the early 1980s, a team led by Ernst Dickmanns at Bundeswehr University Munich in Germany developed a vision-guided vehicle that navigated at speeds of 100 km/h without traffic (Bâela & Mâarton, 2011). Carnegie Mellon University’s NavLab developed a series of vehicles, named NavLab 1 through NavLab 11, from the mid-1980s to the early 2000s. In July 1995, NavLab 5 drove across the country in a “No Hands Across America” tour, in which the vehicle steered autonomously 98% of the time, while human operators controlled the throttle and brakes. Other similar efforts around the world sought to develop and advance initial AV and highway concepts (Anderson et al., 2016).

1.1.1.2 Phase 2: Grand Challenges

From 2003 to 2007, the US Defense Advanced Research Projects Agency (DARPA) held three “Grand Challenges” that markedly accelerated advancements in AV technology. The first two Grand Challenges charged research teams with developing fully autonomous vehicles for competition in a 150-mile off-road race for $1 million and $2 million prizes, respectively. No vehicle completed the 2004 Grand Challenge—the best competitor completed less than 8 miles of the course (BBC News, 2004). However, five teams completed the 2005 Grand Challenge course, held only 18 months later. The fastest team completed the course in just under 7 h, with the next three fastest finishing within the next 35 min (DARPA, undated).

In 2007, DARPA held its third and final AV challenge, dubbed the “Urban Challenge.” As the name suggests, vehicles raced through a 60-mile urban course, obeying traffic laws and navigating alongside other autonomous and human-driven vehicles. Six teams finished the course, and three completed the race within a time of 4.5 h, including time penalties for violating traffic and safety rules. This Grand Challenge spearheaded advancements in sensor systems and computing algorithms to detect and react to the behavior of other vehicles, to navigate marked roads, and to obey traffic rules and signals (Anderson et al., 2016).

1.1.1.3 Phase 3: Commercial Development

The DARPA Challenges solidified partnerships between auto manufacturers and the education sector and mobilized a number of endeavors in the automotive sector to advance AVs. These included the Autonomous Driving Collaborative Research Lab, a partnership between GM and Carnegie Mellon University (Carnegie Mellon University, undated) and a partnership between Volkswagen and Stanford University (Stanford University, undated).

Google’s Driverless Car initiative has brought autonomous cars from the university laboratory into commercial research. The program began shortly after the DARPA Urban Challenge and drew on the talents of engineers and researchers from several teams who participated in that competition. In the years since, Google has developed and tested a fleet of cars and initiated campaigns to demonstrate the applications of the technology through, for example, videos highlighting mobility offered to the blind (Google, 2012). Google is not alone. In 2013, Audi and Toyota both unveiled their AV visions and research programs at the International Consumer Electronics Show, an annua...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Authors
• 1. Introduction
• 2. Development of Autonomous Vehicles
• 3. Autonomous Inspection for Industrial Assets
• 4. Sensors for Autonomous Vehicles in Infrastructure Inspection Applications
• 5. Data Acquisition and Intelligent Diagnosis
• 6. Three-Dimensional Visualization
• 7. Communications
• 8. Autonomous Vehicles for Infrastructure Inspection Applications
• 9. Failure Detection Application in Autonomous Vehicles
• 10. Autonomous Inspection and Maintenance with Artificial Intelligence Infiltration
• 11. Big Data Analytics for AV Inspection and Maintenance
• Index