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Remote Characterization

Robot Swarm

Before we can deploy large numbers of real robots in hazardous environments there are hard problems still to be solved including positioning (i.e., giving each robot a knowledge of where it is), communication (both between robots, robot to operator, and between operators), and power. In order to move into a new, truly distributed paradigm, these problems cannot be addressed using sensors and platforms that emphasize the capabilities and intelligence of the individual. One intriguing solution is the creation of emergent insect-like behaviors to increase the autonomy of the robot.

Despite their limitations, insects do not suffer from a paucity of data; rather they are inundated by a kind of sensor data that is rich precisely because it is desultory and without scope. Insects capitalize on the fact that significant environmental events will most likely produce a light or sound fluctuation. A sudden change in light can speak volumes. However, although insects are very good at sensing environmental stimuli, they do not form sophisticated perceptions. Insects have sacrificed comprehension for more adept powers of apprehension and in so doing have become masters over a chaotic world of light and sound gradients for the most part unnoticed by the human senses.

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Early in our project, simulation of large-scale robot interaction offered key insight, but ultimately could not provide the fertile soil of chaotic, real-world physics necessary for swarm intelligence to reap its full rewards. In an attempt to move closer toward the insect paradigm, the INL uses touch sensors, light sensors, microphones, and IR sensors, all of which allow a tight coupling between sensing and action.

Within this embodied approach, the robots learn to respond appropriately to fluctuations in sound and light; in fact, obstacle avoidance and a variety of social behaviors including searching, spill convergence, and, perimeter formation are all dependent on the robot's ability to both recognize and instigate these fluctuations.

Despite the development challenges, we believe that to enable large numbers of small robots to be successfully deployed, we must have control architectures, robot platforms and sensors that can scale easily in terms of cost, size, computation, and bandwidth. Rather than rely on the crutches of global control, significant processing power, accurate position information, or reliable, explicit communication architectures, we promote the emergence of fully decentralized swarm intelligence whereby many simple agents generate patterns and self-organize through nearest-neighbor interactions.


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