Robotics in the new millennium: entering the age of exploitation

Robotics began in the 1960s as a field studying a new type of universal machine implemented with a computer-controlled mechanism. This period represented an age of overexpectation, which inevitably led to frustration and discontent with what could realistically be achieved given the technological capabilities at that time. In the 1980s, the field entered an era of realism as engineers grappled with these limitations and reconciled them with earlier expectations. Only in the past few years have we achieved a state in which we can feasibly implement many of those early expectations. As we do so, we enter the ‘age of exploitation.’¹

For more than 25 years, progress in concepts and applications of robots have been described, discussed, and debated. Most recently we saw the development of ‘intelligent’ robots, or robots designed and programmed to perform intricate, complex tasks that require the use of adaptive sensors. Before we describe some of these adaptations, we ought to admit that some confusion exists about what intelligent robots are and what they can do. This uncertainty traces back to those early overexpectations, when our ideas about robots were fostered by science fiction or by our reflections in the mirror. We owe much to their influence on the field of robotics. After all, it is no coincidence that the submarines or airplanes described by Jules Verne and Leonardo da Vinci now exist. Our ideas have origins, and the imaginations of fiction writers always ignite the minds of scientists young and old, continually inspiring invention. This, in turn, inspires exploitation. We use this term in a positive manner, referring to the act of maximizing the number of applications for, and usefulness of inventions.

Years of patient and realistic development have tempered our definition of intelligent robots. We now view them as mechanisms that may or may not look like us but can perform tasks as well as or better than humans, in that they sense and adapt to changing requirements in their environments or related to their tasks, or both. Robotics as a science has advanced from building robots that solve relatively simple problems, such as those presented by games, to machines that can solve sophisticated problems, like navigating dangerous or unexplored territory, or assisting surgeons.

Figure 1. Conceptual framework of components for intelligent robot design.

One such intelligent robot is the autonomous vehicle. This type of modern, sensor-guided, mobile robot is a remarkable combination of mechanisms, sensors, computer controls, and power sources, as represented by the conceptual framework in Figure 1. Each component, as well as the proper interfaces between them, is essential to building an intelligent robot that can successfully perform assigned tasks. An example of an autonomous-vehicle effort is the work of the University of Cincinnati Robot Team.² They exploit the lessons learned from several successive years of autonomous ground-vehicle research to design and build a variety of smart vehicles for unmanned operation. They have demonstrated their robots for the past few years (see Figure 2) at the Intelligent Ground Vehicle Contest³ and the Defense Advanced Research Project Agency's (DARPA) Urban Challenge.⁴

Figure 2. ‘Bearcat Cub’ intelligent vehicle designed for the Intelligent Ground Vehicle Contest.³

These and other intelligent robots developed in recent years can look deceptively ordinary and simple. Their appearances belie the incredible array of new technologies and methodologies that simply were not available more than a few years ago. For example, the vehicle shown in Figure 2 incorporates some of these emergent capabilities. Its operation is based on the theory of dynamic programming and optimal control defined by Bertsekas,⁵ and it uses a problem-solving approach called backwards induction. Dynamic programming permits sequential optimization. This optimization is applicable to mechanisms operating in nonlinear, stochastic environments, which exist naturally. It requires efficient approximation methods to overcome the high-dimensionality demands. Only since the invention of artificial neural networks and backpropagation has this powerful and universal approach become realizable. Another concept that was incorporated into the robot is an eclectic controller.⁶ The robot uses a real-time controller to orchestrate the information gathered from sensors in a dynamic environment to perform tasks as required. This eclectic controller is one of the latest attempts to simplify the operation of intelligent machines in general, and of intelligent robots in particular. The idea is to use a task-control center and dynamic programming approach with learning to optimize performance against multiple criteria.

Universities and other research laboratories have long been dedicated to building autonomous mobile robots and showcasing their results at conferences. Alternative forums for exhibiting advances in mobile robots are the various industry or government sponsored competitions.⁷ Robot contests showcase the achievements of current and future roboticists and often result in lasting friendships among the contestants. The contests range from those for students at the highest educational level, such as the DARPA Urban Challenge, to K-12 pupils, such as the First Lego League and Junior Lego League Robotics competitions.⁸ These contests encourage students to engage with science, technology, engineering, and mathematics, foster critical thinking, promote creative problem solving, and build professionalism and teamwork. They also offer an alternative to physical sports and reward scholastic achievement.

Why are these contests important, and why do we mention them here? Such competitions have a simple requirement, that the entry either works or does not work. This type of proof-of-concept pervades many creative fields. Whether inventors showcase their work at conferences or contests, most hope to eventually capitalize on and exploit their inventions, or at least appeal to those who are looking for new ideas, products, and applications.

As we enter the age of exploitation for robotics, we can expect to see many more proofs-of-concept following the advances that have been made in optics, sensors, mechanics, and computing. We will see new systems designed and existing systems redesigned. The challenges for tomorrow are to implement and exploit the new capabilities offered by emergent technologies—such as petacomputing and neural networks—to solve real problems in real time and in cost-effective ways. As scientists and engineers master the component technologies, many more solutions to practical problems will emerge. This is an exciting time for roboticists. We are approaching the ability to control a robot that is becoming as complicated in some ways as the human body. What could be accomplished by such machines? Will the design of intelligent robots be biologically inspired or will it continue to follow a completely different framework? Can we achieve the realization of a mathematical theory that gives us a functional model of the human brain, or can we develop the mathematics needed to model and predict behavior in large-scale, distributed systems? These are our personal challenges, but all efforts in robotics—from K-12 students to established research laboratories—show the spirit of research to achieve the ultimate in intelligent machines. For now, it is clear that roboticists have laid the foundation to develop practical, realizable, intelligent robots. We only need the confidence and capital to take them to the next level for the benefit of humanity.