Robot autonomy is driving an ever-increasing demand for computational power, including on-board multi-core CPUs and accelerators such as GPUs, to enable fast perception, planning, control, and more. Careful scheduling of these computational tasks on the CPU cores and GPUs is important to prevent locking up the finite computational capacity in ways that hinder other critical workloads; delays in computing time-critical tasks like obstacle detection and control can have huge negative consequences for autonomous robots, potentially resulting in damage, substantial financial loss, or even loss of life. In this paper, we leverage recent advances from real-time systems research. We apply TimeWall, a component-based real-time framework, to the computational components of an autonomous drone and experimentally show that the timeliness and safe operation properties of a drone are preserved even in the presence of increasing interfering computational processes.

As scientific research in chemistry, materials science, and applied sciences becomes increasingly complex and data-driven, there is a growing need for efficient, scalable, and flexible automation to accelerate discoveries and reduce human burden and error in laboratories. We introduce the Experiment Orchestration System (EOS), an open-source software framework and runtime offering a comprehensive foundation for laboratory automation. EOS offers an extensible framework allowing users to define labs, devices, tasks, experiments, and optimization criteria using YAML and Python plugins, and also offers a distributed runtime for managing and executing automation. EOS has a central orchestrator that communicates with and controls laboratory equipment to execute tasks. EOS implements autonomous experiment campaigns, parameter optimization, task scheduling, result aggregation, and more. By providing a common infrastructure for laboratory automation, EOS aims to reduce automation implementation barriers and accelerate discoveries in science laboratories.

People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions.

Lab automation has the potential to accelerate scientific progress in the natural sciences, allowing tedious experiments that would require many hours of human time to be automated, enabling higher accuracy, efficiency, and repeatability. Mobile manipulation robots have the potential to work in chemistry labs designed for humans to complete tasks for which setting up customized factory-scale automation is premature or infeasible. We present a new method to enable a mobile manipulation robot to automate injections, a common task in chemistry labs when using equipment such as gas chromatographs (GCs) for analyzing the contents of a sample mixture. This task is challenging for a mobile manipulation robot due to the need to navigate to the equipment in the lab and then achieve millimeter-scale accuracy required for the syringe positioning. Our approach leverages deep learning to create a model capable of localizing the syringe with high accuracy using cameras mounted on the chemistry equipment, and then uses a visual servoing approach based on the syringe's needle localization to achieve the injection. We demonstrate that our approach is robust to uncertainty in navigation as well as uncertainty in the grasping position and orientation of the syringe, achieving errors sufficiently small to enable the mobile manipulation robot to automate injections in real chemistry equipment.

In this paper we present Drone Brush, a prototype mixed reality interface for immersive planning of drone paths for tasks such as collaborative photogrammetry and inspection. This interface employs Microsoft's HoloLens 2 to allow users to draw paths for drone navigation in 3D using hand gestures. Users can place waypoints with a simple pinch gesture, and similarly, delete and move existing waypoints. To validate paths, we leverage the HoloLens spatial map to check for potential collisions ahead of time, greatly reducing the likelihood of a collision during drone navigation. Paths are simplified and cleaned up using density-based clustering to prevent complex or redundant drone movement. In this Late-Breaking Report, we present the design and implementation of our system that integrates mixed reality, natural hand gestures, and drone path planning, which we plan to evaluate in a user study in the near future.