HUMAN PERFORMANCE IN MOTION PLANNING in Visual Studio .NET

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performed better when operating in the dark. One subject did quite well in the dark, then was not even able to nish the task when operating with a completely visible scene, and refused to accept that in both cases he had dealt with the same scene: This one [with complete information] is much harder; I think it has no solution. It seems that extra information doesn t help. What s going on
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Human Versus Computer Performance with the Arm. As we did above with the labyrinth, we can attempt a comparison between the human and computer performance when moving the arm manipulator, under the same conditions. Since in previous examples human performance was similar in tests with complete and incomplete information, it is not important which to consider: For example, the performance shown in Figure 7.6 is representative enough for our informal comparison. On the algorithm side, however, the input information factor makes a tremendous difference as it should. The comparison becomes interesting when the computer algorithm operates with incomplete ( sensing ) information. Shown in Figure 7.8 is the path generated in the same work space of Figure 7.5 by the motion planning algorithm developed in Section 5.2.2. The algorithm operates under the model with incomplete information. To repeat, its sole input information comes from the arm sensing; known at all times are only the arm
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Figure 7.8 Path produced in the work space of Figure 7.5 by the motion planning algorithm from Section 5.2.2; M1 is the shortest (in C-space) path that would be produced if there were no obstacles in the workspace.
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PRELIMINARY OBSERVATIONS
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positions S and T and its current position. The arm s sensing is assumed to allow the arm to sense surrounding objects at every point of its body, within some modest distance rv from that point. In Figure 7.8, radius rv is equal to about half of the link l1 thickness; such sensing is readily achievable today in practice (see 8). Similar to Figure 7.6, the resulting path in Figure 7.8 (dotted line) is the path traversed by the arm endpoint when moving from position S to position T . Recall that the algorithm takes as its base path (called M-line) one of the four possible shortest straight lines in the arm s C-space (see lines M1 , M2 , M3 , M4 in Figure 5.5); distances and path lengths are measured in C-space in radians. In the example in Figure 7.8, the shortest of these four is chosen (it is shown as line M1, a dashed line). In other words, if no obstacles were present, under the algorithm the arm endpoint would have moved along the curve M1; given the obstacles, it went along the dotted line path. The elegant algorithm-generated path in Figure 7.8 is not only shorter than those generated by human subjects (Figure 7.6). Notice the dramatic difference between the corresponding (human versus computer) arm test and the labyrinth test. While a path produced in the labyrinth by the computer algorithm (Figure 7.4) presents no conceptual dif culty for an average human subject, they nd the path in Figure 7.8 incomprehensible. What is the logic behind those sweeping curves Is this a good way to move the arm from S to T The best way Consequently, while human subjects can easily master the algorithm in the labyrinth case, they nd it hard in fact, seemingly impossible to make use of the algorithm for the arm manipulator. 7.2.3 Conclusions and Plan for Experiment Design We will now summarize the observations made in the previous section, and will pose a few questions that will help us design a more comprehensive study of human cognitive skills in space reasoning and motion planning: 1. The labyrinth test is a good easy-case benchmark for testing one s general space reasoning abilities, and it should be included in the battery of tests. There are a few reasons for this: (a) If a person nds it dif cult to move in the labyrinth which happens rarely he or she will be unlikely to handle the arm manipulator test. (b) The labyrinth test prepares a subject for the test with an easier task, making the switch to the arm test more gradual. (c) A subject s successful operation in the labyrinth test suggests that whatever dif culty the subject may have with the arm test, it likely relates to the subject s cognitive dif culties rather than to the test design or test protocol. 2. When moving the arm, subjects exhibit different tastes for control means: Some subjects, for example, prefer to change both joint angles simultaneously, pulling the arm endpoint in the direction they desire, whereas other subjects prefer to move one joint at the time, thus producing circular
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