To enable effective group behavior without explicit communication or centralized control, we needed some means to:
- Minimize redundancy and interference
- Maintain a beneficial level of social interaction
- Adjust each robot's willingness to explore
- Automatically adapt individual robot behavior to different environments and varying numbers of robots
To accomplish these aims, we developed a form of online adaptation that provides the swarm with a means to automatically regulate itself. Positive and negative feedback is supplied to each robot by an internal critic, invoked at regular time intervals in order to continually adjust sensitivity to light and sound fluctuations. If the robot is too sensitive to these fluctuations, it appears “timid” and will fail to cover new ground. On the other hand, if the robot becomes unresponsive to such fluctuations, it will not effectively avoid collisions with obstacles or interact optimally with other robots. Perception of real world light intensity and sound fluctuations offers a perfect means to draw an appropriate level of randomness - a key component of swarm behavior - into the robots’ behavior. By adjusting the level of randomness, the online learning system can modulate certain emergent properties of the swarm such as swarm density, swarm translation, and swarm convergence. It also provides a means to adapt the swarm to new environments and promotes full coverage even in obstacle rich environments.