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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2020

Subscale Indoor Trajectory Testing via Six DOF Tendon-Actuated Robot

The NASA Johnson Space Center (JSC) Safe and Precise Landing â€“ Integrated Capabilities Evolution (SPLICE) project is developing a large-scale six degree of freedom tendon-actuated robot (STAR). STAR is intended to reproduce lunar approach trajectories across subscale terrain in order to rapidly develop and test terrain relative and hazard relative navigation algorithms at low cost. NASA is interested in hosting external partners at the facility to enable them to advance their own algorithm development and testing and intends to activate the facility in Fall 2020. Contact us for more information.

Pedrotty, Samuel M.↗

DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗

NASA NTRS2001

Task-Level Control for a Full Semi-Autonomous Mission: Test Platform Development and Demonstration

Pseudolites can extend the availability of GPS-type positioning systems to a wide range of applications not possible with satellite-only GPS, including indoor and deep-space applications. Conventional GPS pseudolite arrays require that the devices be pre-calibrated through a survey of their locations, typically to sub-centimeter accuracy. This can sometimes be a difficult task, especially in remote or hazardous environments. By using the GPS signals that the pseudolites broadcast, however, it is possible to have the array self-survey its own relative locations, creating a Self-Calibrating Pseudolite Array (SCPA). In order to provide the bi-directional ranging signals between devices necessary for array self-calibration, pseudolite transceivers must be used. The basic principles behind the use of transceivers to create an SCPA were first presented in paper presented to the Institute of Navigation GPS-98 Conference. This paper begins with a brief review of the transceiver architecture and the fundamental direct-ranging algorithm presented in that paper. This is followed by a description of a prototype self-differencing transceiver system that has been constructed, and a presentation of experimental code- and carrier-phase ranging data obtained using that system. A second algorithm is then described which uses these fundamental range measurements between transceiver pairs to self-calibrate a larger stationary array and to provide positioning information for a vehicle moving within that array. Simulation results validating the accuracy and effective convergence of this algorithm are also presented.

Rock, Stephen M.↗

NASA NTRS1993

Scene segmentation of natural images using texture measures and back-propagation

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Sridhar, Banavar↗