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UPS 6000 and UP Xtreme i11 Robot Kits
Create Your Own Robot Kit
Perception
Navigation
Simulation
Benchmarking and Profiling
EI for AMR Container on a Virtual Machine
Fibocom’s FM350 5G Module Integration
Change Existing and Add New Docker* Images to the EI for AMR SDK
Troubleshooting for Robot Tutorials
Spatial Partitioning and Search Operations with Octrees
Constructing a Convex Hull Polygon for a 3D Point Cloud
Detecting Specific Models and Their Parameters in 3D Point Clouds
Plane Model Segmentation
Surface Reconstruction with Intel® oneAPI Base Toolkit's Moving Least Squares (MLS)
Intel® oneAPI Base Toolkit's Iterative Closest Point (ICP)
KdTree Search Using Intel® oneAPI Base Toolkit's KdTree FLANN
Downsampling 3D Point Clouds with a Voxelized Grid
Filtering Point Clouds with a Passthrough Filter
PCL Optimizations Outside of Docker* Images
Optimized PCL Known Limitation
Optimized PCL Troubleshooting
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Remote Inference End-to-End Use Case
This tutorial describes how to use the basic fleet management server to set object detection inference on EI for AMR remotely at the OpenVINO™ model server when its battery is lower than the 60% threshold. If the battery is equal to or greather than 60%, the inference is set to be done locally at EI for AMR.
Prerequisites:
The server is configured with the Get Started Guide for Robot Orchestration.
The robot is onboarded with the Device Onboarding End-to-End Use Case.
Collaboration Diagram
When a robot’s battery level is less than 60%, basic fleet management tells the robots to do Remote Inference. When the battery level is back to equal or greater than 60%, basic fleet management tells the robots to do Local Inference. The following diagram depicts the steps.
Check Object Detection with Local or Remote Inference
On the robot
Example logs when local inference is performed:
[object_detection_node-3] [object_detection_node-3] [ INFO ] <LocalInference> Done frame: 5516 . Processed in: 0.279625 ms [object_detection_node-3] [object_detection_node-3] [ INFO ] <LocalInference> Label tv [object_detection_node-3] [object_detection_node-3] [ INFO ] <LocalInference> Done frame: 5517 . Processed in: 0.240508 ms [object_detection_node-3] [object_detection_node-3] [ INFO ] <LocalInference> Label tv
Example logs when remote inference is performed:
[object_detection_node-3] [INFO] [1643382428.696445729] [object_detection]: switchToRemoteInfCallback [object_detection_node-3] [INFO] [1643382428.720869717] [object_detection]: <RemoteInference> Sending Image [object_detection_node-3] [INFO] [1643382428.854300655] [object_detection]: <RemoteInference> Sending Image [remote_inference-4] [INFO] [1643382428.863697253] [remote_inference]: <RemoteInference> Receiving video frame [object_detection_node-3] [INFO] [1643382428.882912426] [object_detection]: <RemoteInference> Sending Image [remote_inference-4] [INFO] [1643382428.895332223] [remote_inference]: <RemoteInference> Processing and inference took 31.16 [object_detection_node-3] [INFO] [1643382428.896419726] [object_detection]: <RemoteInference> Detected Objects Received [object_detection_node-3] [INFO] [1643382428.896478543] [object_detection]: <RemoteInference> Label : tv [remote_inference-4] [INFO] [1643382428.897817637] [remote_inference]: <RemoteInference> Receiving video frame [object_detection_node-3] [INFO] [1643382428.921090305] [object_detection]: <RemoteInference> Sending Image [remote_inference-4] [INFO] [1643382428.922211172] [remote_inference]: <RemoteInference> Processing and inference took 23.68
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