Overview:
Recent research has developed sensor networks, where tiny devices with processors, memory, and wireless communication can be deployed to collaboratively sense and reason about the environment around them. Sensor nets are versatile enough to be thrown out into some outdoor environment, or can be integrated into offices and homes to create “smart buildings.” We have begun investigating the concept of an Actuator Networks, where we enhance these passive sensing devices with distributed actuation, such as emitting different amplitudes and wavelengths of light or sound, to affect an environment. The general idea is to generate a “potential field” of attracting and repelling actuators.
We forsee a number of interesting theoretical and practical problems as we must not only develop new algorithms to determine the correct actuation for the desired goal in a general context, but we must also determine which modalities are most applicable for our subject to easily observe or be controlled by the potential field for our specific applications.
Emphasized Applications
Security
Examples we are currently investigating include using a pulse of sound to get a person’s attention for better facial recognition (which is currently very dependent on angle of the image) or using a bright light, unpleasant odor or annoying sound to coax people away from a region for issues of crowd control.
Natural Environments
We are experimenting with using modalitites such as light and sound to attract and scare animals in order to induce them to move to a desired location. For instance, we could actively guide birds toward a desired zone for closer viewing or herd animals animals.
Emergency Evacuation
We are exploring the use of action nets either pre-installed in "smart homes" or dynamically deployed by emergency personnel during search and rescue operations to help guide both those the search and rescue team and the trapped victims out of the building safely. In the firefighter scenario, the sensors (called motes) would sense heat and light up red or green in different amplitudes to shape a green corridor for rapid exit.
Current Work
Publication
Actuator Networks: Inducing Potential Fields to Guide a Moving Element to a Desired Position Using Push-Cage-Squeeze Cycles. Jeremy Schiff, Danny Bazo, Vincent Duindam, Dezhen Song, and Ken Goldberg. International Conference on Robotics and Automation (ICRA) . [Submitted] Pasadena, California. May 2008.
Abstract
Building on recent work in sensor networks and distributed manipulation, we propose Actuator Networks--networks of devices capable of exerting influence on their environment in addition to monitoring it. We show how an Actuator Network can be used to guide a moving element to a desired location through the creation of potential gradients, and introduce an algorithm capable of calculating the required actuation. In this algorithm, motion is achieved with three steps: ``Push, Cage, and Squeeze'', whose sequential application we term a PCS cycle. Guiding a moving element via PCS cycles is robust to modeled trajectory error and provides a framework into which path planning and obstacle avoidance can be integrated. We explore the PCS cycle as an example of one of the types of distributed actuation possible with an Actuator Network.
We introduce models, notation, terms and properties related to the nature of Actuator Networks, describe the distributed guidance algorithm, and performed simulations showing how an Actuator Network with eight nodes can guide a moving element to a desired location while avoiding obstacles.
Examples
 |
A triangulated Actuator Network with actuators shown as squares, the moving element's locations over time depicted with circles, and an obstacle represented by the black rectangle. The hashed regions depict increasing uncertainty in the moving element's trajectory over time due to a single actuator performing a Push step. The solid triangular regions show the reduction in trajectory uncertainty due to Cage and Squeeze steps. This figure illustrates how the PCS algorithm is robust to trajectory uncertainty and capable of being integrated with path planning for obstacle avoidance. |
 |
An example of repeated PCS cycles, followed by a final Squeeze. The squares correspond to actuators, and the circle represents the moving element. The letters P, C, and S indicate which step was performed. |
 |
Results from a simulation of the PCS algorithm with an obstacle (black) within the Actuator Network's workspace. Plot 1 shows the calculated capture regions (shaded) for each triangle's incenter along the path from x_0 (top left circle) to x_f (bottom right circle). Plots 2-5 show alternating Push, Cage and Squeeze steps. The entire trajectory of the moving element is shown in Plot 6. |
Members
Profs: Ken Goldberg, Steven Wicker, Dezhen Song
Post-Docs: Vincent Duindam
PhD Students: Jeremy Schiff
Undergraduates: Danny Bazo
UC Berkeley and Cornell University, Summer 2007 - Present
Funding:
|
NSF Science and Technology Center, Team for Research in Ubiquitous Secure Technologies, NSF CCF-0424422, with additional support from Cisco, HP, IBM, Intel, Microsoft, Symmantec, Telecom Italia and United Technologies. |
| CONE Project NSF Award 0534848/0535218 Robotics and Robust Intelligence Program Division of Information and Intelligent Systems Directorate for Computer Science and Engineering National Science Foundation |
