Deft Control Software (DCS) for Remote Robotic Operations with Underlying Structure

BluHaptics proposes Deft Control Software (DCS), which utilizes machine learning to enable intuitive and efficient control of robotic arms in remote operations with underlying structure. The human-centered control methodology utilizes 3d sensor fusion for workspace visualization, machine learning with on-the-fly training, and pilot assist features to garner operator trust, improve safety, mitigate training latency, and support rapid task switching. The integrated algorithms identify and track underlying structure to enable pilot assistance and other safety features such as collision avoidance. DCS utilizes a common interface across robotic platforms and supports variable levels of autonomy specific to each task and/or operator. DCS permits robotic execution of exceedingly complex tasks that require high-levels of cognition and precise motor control which, to date, have been intractable for purely manual or automated control schemes to accomplish. The overall Phase I and II objectives are to: (1) demonstrate the value of a DCS interface to support intuitive manual control for remote operations, (2) demonstrate 3d visual-feedback and operator assistance supported by machine learning for tasks with underlying structure and varying levels of complexity, and (3) demonstrate the DCS platform can be extended to support different classes of robots with varying levels of autonomy. The objectives specific to Phase I are to: (1) Demonstrate intuitive manual control of a simulated NASA robot, (2) mitigate program risk by demonstrating basic assistive features, and (3) refine Phase II technical objectives based on collected user feedback and specific scenario requirements.

DCS algorithms developed as part of this programs can serve as a foundation with the potential to increases the range of beneficial tasks robots can perform and allows for improved safety and efficiency of operations across NASA mission scenarios. Future DCS algorithms could enable advanced capabilities for grappling of large payloads in space (Canadarm2 scope), for establishing and caretaking precursor structures and equipment on extraterrestrial surfaces (Valkyrie scope), and for supporting both robotic (rover scope) and astronaut-based (Robonaut 2 and MANTIS) exploration and science. Indeed, DCS could impact operational scenarios for many NASA funded projects, especially those relating to in-space manufacturing.

DCS algorithms developed for NASA have numerous applications for other U.S. Government organizations. The algorithms can be used by the Combatting Terrorism Technical Support Office (CTTSO) to safely disarm Improvised Explosive Devices (IEDs) and support the Navy with its mandate from the EPA to safely mitigate subsea unexploded ordinances (UXOs). The algorithms could also enable the Navy Research Laboratory to repair, reposition and update space satellites. The largest single commercial market for DCS algorithms developed for NASA is arguably with subsea oil and gas field service companies who routinely utilize ROVs from OEMs such as Oceaneering and Saab to solve complex intervention challenges relating to the maintenance of subsea assets. Additionally, prospective commercial opportunities may be present in nuclear decommissioning or catastrophe response applications on demolition equipment like that offered by Brokk Robotics (Sweden). It should also be noted that DCS is expected to enable increased utility for a range of robotic systems and therefore has implications for the commercial viability of other hardware products (both sensor systems and robots) which would encourage further development and commercialization in the sector.

This is early/mid-stage (TRL 5) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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