The Robobee Project

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 theory 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.

Areas of Research in the Colony Group

A main area of research has been the design of programming languages for controlling swarms of robotbees We have developed two abstract languages to start with. In the Karma language, one can specify a flowchart of tasks that the colony must achieve with links that represent conditions that trigger new tasks. The Karma system uses information that comes back from individuals to adjust the allocation of resources to tasks in a way that mimics the role of the hive in real honeybee colonies. A different second, called OptRAD (Optimizing Reaction-Advection-Diffusion), treats the colony of aerial robots as a fluid that diffuses through the environment. Any individual RoboBee uses a probabilistic algorithm to determine whether it will execute a task based on the current state of the environment. OptRAD uses the fluid model to efficiently reason at a macro-level about the expected outcomes and adjust its behavior to adapt to new circumstances.

We are also developing autonomous robots and swarm environments to study the design and operation of large-scale swarms. One environment is the Simbeeotic simulator, that uses a physics simulation to model scenarios and interfaces with a vicon-based helicopter laboratory, to do mixed simulation-hardware experiments. This project has also contributed to the Kilobot system: a collective of a thousand robots, each about the width of a quarter, that move by vibrating and that communicate with other nearby Kilobots. We intend to use this collective to test the efficacy of our programming languages and our mathematical models of emergent behavior. Most recently, we have been working on insect-inspired vision-based control for lightweight aerial vehicles. using optic flow concepts to enable autonomous speed control, centring, heading stabilisation, collision-avoidance, and limited forms of odometery.

We are also applying research from this project to other disciplines. Our work in OptRAD on developing fast swarm simulations has been applied to designing computational simulation of nanoparticle designs for cancer therapy. We have also tested different models of information propagation in honeybees and how that affects the efficiency of foraging in diffeernt environments.


Autonomous MAV guidance with a lightweight omnidirectional vision sensor
Richard Moore, Karthik Dantu, Geoffery Barrows, Radhika Nagpal
IEEE Intl. Conf on Robotics and Automation (ICRA), June 2014. (pdf)

Flight of the Robobees
Robert Wood, Radhika Nagpal, Gu-Yeon Wei
Scientific American, March 2013. (link)

A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors
Sabine Hauert, Spring Berman, Radhika Nagpal, Sangeeta N. Bhatia
Nano Today, Dec 2013. (link)

Collective Transport of Complex Objects by Simple Robots: Theory and Experiments
Mike Rubenstein, Adrian Cabrera, Justin Werfel, Golnaz Habibi, James McLurkin, Radhika Nagpal
Intl. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), May 2013. (pdf)

A Comparison of Deterministic and Stochastic Approaches for Allocating Spatially Dependent Tasks in Micro-Aerial Vehicle Collectives
Karthik Dantu, Spring Berman, Bryan Kate, Radhika Nagpal
IEEE Intl. Conference on Robots and Systems (IROS), Oct 2012. (pdf)

Kilobot: A Low Cost Scalable Robot System for Collective Behaviors
Michael Rubenstein, Christian Ahler, Radhika Nagpal
IEEE Intl. Conf on Robotics and Automation (ICRA), 2012. (pdf)

Simbeeotic: A Simulator and Testbed for Micro-Aerial Vehicle Swarm Experiments
Bryan Kate, Jason Waterman, Karthik Dantu, Matt Welsh
Intl. Conf. on Information Processing in Sensor Networks (IPSN, SPOTS Track), Apr 2012. (pdf)

Optimization of Stochastic Strategies for Spatially Inhomogeneous Robot Swarms: A Case Study in Commercial Pollination
Spring Berman, Radhika Nagpal, Adam Halasz
Intl. Conference on Robots and Systems (IROS), Sept 2011. (pdf)

Design of Control Policies for Spatially Inhomogeneous Robot Swarms with Application to Commercial Pollination
Spring Berman, Vijay Kumar, and Radhika Nagpal
IEEE International Conference on Robotics and Automation, ICRA, 2011 (pdf)

Programming Micro-Aerial Swarms with Karma
Karthik Dantu, Bryan Kate, Jason Waterman, Peter Bailis, Matt Welsh
Intl. Conf. on Embedded Networked Sensor Systems (Sensys), Nov 2011. (pdf)

Positional Communication and Private Information in Honeybee Foraging Models
Peter Bailis, Radhika Nagpal, Justin Werfel
Intl Conference on Swarms Intelligence, ANTs 2010 (pdf)
Best Student Paper Award

Exhibit: Museum of Science, Boston

May 2014: The museum of science exhibit featured all three aspects of the robobee project, including a section on the colony behavior with a "colony simulation game" and a profile of Mike Rubenstein who helped design the game.

2009 NSF Expeditions Project
The Robobee Project (Main Page) is an NSF Expeditions Project, which has many parallel efforts divided into three areas - brain, body, and colony - and involves many faculty members at Harvard. Read more about this project on the main webpage and in our Scientific American article below.

Flight of the Robobees
Robert Wood, Radhika Nagpal, Gu-Yeon Wei
Scientific American, March 2013. (link)