Autonomous Integration of Bench-Top Wet Lab Equipment

Laboratory automation is an expensive and complicated endeavor with limited inflexible options for small-scale labs. We develop a prototype system for tending to a bench-top centrifuge using computer vision methods for color detection and circular Hough Transforms to detect and localize centrifuge buckets. Initial results show that the prototype is capable of automating the usage of regular bench-top lab equipment.

Autonomous Soil Collection in Environments With Heterogeneous Terrain

To autonomously collect soil in uncultivated terrain, robotic arms must distinguish between different amorphous materials and submerge themselves into the correct material. We develop a prototype that collects soil in heterogeneous terrain. If mounted to a mobile robot, it can be used to perform soil collection and analysis without human intervention. Unique among soil sampling robots, we use a general-purpose robotic arm rather than a soil core sampler.

Automated Multimodal Data Annotation via Calibration With Indoor Positioning System

Learned object detection methods based on fusion of LiDAR and camera data require labeled training samples, but niche applications, such as warehouse robotics or automated infrastructure, require semantic classes not available in large existing datasets. Therefore, to facilitate the rapid creation of multimodal object detection datasets and alleviate the burden of human labeling, we propose a novel automated annotation pipeline. Our method uses an indoor positioning system (IPS) to produce accurate detection labels for both point clouds and images and eliminates manual annotation entirely. In an experiment, the system annotates objects of interest 261.8 times faster than a human baseline and speeds up end-to-end dataset creation by 61.5%.