### Table 1 summarizes the multi-robot Markov localization algorithm. The time index a29 and the state

"... In PAGE 7: ... Table1 : Multi-robot Markov localization algorithm for robot number a1 . it makes certain independence assumptions (e.... ..."

### Table 1 summarizes the multi-robot Markov localization algorithm. The time index a29 and the state variable a43

"... In PAGE 7: ... Table1 : Multi-robot Markov localization algorithm for robot number a1 . it makes certain independence assumptions (e.... ..."

### Table 4: Statistics on 9 multi-robot problems.

2001

"... In PAGE 8: ... For example, SBL assumes that collisions may occur between any two bodies of any two robots, while many pairs of bodies cannot col- lide. Table4 gives averages over 100 runs of SBL on 9 problems. Fig.... In PAGE 8: ... Fig. 5 shows the initial and final configura- tions for the problem named PIII-6 in Table4 . PIII-2 and PIII-4 are the same problem, but reduced to robots 1 and 2, and robots 1 through 4, respectively.... ..."

Cited by 51

### Table 2. Performance values stored in the GOT for the Searching Task tree in a multi-robot experiment. Alternative-behavior evalGOT (%) Number of behavior use

"... In PAGE 20: ... One or two stable strategies \won out quot; in most runs. Figure 14 shows a tree learned by the robot after 68 trials, and Table2 presents the performance stored in the corresponding GOT. Table 2.... ..."

### TABLE II FAULT SIGNATURES FOR MULTI-ROBOT BOX-PUSHING SYSTEM, WITH LEADING ZEROS REMOVED

2006

Cited by 4

### Table 1. 26 experiments of map combination. in Figure 2. The column indicating the Correctness of the Match is the ratio of correct matches to all reported matches. Finally, the last column gives a measure of the false alarms that were successfully eliminated by combining the maps built by di erent robots (or, by the same robot at di erent times). Since the probability of both robots experiencing the same false positive at the same location is lower than it is in the single robot case, multi-robot mapping is inherently well-suited to eradicating false positives (e.g., the feature detector can mistakenly signal the presence of a door due to noisy sensory input). The rst example of map combination is depicted on Figures 3 through 4. At a rst glance, the maps D9 and D7 may seem to have most landmarks of the building in common. Yet, a closer look reveals that in D9, the robot explored a cul-de-sac in the lower right corner of the map, which does not

2000

Cited by 31

### TABLE II PERCEPTUAL SCHEMAS, COMMUNICATION SCHEMAS, AND MOTOR SCHEMAS IN MULTI-ROBOT TRANSPORTATION TASK Schema Description Input Output

2005

Cited by 15

### TABLE IV PERCEPTUAL SCHEMAS, COMMUNICATION SCHEMAS, AND MOTOR SCHEMAS IN MULTI-ROBOT BOX PUSHING Schema Description Input Output

2005

Cited by 15

### Table 1 summarizes the multi-robot Markov localization algorithm. The time index D8 and the state variable C4 is omitted whenever possible. Of course, this algorithm is only an approximation, since

"... In PAGE 7: ... Table1 : Multi-robot Markov localization algorithm for robot number D2. it makes certain independence assumptions (e.... ..."

### Table 1 summarizes the multi-robot Markov localization algorithm. The time index t and the state variable L is omitted whenever possible. Of course, this algorithm is only an approximation, since

"... In PAGE 7: ... Table1 : Multi-robot Markov localization algorithm for robot number n. it makes certain independence assumptions (e.... ..."