The Self-Reconfiguring Robotic MoleculeA self-reconfiguring robot consists of a set of identical modules that can dynamically and autonomously reconfigure in a variety of shapes, to best fit the terrain, environment, and task. Self-reconfiguration leads to versatile robots that can support multiple modalities of locomotion and manipulation. For example, a self-reconfiguring robot can aggregate as a snake to traverse a tunnel and then reconfigure as a six-legged robot to traverse rough terrain, such as a lunar surface, and change shape again to climb stairs and enter a building.
We have designed a small robotic module we call the Molecule capable of self-reconfiguration in three-dimensional space. The Molecule is capable of independent movement on a substrate of identical Molecules, including straight-line traversal and 90 degree convex and concave transitions to adjacent surfaces.
A Molecule robot consists of two atoms linked by a rigid connection called a bond. Each atom has five inter-Molecule connection points and two degrees of freedom. One degree of freedom allows the atom to rotate 180 degrees relative to its bond connection, and the other degree of freedom allows the atom (thus the entire Molecule) to rotate relative 180 degrees relative to one of the inter-Molecule connectors at a right angle to the bond connection.
This movie shows how Molecules can reconfigure to change the shape of the robot. In this case, the task is locomotion -- utilizing individual module motions, the structure can move itself. With only four modules the movement is limited to the plane, but with additional modules the system can perform more sophisticated tasks such as stair climbing or building towers to access locations out of the plane.
The Molecule is controlled by two types of software: low-level assembly code in the onboard processor(s), and high-level code on a workstation.
Simulation is an important part of our Molecule research. It is difficult and time consuming to build hundreds of Molecule robots, however using simulation we can learn about systems with many more robots than we could build by hand.