of Kalman Filtering: 1.5 weeks.6 Remote Sensing: 1.5 weeks.
7 Target Tracking Including Multiple Targets with Multiple Sensors: 1.0 week.
8 Obstacle Mapping and Its Application to Robot Navigation: 1.0 week.
9 Operating a Robotic Manipulator: 1.0 week.
10 Remote Sensing via UAVs: 0.5 weeks.
11 Dynamics Modeling of AUVs: 1.0 week.
12 Control of AUVs: 1.5 week.
It is hoped that this book will also serve as a useful reference to those working in related areas. Because of the overriding objective described in the title of the book, the topics cut across traditional curricular boundaries to bring together material from several engineering disciplines. As a result, the book could be used for a course taught within electrical engineering, mechanical engineering, aerospace engineering, or possibly others. We would like to acknowledge here that MATLAB® is a registered trademark of The MathWorks, Inc. Also, please note, two of the videos referred to in Appendix A can be viewed at https://www.wiley.com/en-us/Mobile+Robots%3A+Navigation%2C+Control+and+Remote+Sensing%2C+2nd+Edition-p-9781119534785.
About the Authors
Gerald Cook, ScD, is the Earle C. Williams Professor Emeritus of Electrical Engineering and past chairman of Electrical and Computer Engineering at George Mason University. He was previously Chairman of Electrical and Biomedical Engineering at Vanderbilt University and before that, Professor of Electrical Engineering at the University of Virginia. He is a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a former president of the IEEE Industrial Electronics Society and a former Editor in Chief of the IEEE Transactions on Industrial Electronics.
Feitian Zhang, PhD, is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. He received the Bachelor's and Master's degrees in Automatic Control from Harbin Institute of Technology in China, and the PhD degree in Electrical and Computer Engineering from Michigan State University. He was a Postdoctoral Research Associate in the Department of Aerospace Engineering at the University of Maryland prior to joining Mason. His research interests include robotics, control, artificial intelligence, and underwater vehicles.
Introduction
I wish to take this opportunity to express my appreciation to Dr. Feitian Zhang for joining with me as Co‐Author in developing this second edition of Mobile Roots. He has demonstrated a high level of knowledge and skill in the area of autonomous underwater robots (AUVs) and adds a new dimension to the book with this contribution. It has been a pleasure working together on this project.
Mobile robots, as the name implies, have the ability to move around. They may travel on the ground, on the surface of bodies of water, under water, and in the air. This is in contrast with fixed‐base robotic manipulators that are more commonplace in manufacturing operations such as automobile assembly, aircraft assembly, electronic parts assembly, welding, spray painting, and others. Fixed‐base robotic manipulators are typically programmed to perform repetitive tasks with perhaps limited use of sensors, whereas mobile robots are typically less structured in their operation and likely to use more sensors.
As a mobile robot performs its tasks, it is important for its supervisor to maintain knowledge of its location and orientation. Only then can the sensed information be accurately reported and fully exploited. Thus navigation is essential. Navigation is also required in the process of directing the mobile robot to a specified destination. Along with navigation is the need for stable and efficient control strategies. The navigation and control operations must work together hand‐in‐hand. Once the mobile robot has reached its destination, the sensors can acquire the needed data and either store it for future transfer or report it immediately to the next level up. Thus, there is a whole system of functions required for effective use of mobile robots.
Mobile robots may be operated in a variety of different modes. One of these is the teleoperated mode in which a supervisor provides some of the instructions. Here sensors including cameras provide information from the robot to the supervisor that enables him or her to assess the situation and decide on the next course of action. The supervision may be very complete, leaving no decision making to the robot, or it may be at a high level only, leaving details to be worked out by algorithms residing on the robot. Some examples of this type of operation are the Mars rovers and the walking robots that descended down into the volcano on Mount Saint Helens in the state of Washington. Additional applications include the handling of hazardous materials such as nuclear waste or explosives and the search in war operations for explosives such as landmines. Other examples are unmanned air vehicles (UAVs) and AUVs that can be used for reconnaissance operations. The trajectory may be prespecified with the provision for intervention and redirection as the circumstances dictate.
One of the more interesting stories involving a teleoperated mobile robot took place in Prince William County, Virginia in the nineties. The police had a suspect cornered in an apartment house and decided that since he was armed they would send in their mobile robot. It was a tracked vehicle with a camera, an articulated manipulator, and a stun gun. Under the direction of a supervisor the robot was able to climb the stairs, open the apartment door, open a closet door, lift a pile of clothes off the suspect, and then stun him so that he could be apprehended. This served a very useful purpose and alleviated the need for the police officers to subject themselves to risk of injury or death.
Another possible mode is autonomous operation. Here the robot operates without external inputs except those inputs obtained through its sensors. Often there is a random element to the motion with sensors for collision avoidance and/or signal seeking. One example of this type of operation was the miniature solar‐powered lawn mowers at the CIA in Langley, Virginia. These mobile robots were the size of a dinner plate and had razor sharp blades. The courtyard in which they worked was quite smooth with well‐defined boundaries. Each robot could move in a random direction until hitting an obstacle at which time it switched to a new direction. Another example of this autonomous robotic behavior is a swimming‐pool cleaner. This device moves about the pool sucking up any debris on the bottom of the pool and causing it to be pumped into the filtration system. The motion of the mobile robot seems to be somewhat random with the walls of the pool providing a natural boundary. Similar devices exist for vacuuming homes or offices.
A very exciting and recent example of an underwater semi‐autonomous vehicle was the crossing of the Atlantic Ocean, from the coast of New Jersey to the coast of Spain, by the deep‐sea glider Scarlet. This 8‐ft long, 135 lb, unmanned vehicle was the product of a research team at Rutgers University and Teledyne Webb Research. The voyage took 221 days, extended over 4,600 miles, and provided data on the water temperature and salinity as a function of depth. The glider was powered by a battery that alternately pumped water out of the front portion of the vehicle to cause it to rise and took on water to cause it to dive. The battery could also be shifted forward or backward to modify the weight distribution and thereby adjust the glide angle. As the glider dove or climbed, its hydrodynamic wings gave it forward motion in much the same manner as that of a toy airplane glider dropped from a second floor window. It was equipped with a rudder for steering. Normally it traveled down to a depth of 600 ft below the surface of the ocean and then up to within 60 ft of the surface. A few times per day it would surface to get a GPS fix on its position, make radio contact with its supervisor and obtain a new way‐point to head toward. Apparently the vehicle was equipped with an inertial measurement
