Honeybee colonies exhibit incredibly efficient and adaptive behaviors as a group, even though an individual bee is tiny compared to the world it lives in. Honeybee colonies regularly find and exploit resources within 2-6 km of their hive, adapt the number of bees exploring and exploiting multiple resources (pollen, nectar, water) based on the environment and needs of the colony, and can even recover when dramatic changes are made to their world. While much remains to be understood, biologists believe that many of these sophisticated group behaviors arise from fairly simple interactions between honeybees in the hive, as they share information and adapt their own choices. There seems to be no leader, no centralized authority, to coordinate the hive.
Achieving the sophistication of social insect colonies poses a number of challenges. It will involve the development of sophisticated coordination algorithms, that match the fairly simple and limited sensing and communication we expect in individual robobees. Just as with honeybees, the ability to leverage the colony as a whole will be critical -- for parallelism (exploration of large areas), energy efficiency (through information sharing and division of labor), and robustness (since individuals may fail or make errors). Especially since each individual robobee has strong limitations on the weight and power (and thus sensing/communication) it can carry.
At the same time, to manage swarms of robots (with thousands or more individuals) one cannot be managing single robobees. We will need programming languages and run-time tools that support a "global-to-local" approach. A key challenge will be the design and scalable implementation of macro languages, where goals can be expressed in terms of high-level objectives for the colony and where the underlying system translates objectives into individual bee decisions and re-optimizes as the world changes.
The RoboBee colony challenges are shared with many other fields in computer science -- for example multi-robot and robot swarm systems, distributed sensor networks, programming languages research, and even synthetic biology. Our colony team leverages expertise and knowledge in multiple disciplines, and we expect our methodologies to apply to many large-scale systems.
Some of our current efforts include
(1) Karma Programming System and Stochastic control policies
(2) Simbeeotic Simulation Environment
(3) Heli-testbed Environment
(4) Models of Honeybee Information-sharing
To read more about our current work, take a look at our publications section.
You can also see videos of our work on our Robobees Colony Youtube Channel