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Table 2: Multiple linear regressions for SH robot at 2:1 force scaling
"... In PAGE 7: ... Therefore, SH robotic assistance in force-feedback mode reduces cumulative force at the stapes footplate likely by decreasing the duration of fenestration. SH Robot assistance in force-scaling mode: Tremor reduction and amplified tactile feedback Table2 presents the results of a multiple linear regression comparing FH performance to SH robotic assistance in force-scaling mode. In this mode, SH robotic assistance provides both tremor reduction and amplified ... In PAGE 7: ... In this mode, SH robotic assistance provides both tremor reduction and amplified tactile feedback. Table2... ..."
Table 2. Multiple linear regressions for SH robot at 2:1 force scaling
"... In PAGE 6: ... There- fore, SH robotic assistance in force-feedback mode reduces cumulative force at the stapes foot- plate likely by decreasing the duration of fenes- tration. SH Robot Assistance in Force-Scaling Mode: Tremor Reduction and Amplified Tactile Feedback Table2 presents the results of multiple linear regressions comparing FH performance to SH ro- botic assistance in force-scaling mode. In this mode, SH robotic assistance provides both tremor reduction and amplified tactile feedback.... ..."
Table 1. Multiple linear regressions for SH robot at 1:1 force-feedback
"... In PAGE 6: ...Feedback Mode: Tremor Reduction Table1 presents the results of multiple linear regressions comparing FH performance to SH ro- bot assistance in force-feedback mode. In this mode, the primary effect of SH robotic assistance is to reduce tremor.... In PAGE 6: ... In this mode, the primary effect of SH robotic assistance is to reduce tremor. Table1 shows that the SH robot in force-feedback mode significantly re- duced the cumulative force applied to the stapes footplate by an average of 31 Ns (P H11005 0.05).... In PAGE 6: ... Neither surgeon experience nor SH robotic assistance in force-feedback mode affected maximum force or fenestration targeting (dis- placement). Both cumulative force and duration of fenestra- tion are significantly reduced by SH robot assis- tance in force-feedback mode ( Table1 ). There- fore, SH robotic assistance in force-feedback mode reduces cumulative force at the stapes foot- plate likely by decreasing the duration of fenes- tration.... ..."
Table 1: Table of Model Parameters closely match the published gures for the products modeled. For some experiments, however, the value of one parameter was altered. The number of robot arms for most libraries is usually 1 or 2 whereas we have set the number of arms to 4 for some of the experiments with multiple drives. The reason for this is that if the number of robots is less than the number of drives, then contention between the drives for the robot arms a ects the performance. To evaluate the performance of the heuristics for multiple drives relative to the optimal solution independent of contention for the robots we have set the number of robots to 4. The experiments were repeated with a single robot to determine the performance of these heuristics under contention. Parameter Meaning
"... In PAGE 17: ... The performance of the drives and robot arms and the characteristics of the media are also con gurable. Table1 lists some of the parameters that can be adjusted for the model. The model mimics an actual robotic library.... ..."
Table 1. Overview of robot characteristics
"... In PAGE 30: ... Only four of the smallest robots were used on the rubble pile, but all of the following equipment was cached during the rescue. An overview of the robot characteristics is shown in Table1 . The quot;sensing quot; characteristic is not included in the table due to the availability of multiple sensor con gurations on each of these platforms.... In PAGE 66: ... In Table 9, the last three columns show the total time the robot spent in that failure state. This time is converted into a percentage of the overall time in Table1 0, allowing for an objective comparison with the other drops in the same column. The nature of this disaster made the video data collection di cult and in- complete in many cases, so several considerations must be taken into account when observing the data set.... ..."
Table 2: Approaches to Controlling Multiple Enti- ties
"... In PAGE 4: ... The self-organizing ability of swarms enables them to respond autonomously to such changes. 3 How can we Generate Swarming? There are three major approaches to the command and control of multiple robotic entities, which can be distinguished on the basis of the location in the architecture at which intelligent decisions are concentrated ( Table2 ). Centralized command and control, which is not swarming under our definition, treats the centralized commander as the main locus of intelligence.... ..."
Table 6: Multiple tape drives case: response time vs request arrival rate. robot arm contention is not a major factor in determining the average response time.
1997
"... In PAGE 38: ...90 requests/hour. The results are shown in Table6 . A plot of the relative response time vs arrival rate is shown in Figure 9.... ..."
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Table 1. Results of the experiments. Transitions mean the changing between modules for controlling the robot. If there are 0 transitions, only one module controlled the robot through the entire last epoch. If there is one transition, two modules controlled the robot, and so on. However, multiple transitions does not ensure different modules were in control for each transition, therefore the number of unique modules that controlled the robot are also listed. ufeedback/ut says how strong the feedback error motor signal was relative to the total motor command at the last epoch. The performance error pe is the accumulated difference between the desired state and the actual state of the system at the last epoch.
Table 9: Multiple Hypothesis state estimation propagation algorithm
2006
"... In PAGE 25: ... The specific notation for these algorithms is described in Table 8. The propagation algorithm for our disjoint Multiple Hypothesis tracker is shown Table9 , and the sensor update algorithm is shown in Table 10. Directly evaluating this algorithm on real robots is much more difficult due to the challenge of obtaining the ground truth of the ball and the robot in the environment.... ..."
Table 9: Multiple Hypothesis state estimation propagation algorithm
2006
"... In PAGE 26: ... The specific notation for these algorithms is described in Table 8. The propagation algorithm for our disjoint Multiple Hypothesis tracker is shown Table9 , and the sensor update algorithm is shown in Table 10. Directly evaluating this algorithm on real robots is much more difficult due to the challenge of obtaining the ground truth of the ball and the robot in the environment.... ..."
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