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Table 3: Summary of results on self-assembly obtained for the experiments with one s-bot (number 13) and a prey, and those with six s-bots and a prey. Notation: N (group size), D (initial distance in cm), C (percentage of connections), T (median group completion time in s; only trials with N connections). Each configuration was tested at least 20 times (see text for details). Values marked with the *-symbol were obtained with the modified controller. N D flat terrain mod. rough terrain very rough terrain
2006
"... In PAGE 22: ... In total, 120 times an s-bot was controlled in order to establish a connection, and in 109 cases it succeeded. Table3 summarizes the results obtained for the experiments with one s-bot (number 13) and a prey, and those with six s-bots and a prey, for the three different types of terrain. Overall, the reliability of the algorithm which was designed to control s-bots on flat terrain is not affected by the roughness of the moderately rough terrain.... ..."
TABLE I PERFORMANCE OF THE BEST EVOLVED CONTROLLER TESTED IN SIMULATION AND REALITY. TESTS INVOLVE FOUR S-BOTS FORMING A LINEAR STRUCTURE. THE FIRST TWO COLUMNS INDICATE THE PERFORMANCE ON FLAT TERRAIN RESPECTIVELY IN THE CASE OF SIMULATED AND REAL S-BOTS. THE LAST TWO COLUMNS INDICATE THE PERFORMANCE OF REAL S-BOTS ON BROWN AND WHITE ROUGH TERRAIN (SEE TEXT). THE SIX ROWS INDICATE IN ORDER: THE AVERAGE PERFORMANCE OVER 20 TRIALS, THE STANDARD DEVIATION, THE STANDARD ERROR, THE RATIO OF PERFORMANCE WITH RESPECT TO THE THEORETICAL MAXIMUM, THE RATIO OF PERFORMANCE WITH RESPECT TO THE CORRESPONDING SIMULATED TEST, AND THE NUMBER OF TRIALS (OUT OF 20) IN WHICH THE SWARM-BOTS DID NOT MANAGE TO PERFECTLY COORDINATE.
Table 4. Simulator performance for different abstraction models. The numbers represent real time multipliers, i.e., the ratio between simulated real time and simulation time (the higher, the better).
2004
"... In PAGE 26: ...06GHz Xeon PC with one nVidia QuadroFx 1000 graphics card. The readings obtained for a smooth plane terrain are reported in Table4 , where, for comparison purposes, data is shown also for 5 connected s-bots on a plane and for 5 disconnected s-bots on a rough terrain. All measurements were taken using a time-step of 10 ms, except for the fast model, that, because of the large step used with it, showed instability when used on rough terrain.... ..."
Cited by 27
Table 4. Simulator performance for different abstraction models. The numbers represent real time multipliers, i.e., the ratio between simulated real time and simulation time (the higher, the better).
2004
"... In PAGE 26: ...06GHz Xeon PC with one nVidia QuadroFx 1000 graphics card. The readings obtained for a smooth plane terrain are reported in Table4 , where, for comparison purposes, data is shown also for 5 connected s-bots on a plane and for 5 disconnected s-bots on a rough terrain. All measurements were taken using a time-step of 10 ms, except for the fast model, that, because of the large step used with it, showed instability when used on rough terrain.... ..."
Cited by 27
Table 4. Simulator performance for different abstraction models. The numbers represent real time multipliers, i.e., the ratio between simulated real time and simulation time (the higher, the better).
"... In PAGE 19: ...06 GHz Xeon PC with one nVidia QuadroFx 1000 graphics card. The readings obtained for a smooth plane terrain are reported in Table4 , where, for comparison purposes, data is shown also for 5 connected s-bots on a plane and for 5 disconnected s-bots on a rough terrain. All measurements were taken using a time-step of 10 ms, except for the fast model, that, because of the large step used with it, showed instability when used on rough terrain.... ..."
Table 1. Terrain complexity
2002
"... In PAGE 4: ...ay tracer of Parker et. al. [13] to render two different terrain models, which are called Hogum and Range-400. Images of these data sets are given in Figure 2 and their geo- metric complexity is given in Table1 . The intersection routine is similar to that used for isosurface rendering [14].... ..."
Cited by 7
Table 2: Trials and intervention data Arid Terrain Wooded Terrain
2004
"... In PAGE 4: ... We have not included the data from the 7000 meter runs in our analysis here. Table2 shows the number of trials conducted in each section, the number of trials in which interventions occurred, and the number of trials that had multiple interventions. Table 2: Trials and intervention data Arid Terrain Wooded Terrain ... ..."
Cited by 1
Table 2 Terrain attribute descriptions
2000
"... In PAGE 13: ...1. Terrain attribute calculations Terrain attributes ( Table2 ) were calculated on the 50 simulated DEMs in ARC/INFO, and statistics calculated for the distribution of values in each pixel.... ..."
Table 2. The product of terrain and geology classi cations Terrain classes
2004
"... In PAGE 4: ...Alexandre Sorokine and Thomas Bittner Table 1. Hierarchical Arrangement of Subsections [3, Table2 , p. 16] I.... In PAGE 8: ...ig. 4(a) and Fig. 4(b) respectively. The product of these classi cations is depicted in Table2 , with terrain classes as columns and geologic classes as rows. Each cell of the table contains the number of individuals that instantiate corresponding classes of both hierarchies.... In PAGE 8: ... The class \Volcanics quot; that violates the weak supplementation principle was moved from the terrains hierarchy into the geologic classes hier- archy (Fig. 4 and Table2 ). This is a more natural place for this class because there is already a class with an identical name.... In PAGE 12: ... Most likely N will be greater than the number of classes that can be instantiated by the in- stances. In our example most of the cells of the Table2 are empty indicating that there are no individuals that instantiate classes in either classi cation tree. The reason for it is that certain higher-level classes do not demonstrate as much diversity on the studied territory as other classes do.... ..."
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