NASA and other governmental agencies have supported robotics research for decades, resulting in exciting advances and incredible demonstrations---but it is difficult to adapt robotics software in response to unknown environmental changes (e.g., severe weather change or radiation change in space). The underlying problem is the limited degree of autonomy to react to unexpected environmental changes in a timely fashion, requiring human operators on Earth to devise a plan to execute based on data that had been transmitted from the robot to Earth, transmit it to the robot in space, and hope that execution of the plan proceeds as expected and more importantly, this communication is limited (e.g., few times per day). Corrections to these plans, or reactions to unexpected circumstances, could only happen after the data describing the current situation had been transmitted back to Earth and analyzed. High-latency communications associated with remote robot operations in space are cumbersome, delay mission completion, and increases the danger of rendering robots unusable.
RASPBERRY SI (Resource Adaptive Software Purpose-Built for Extraordinary Robotic Research Yields - Science Instruments) leverages software and algorithms developed under the DARPA BRASS (Building Resource Adaptive Software Systems) program, which was successfully completed in December 2019. RASPBERRY SI will work with existing and/or planned science instruments to autonomously adapt lander and instrument software (and therefore its behaviors and actions) in response to newly discovered data on the planetary surface. As an example, if instruments detect an unexpected element or compound which would ordinarily lead scientists to perform a high-fidelity analysis in a certain spectrum, the system will analyze its existing resources and reconfigure itself to perform that analysis without waiting for round trip communication to Earth for a new set of commands from the ground station.
RASPBERRY SI will provide NASA and partner scientists with unprecedented, yet necessary, capabilities to autonomously respond to newly discovered data in real-time ``on the ground". Without the capabilities provided by RASPBERRY SI, the return of valuable science data will remain slow due to extremely long round trip transmission times especially in the outer solar system, and the lander system will rest in an idle state for a significant amount of its time on the surface. When missions include time constraints (e.g., observation of transient phenomena), RASPBERRY SI becomes even more critical, as the volume of scientific data that must be collected simply cannot be obtained within the available time window.
The aim of this project is to increase the autonomy of a mission on the surface of another planet without the need for round-trip control data for human supervision. We also aim to increase the autonomy of the spacecraft in unknown and uncertain environments. This project will also increase the speed of scientific exploration via accurate task prioritization and also by reducing the number of interruptions in missions required by dynamically and carefully adapting to environmental and system changes during operation. We will demonstrate the effectiveness of our methods by deploying and optimizing state-of-the-art machine learning on the NASA testbed.
Our team is in a unique position to undertake this project as we start this project based on the DARPA BRASS technology that
we have matured over 4 years (2016-2019). This technology will enable automated software adaptation with "learning-based autonomous planning and adaptation". Our approach will deal with a wide variety of changes including adding, removing, or updating sensors, actuators, and software components, protocols, and semantic incompatibilities. Our objectives include: enabling landers to automatically adapt software that fails to meet its objectives; to automatically incorporate functionality into the lander.