There are specific hardware requirements for RapidSense to function successfully. Special attention should be given in following the requirements to achieve best performance possible.
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1. Sensors
Supported Sensors
Lower-resolution/budget applications: Intel RealSense D455
High-resolution applications: Photoneo (coming soon)
Safety-applications: SICK safeVisionary2 (coming soon)
Note: The architecture is built to currently support 2 cameras (Photoneo for high-resolution applications requiring tight tolerances ~2-10mm and willing to spend more or Intel RealSense cameras for a budget-friendly solution where resolution can be relaxed more).
Sensor Mounting
Occlusions are the parts of the environment that are not in view of the sensor due to an obstruction. The goal in placing your sensors, therefore, is to minimize occlusions. What positions will give the sensors as complete a view as possible of the robot setup at all times?
Sensor placement also depends on the robot application. For example, for a pick and place application, ask yourself, where will the robot move? Where will the occlusions be, and for which sensors?
You should expect this process to be somewhat trial-and-error.
To get the optimal performance out of the RapidSense system, keep the following guidelines in mind when mounting your sensors:
Make sure to position the sensors in your workspace to maximize the likelihood that obstacles will appear within the rated Field of View (FOV) of the sensor that is selected. Most sensor providers provide CAD models of the sensors and include the FOV in the model, which this can be used to visualize and verify the sensor ability to see the Volume of interest.
The Intel Realsense specifications are for +/- 2% depth accuracy when they’re within 2 meters of an obstacle, so we recommend placing cameras across the workspace so that obstacles are within this distance.
Space the sensors apart from one another and at different angles to provide different perspectives of the scene.
Mount the sensors securely and rigidly. If a sensor vibrates relative to robot motion, it will return noisy data resulting in false positives for obstacles. It can also cause sensors to shift and invalidate the calibration data.
Connect the sensor cables with strain relievers.
Lighting Conditions
It is recommended to control lighting conditions as best as possible for applications through adding covers to deflect direct overhead lighting, sunlight, or other direct ambient light sources. Lighting that is directed directly into the sensors or that is reflected off of very reflective surfaces can cause issues with sensor detections and should be avoided. Changes in lighting conditions during operation, such as the effects of sunlight, should be prevented. Depending on sensor selection, the effects of lighting to the system will vary.
2. Calibration Aruco Tags
Aruco tags will be necessary to acquire in order to perform calibration of the sensors to the RTR system.
At least (2) different Aruco tag patterns are required per application:
One mounted on the robot end effector (TCP location of tag will need to be provided)
One mounted static in the scene that is visible to the sensors
Additional Aruco tags may be required depending on the system configuration.
See section "Calibration Tag Information" for further information.
Calibration Tag Mounting
When you mount the calibration tags on the robot end effector, note the orientation of the tag. In the photo below, the one with the text is down.
In order to the calibration repeatable with accuracy, you may need to purchase or create a fixture of the tag and its CAD model like below.
Here are some examples of tags attached to robots.
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Target positions for each camera must be created in RapidPlan. Set the target at a location that is easily recognized by the camera. Once the target points are decided, auto-connect the targets for the calibration sequence.
3. PC spec and OS requirements
Required PC specification
Intel i7 or equivalent or better
128GB RAM minimum
I/O ports for sensors (either USB or Ethernet)
SSD hard drive preferred
nVidia or AMD GPU preferred but not required
OS Requirements
Ubuntu Linux 22.04+