Page 406 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

DISCUSSION 381

TABLE 7.20. Results of MANOVA in the Combined Data Set
MANOVA Effects Studied: 1—visibility, 2—day
Effect Wilks’ Lambda df 1 df 2 p-level
1 0.961175 2 19 0.686476
2 0.877370 2 19 0.288559
12 0.812598 2 19 0.139257

• Independent variables:
1. Visibility, with 2 levels: visible and invisible.
2. Day (repeated measures), with 2 levels: day 0 and day 2.

The results are shown in Table 7.20. Note that the p-level for the interaction
effect is larger than the significance level. We therefore conclude that there is
no interaction effect in these data, and hence any main effect is not modified
across the levels of another main effect. The p-levels for the day factor and
visibility factor are large enough, so the null hypotheses for these main effects
are accepted. This means that both the day (training) factor and the visibility
factor do not improve the overall human performance; that is, subjects’ path
lengths and completion times are not improved (decreased) by providing them
with a visible environment or with the opportunity to train and practice.

7.6 DISCUSSION

The study described in this chapter focuses on experimental testing of human
performance in tasks that deal with motion planning and require spatial reasoning.
The experimental stage was followed with a thorough statistical analysis of the
obtained test data. As said in the introduction to this chapter, the motivation
for the study was two-prong. First, we wanted to use these data to compare
human performance with the performance of robot sensor-based motion planning
algorithms described elsewhere in this book. Second, we wanted to foresee the
human performance in robot teleoperation systems, with an eye on techniques to
compensate for operator deficiencies via a synergistic human–robot operation.
To recap our prior discussion, assessing robot motion planning algorithms raises
these questions:

• The question of quality of sensor-based robot motion planning algorithms
can in principle be addressed in a number of ways. As those options are
listed below, we will note that the last option—a comparison with human
performance—stand out as more attractive:
— One can compare actual generated paths with optimal paths. This com-
parison would make, however, little sense simply because producing