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Ono, Hiro

Publications and source records attributed to Ono, Hiro.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

First 210 solar days of Mars 2020 Perseverance Robotic Operations – Mobility, Robotic Arm, Sampling, and Helicopter

This paper includes the summary, lessonslearned, and upcoming plans for the first 210 Mars solar days(sols) of the mission. The focus of the paper is on roboticoperations which has the primary responsibility for strategicplanning, uplink commanding and downlink analysis forrover mobility and navigation, robotic arm operation, thesampling and caching capability including coring, theadaptive caching assembly and the 2nd sample handlingrobotic arm, and interface to the Mars helicopter Ingenuity.As of Sol 210 the rover has driven 2663.65 meters, executed20764 robotic arm and sampling commands, and hassuccessfully completed 13 helicopter flights covering 2382meters horizontal distance. It includes the OperationsReadiness Tests in preparation for landing, landing and initialcheckouts, strategic route planning to the science destinationand waypoints, surface checkout of all of the roboticscapability of the rover. It also discusses the strategic planningand tactical agility needed for interleaving scienceinvestigation and technology demonstration of the Marshelicopter flights where a minimum distance had to bemaintained between the rover and helicopter during flights. Itdiscusses the challenges with planning robotic operations andaddressing anomalies with the larger uncertainty presentduring early mission operations. It also discusses the impacton robotic operations from lessons incorporated fromprevious missions.

Ono, Hiro↗
NASA NTRS2022

Space Applications of a Trusted AI Framework: Experiences and Lessons Learned

Artificial intelligence (AI), which encompasses machine learning (ML), has become a critical technology due to its well-established success in a wide array of applications. However, the proper application of AI remains a central topic of discussion in many safety-critical fields. This has limited its success in autonomous systems due to the difficulty of ensuring AI algorithms will perform as desired and that users will understand and trust how they operate. In response, there is growing demand for trustability in AI to address both the expectations and concerns regarding its use. The Aerospace Corporation (Aerospace) developed a Framework for Trusted AI (henceforth referred to as the framework) to encourage best practices for the implementation, assessment, and control of AI-based applications. It is generally applicable, being based on terms and definitions that cut across AI domains, and thus is a starting point for practitioners to tailor to their particular application. To help demonstrate how the framework can be tailored into mission assurance guidance for the space domain, Aerospace sought the involvement of the Jet Propulsion Laboratory (JPL) to engage with actual examples of AI-based space autonomy.

Kaufman, James↗
NASA NTRS2022

Adapting a Trusted AI Framework to Space Mission Autonomy

As artificial intelligence (AI) is increasingly pro- posed for new and future capabilities in space missions, the question of how to trust AI-enabled space autonomy has been explored. Recently, a collaboration between The Aerospace Corporation (Aerospace) and NASA’s Jet Propulsion Labora- tory (JPL) investigated how Aerospace’s Trusted AI Frame- work could be applied to two JPL projects that planned on lev- eraging AI for critical autonomous tasks. This combined effort led to many insights in the practical implementation of trusted AI along with considerable updates to the Trusted AI Frame- work that tailored its topic threads to space exploration. This document cohesively summarizes the enhanced framework as tailored to space missions as well as estimation of the level of trust required as a function of mission criticality and key stakeholders. The goal of this work is to provide a set of best practices to inform autonomy researchers, flight engineers, mission and proposal reviewers, and instrument and mission principal investigators (PI’s) to drive AI-based autonomy that maximizes trust and lowers the barriers to mission adoption for both science and engineering applications.

Amini, Rashied↗
NASA NTRS2021

AI4MARS: A Dataset for Terrain-Aware Autonomy on Mars

Deep learning has quickly become a necessity for selfdriving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA’s latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by ~10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.1

Ono, Hiro↗
NASA NTRS2019

ARIEL: Autonomous Excavation Site Selection for Europa Lander Mission Concept

This paper presents ARIEL (Autonomous Ranking and Interrogation of Excavation Location), an autonomy system for selecting an excavation site on-board for NASA’s Europa Lander Mission Concept. Historically, excavation site selection has been performed by a lengthy ground-in-the-loop (GITL) process involving manual inspections, assessments, and decision making in past missions. However, as Europa Lander would have approximately 20 days of lifetime after the landing, many surface activities, including excavation site selection, must be autonomously performed on-board. This paper describes the overall system of ARIEL as well as its two major algorithmic components: vision-based candidate selection and smart interrogation, which estimates the physical properties of the icy surface through physical contact with the robotic arm’s endeffector. Preliminary results are presented using images from Earth analogue sites. The Europa Lander mission returned to the formulation phases in early 2019 while ARIEL was at an early stage of development. Described in this paper is a snapshot of ARIEL as of the project suspension. This paper also describes the remaining challenges to be solved, should the mission resume in the future.

Ono, Hiro↗
NASA NTRS2018

E-Glider: Active Electrostatic Flight for Airless Body Exploration

The environment near the surface of asteroids, comets, and the Moon is electrically charged due to the Sun's photoelectric bombardment and lofting dust, which follows the Sun illumination as the body spins. Chargeddust is ever present, in the form of dusty plasma, even at high altitudes, following the solar illumination. If abody with high surface resistivity is exposed to the solar wind and solar radiation, sun-exposed areas andshadowed areas become differentially charged. The E-Glider (Electrostatic Glider) is an enabling capability foroperation at airless bodies, a solution applicable to many types of in-situ mission concepts, which leverages thenatural environment. With the E-Glider, we transform a problem (spacecraft charging) into an enablingtechnology, i.e. a new form of mobility in microgravity environments using new mechanisms and maneuveringbased on the interaction of the vehicle with the environment. Consequently, the vision of the E-Glider is toenable global scale airless body exploration with a vehicle that uses, instead of avoids, the local electricallycharged environment. This platform directly addresses the "All Access Mobility" Challenge, one of the NASA'sSpace Technology Grand Challenges. Exploration of comets, asteroids, moons and planetary bodies is limitedby mobility on those bodies. The lack of an atmosphere, the low gravity levels, and the unknown surface soilproperties pose a very difficult challenge for all forms of know locomotion at airless bodies. This E-Gliderlevitates by extending thin, charged, appendages, which are also articulated to direct the levitation force in themost convenient direction for propulsion and maneuvering. The charging is maintained through continuouscharge emission. It lands, wherever it is most convenient, by retracting the appendages or by firing a cold-gasthruster, or by deploying an anchor. The wings could be made of very thin Au-coated Mylar film, which areelectrostatically inflated, and would provide the lift due to electrostatic repulsion with the naturally chargedasteroid surface. Since the E-glider would follow the Sun's illumination, the solar panels on the vehicle wouldconstantly charge a battery. Further articulation at the root of the lateral strands or inflated membrane wings,would generate a component of lift depending on the articulation angle, hence a selective maneuveringcapability which, to all effects, would lead to electrostatic (rather than aerodynamic) flight. Preliminarycalculations indicate that a 1 kg mass can be electrostatically levitated in a microgravity field with a 2 mdiameter electrostatically inflated ribbon structure at 19kV, hence the need for a "balloon-like" system. Due tothe high density and the photo-electron sheath and associate small Debye length, significant power is requiredto levitate even a few kilograms. The power required is in the kilo-Watt range to maintain a constant chargelevel.

Electrostatic Flight↗