In mechatronic systems, mechanical, electrical, and embedded control components are tightly integrated both functionally and spatially. Examples of such systems range from small consumer products (VCRs, Camcorders, Microwave ovens) to large assembly lines, airplanes, and cars. To exploit the tight integration of multiple energy domains and embedded controllers, a new design paradigm is needed.
The simulation-based design approach allows designers to evaluate different design alternatives quickly in a virtual environment. We have developed software with which designers can compose system-level simulation models by combining models of system components in a 2D graph. At the same time, the corresponding 3D geometry of the system components can be viewed and manipulated in a mechanical CAD software package (such as ProEngineer) or a web-based Java 3D viewer. By integrating the simulation environment with the design environment, designers can receive immediate feedback on the impact of their design decisions. This allows them to explore more design alternatives and create better, cheaper products faster.
In distributed robotics, multiple robots collaborate with each other to perform sensing
and actuation tasks that surpass the capabilities of a single robot on the team. Team
members may exchange sensor information, help each other to scale obstacles, or
collaborate to manipulate heavy objects. However, to achieve true collaboration, several
interesting research topics still need to be addressed. These topics range from robot team
localization and navigation, to sensor fusion, information sharing, and machine learning
for robot teams. To evaluate our research results, we have developed a team of
heterogeneous, centimeter-scale robots that collaborate to map and explore unknown
environments. The robots, called Millibots, are configured from modular components that
include sonar and IR sensors, camera, communication, computation, and mobility modules.
For mapping and exploration with multiple robots, it is critical to know the relative
positions of each robot with respect to the others. We have developed a novel localization
system that uses sonar-based distance measurements to determine the positions of all the
robots in the group. With their positions known, we use an occupancy grid Bayesian mapping
algorithm to combine the sensor data from multiple robots with different sensing
modalities.
Last modified: Wed May 31 18:47:07 EDT 2000