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environment around us but with today s technology, don t we have more than enough sensor gadgetry to do the job The purpose of this book is to identify those dif culties, see why they are so hard, attempt solutions, and try to identify directions that will lead us to conquering the general problem. A few points that will be at the center of our work should be noted. First, we will spend much effort designing motion planning algorithms. This being the area that humans deal with all the time, it is tempting to try to use human strategies. Unfortunately, as often happens with attempts for intelligent automation, asking humans how they do it is not a gratifying experience. Similar to some other tasks that humans do well (say, medical diagnostics), we humans cannot explain well how we do it. Why did I decide to walk around a table this way and not some other way, and how did this decision t into my plan to get to the door I can hardly answer. This means that robot motion planning strategies will not likely come from learning and analysis of human strategies. The other side of it is, as we will see, that often humans are not as good in motion planning as one may think. Second, the above example with moving in the dark underlines the importance of sensing hardware. Strategies that humans and animals use to realize safe motion in an unstructured environment are intimately tied to the sensing machinery a species possesses. When coming from the outside into a dark room, your movement suddenly changes from brisk and con dent to slow and hesitant. Your eyes are of no use now: Touching and listening are suddenly at the center of the motor control chain. Your whole posture and gait change. If audio sources disappear, your gait and behavior may change again. This points to a strong connection between motion planning algorithms and sensing hardware. The same has to be true for robots. We will see that today s sensing technology is far from being adequate for the task in hand. In an unstructured environment, a trouble may come from any direction and affect any point of the robot body. Robot sensing thus has to be adequate to protect the robot s whole body. This calls for a special sensing hardware and specialized sensor data processing. One side effect of this circumstance is that algorithms and sensing hardware are to be addressed in the same book which is not how a typical textbook in robotics is structured. Hence we hope that a reader knowledgeable in the theory of algorithms will be tolerant of the material on electronics, and we also hope that a reader comfortable with electronics will be willing to delve into algorithms. Third, human and animals motion planning is tied to the individual s kinematics. When bending to avoid hitting a low door opening, one invokes multiple sequences of commands to dozens of muscles and joints, all realized in a complex sequence that unfolds in real time. Someone with a different kinematics due to an impaired leg will negotiate the same door as skillfully though perhaps very differently. Expect the same in robots: Sensor-based motion planning algorithms will differ depending on the robot kinematics. Aside from raising the level of robot functional sophistication, providing a robot with an ability to operate in an unstructured world amounts to a jump in its
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universality. This is not to say that a robot capable of moving dirty dishes from the table to a dishwasher will be as skillful in cutting dead limbs from trees. The higher universality applies only to the fact that the problem of handling uncertainty is quite generic in different applications. That is, different robots will likely use very similar mechanisms for collision avoidance. A robot that collects dishes from the table can use the same basic mechanism for collision avoidance as a robot that cuts dead limbs from trees. As said above, we are not there yet with commercial machines of this kind. The last 40 years of robotics witnessed a slow and rather painful progress much slower, for example, than the progress in computers. Things turned out to be much harder than many of us expected. Still, today s robots in automation-intensive industries are highly sophisticated. What is needed is supplying them with an ability to survive in an unstructured world. There are obvious examples showing what this can give. We would not doubt, for example, that, other issues aside, a robot can move a scalpel inside a patient s skull with more precision than a human surgeon, thus allowing a smaller hole in the skull compared to a conventional operation. But, an operating room is a highly unstructured environment. To be useful rather than to be a nuisance or a danger, the robot has to be environment-hardened. There is another interesting side to robot motion planning. Some intriguing examples suggest that it is not always true that robots are worse than people in space reasoning and motion planning. Observations show that human operators whose task is to plan and control complex motion for example, guide the Space Shuttle arm manipulator make mistakes that translate into costly repairs. Attempts to avoid such mistakes lead to a very slow, for some tasks unacceptably slow, operation. Dif culties grow when three-dimensional motion and wholebody collision avoidance are required. Operators are confused with simultaneous choices say, taking care of the arm s end effector motion while avoiding collision at the arm s elbow. Or, when moving a complex-shaped body in a crowded space, especially if facing simultaneous potential collisions at different points of the body, operators miss good options. It is known that losing a sense of direction is detrimental to humans; for example, during deep dives the so-called Diver s Anxiety Syndrome interferes with the ability of professional divers to distinguish up from down, leading to psychological stress and loss in performance. Furthermore, training helps little: As discussed in much detail in 7, humans are not particularly good in learning complex spatial reasoning tasks. These problems, which tend to be explained away as artifacts of poor teleoperation system design or insuf cient training or inadequate input information, can now be traced to the human s inherent relatively poor ability for spatial reasoning. We will learn in 7 that in some tasks that involve space reasoning, robots can think better than humans. Note the emphasis: We are not saying that robots can think faster or compute more accurately or memorize more data than humans we are saying that robots can think better under the same conditions. This suggests a good potential for a synergism: In tasks that require extensive spatial reasoning and where human and robot thinking/planning abilities are
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