Overview of Research
The overarching aspiration of this project is to combine multiple diverse research topics under the goal of robotic pollinators. By bringing together experts from biology, computer science, and mechanical and electrical engineering, our end product will be much more than the sum of the parts.
While a project of this scale relies on numerous interactions and requires tight collaboration between the investigators, the proposed research neatly falls into three aforementioned categories: body, brain, and colony.
The body involves all aspects of the proposed work that revolve around construction of a flapping-wing robot. We will explore several aspects of free flight mechanics and performance to guide our design of an autonomous robotic bee.
The brain incorporates all of the sensors, control (i.e. algorithms and software), and circuitry (i.e., hardware) to coordinate flight and target identification capabilities of the RoboBees.
The colony encompasses higher-level support required to accomplish objectives of a complex task in a collaborative manner. We seek to leverage the colony as a whole for parallel, energy-efficient, and robust operation.
This project emphasizes collaboration between researchers across a wide range of scientific and engineering disciplines to realize its ambitious goals. It also offers an exciting platform from which to share scientific and technological breakthroughs with the public in a tangible, easily-accessible manner.
By leveraging existing breakthroughs from Professor Wood’s Microrobotics Lab, which conducted the first successful flight of a life-sized robotic fly in 2007, the team will explore ways to emulate such aerobatic feats in their proposed devices. In addition, achieving autonomous flight will require compact high-energy power sources and associated electronics, integrated seamlessly into the ‘body’ of the machine.
The robotic platform for the colony of artificial bees will be designed using principles derived from insect biomechanics and the fluid dynamics of flapping wings. Proper design of all mechanical and aeromechanical components of the robotic bee are crucial, since propulsive efficiency will determine flight time, and payload limitations will determine the size and mass available for sensing, communication, and other on-board electronics.
Similarly, actuator power requirements necessitate the development of efficient drive electronics, and require portable power sources with high energy-to-weight ratios. Therefore, a rigorous study of the coupled mechanics and aerodynamics of an insect-scale vehicle is essential to the success of this project.
Realization of the body will require extensive research in (1) aerodynamics and control of flapping-wing flight, (2) design and fabrication of the flight apparatus, and (3) portable power sources and drive electronics.
One of the most complicated areas of exploration the scientists will undertake will be the creation of a suite of artificial “smart” sensors, akin to a bee’s eyes and antennae. Professor Wei explains that the ultimate aim is to design dynamic hardware and software that serves as the device’s ‘brain,’ controlling and monitoring flight, sensing objects such as fellow devices and other objects, and coordinating simple decision-making.
The second group of research topics involves the development of the ‘brain’ to coordinate all activities of the body and carry out higher-order mission objectives of the colony, described later.
This will include sensors for proprioception and exteroception, an electronic nervous system (ENS), and control algorithms. Just as the focuses of the body were on lightweight and energetically efficient propulsion components, the focuses of the brain will include computationally-efficient control, compact and efficient sensors, and energy-efficient electronic hardware.
The figure illustrates a functional description of the insect nervous system (for Diptera) as well as a hybrid architecture block diagram of the physical manifestation of functions that correspond to the inner workings of the nervous system.
While we are not proposing to build a one-to-one replica, we again use insect biology to guide our design of an artificial brain.
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