All Videos Tagged open (Grasshopper) - Grasshopper

smartscreen

2013-03-07T21:05:09.416Z

screen prototype that opens and closes by reacting to changes in temperature. This is achieved by using a shape memory alloy wire (commonly known as muscle wire) which acts as both sensor, motor and actuator by applying a specific amount of voltage (15.82v in this case) which brings the wire temperature to 70 degrees. Once the wire cools down it goes back to a relaxed state, allowing the screen to go back to a closed position.

Learning to Fly in Grasshopper3d

2010-06-20T23:19:19.310Z

Video shows operation of the updated gHowl UDP Receiver, most notably, implementation of the Open Sound Control Protocol. The parameters in the Grasshopper Definition are directly controlled through an iPod Touch 2G running TouchOSC. The camera is being controlled erratically by the Accelerometer Data on the iPod Touch. gHowl is a set of interoperability components for Grasshopper.
gh 0.7.0030

gHowl is developed by Giulio Piacentino, Luis E. Fraguada, and Damien Alomar

Thanks to Andy Payne for putting some wood on the proverbial fire.

Maya Fluids in Grasshopper via UDP 2: vector field

2009-07-25T13:51:25.014Z

http://www.livearchitecture.net
This video shows another example of communication between Autodesk's Maya 2009 and McNeel's Rhinoceros/Grasshopper via UDP. This is all made possible with the collaboration of Shajay Bhooshan (http://data-tribe.net/wework4her/index. php), who is creating custom Maya nodes through the API using C++ and the Open Frameworks (http://www.openframeworks.cc/) platform. This fluidNode communicates to Grasshopper via UDP and a custom vb.net component. The GH canvas is refreshed at an interval with Giulio Piacentino's (http://www.giuliopiacentino.com/) "The Engine" component written in C#.

Here I am grabbing the velocity vector values from maya and rendering fluid velocity direction and magnitude. Video is in real time on a single Pentium M processor @ 2.26GHz and 2GB of ram.

Maya Fluid to Grasshopper via UDP

2009-07-24T21:26:33.619Z

In a collaboration with Shajay Bhooshan (http://data-tribe.net/wework4her/index.php), Autodesk Maya 2009 can communicate with McNeel’s Rhinoceros::Grasshopper. The work on communicating to and from Grasshopper via the user datagram protocol (UDP) was to effectively speed up communication between programs without the use of each program writing and reading a text file. This investigation was begun to open up Grasshopper to the outside world and eventually use it to drive physical associations via arduino and other interfaces. Processing was used initially as it is a very simple platform to used to test communication to and from Grasshopper. The first objective of the investigation was to eventually connect up with a custom plugin which Shajay Bhooshan has been developing.

On the Maya side, Shajay is controlling and augmenting this already vast platform with custom C++ API Nodes. His work can be seen on We Work 4 Her. This particular ‘fluid_UDP_Node’ transmits fluid data per voxel over UDP. Shajay has also taken advantage of the Open Frameworks, leveraging some of the code within the Maya plugin.

On the Rhino Grasshopper side, I have developed a very simple UDP receiver component in vb.net. The data is transmitted as one long string of comma separated values. Currently Shajay can send me fluid density and velocity information per voxel, but really, any type of information could be sent out. This information is parsed in the GH component and used to visualize the fluid as a Surface. As with the rest of the UDP experiments, Giulio Piacentino’s “The Engine” (http://www.giuliopiacentino.com/grasshopper-tools/) component made the refreshing of the Grasshopper canvas possible.

As you can tell, running all of these applications (including screen capturing) start to have an effect on this single processor machine. Maya can effectively optput the data between 8 - 12 frames per second depending on how many applications are running. The promising aspect of using UDP is that data can be sent from one computer to another via a network. This could effectively distribute the workload of complex combinations of processes to many devices.