Before we could fully understand the effects of social potential fields, we needed some way to empirically measure the performance of our swarm as it accomplished search and perimeter formation tasks. Our goal was to show that the social interactions wrought through our implementation of social potential fields could produce desirable emergent effects that helped the robots accomplish their task. We needed some means to quantify the performance benefits achieved though emergent social effects and chose to do so in terms of a coverage efficiency experiment. The experiment focuses on the autonomous searching behavior of the privates and does not examine command and control issues related to infusing the system with human knowledge and guidance.
In order to acquire empirical, objective data on the behavior of the robots, we constructed a 64 square foot environment consisting of an eight by eight foot walled enclosure with a floor covering consisting of large sheets of white-board. Each robot was instrumented with a Velcro sponge pad, which allowed us to securely attach a dry erase marker to the rear of the robot. Each robot was fitted with a different color marker to differentiate its path from the others. The marker provided an effective means to capture “ground-truth” on the movements of each robot and the cumulative effect on the resulting area coverage.
To complete the coverage task, the robot(s) were required to fully explore the floor of the test bed described above. We considered several means of ascertaining coverage and decided that full-coverage would be defined as “no unmarked space remaining into which a robot could fit lengthwise.” Throughout the experiment, four rectangular, cardboard obstacles of varying sizes remained fixed in position within the testbed. We ran five trials with one, two, three, four, six and nine robots. For each trial the robot(s) were placed in the same corner and were all started within a few seconds of each other. For each trial, we recorded the total time required to achieve full coverage and then wiped the test-bed clean.
The average time required to achieve full coverage decreases drastically as the number of robots increases.
If we define performance as the reciprocal of time required, we can present the data in terms of overall performance / the number of robots.
Our data shows that the performance per robot increases as we add more robots, indicating that there is a “synergistic” effect emerging. This indicates that we are indeed benefiting from the social effects of multiple robot interaction and that these effects grow as we add more robots. However, our results also indicate that the benefit to adding additional robots extends only to a point after which the synergistic effects begin to be offset by the detrimental effects of increasing interference. The performance per robot augments through six robots but then begins to diminish. With nine robots the performance per robot has drastically decreased.
While a 64 sq. ft. environment may seem small in comparison to many operational environments, complete coverage proved to be a stiff requirement. The time required for a single robot to achieve full coverage varied drastically from trial to trial. Indeed, the distribution range between trials may be, in and of itself, a significant result. Besides reducing the overall time required to search an area, the use of multiple robots renders overall task performance more reliable.
These results suggest that use of multiple robots can be a great advantage for search and detection tasks. However, before we can draw definitive conclusions regarding these speculations, it is necessary to reproduce the experiments with larger numbers of robots in different environments. Reproducing this experiment will allow us to ascertain which results generalize across environments and which are a function of the specific study reported here.