Terrain Relative Navigation for Safe Landing of the Perseverance Mars Rover
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As a foundational element of the National Aeronautics and Space Administration (NASA) Artemis Campaign, the Gateway is an incrementally built cislunar spacecraft that will serve as a platform for deep space human exploration, science, and technology demonstration. The Gateway will be a unifying catalyst for partners around the world to establish sustained deep space scientific investigations, lunar surface access, and missions to Mars. As human exploration moves farther from Earth, spacecraft designs must optimize mass and volume while minimizing human and spacecraft risk. To accomplish this, the Gateway Program Safety and Mission Assurance (S&MA) office develops, implements, and ensures compliance with requirements, in concert with the accurate characterization and transparent communication of residual hazard risks, for integrated safety, reliability and maintainability, and quality assurance. This paper will detail the evolution of the Gateway Program S&MA integration functions, and provide its current status and lessons learned for future human spaceflight programs.
We previously presented Soil Property and Object Classification (SPOC), a machine learning-based terrain classifier for Mars rovers, for automatically segmenting rover images by its surface type such as sand and bedrock. This paper presents a number of practical improvements to pave the way for potential future onboard deployment. First, we achieved 97.0% overall pixel accuracy, evaluated against the classification generated by human experts on images from Mars Science Laboratory (MSL) missions. The substantial increase in accuracy was primarily enabled by the sheer volume of data used for training; we created a new large-scale dataset of Martian terrain labels, namely AI4Mars, which contains more than 400k labels contributed by citizen scientists for 50k images taken by the Mars Exploration Rovers (MER) and Mars Science Laboratory (MSL) rover. Second, we demonstrated that SPOC can quickly adapt to a new mission landed on a previously unseen site. Specifically, we pretrained a model with MER and MSL data from the AI4Mars dataset and then adapted to the Mars 2020 Rover (M2020) by feeding a small volume of data between Sol 0 and 157; the adapted model was tested on Sol 200-203 and resulted in 84.2% overall pixel accuracy and 93.4% reliability (recall) for detecting sand, the most concerning class for rover’s traversability. Third, we found that pretraining can substantially mitigate the decline of accuracy over time. We showed that the performance of a SPOC model pretrained with the ImageNet dataset and then trained by MSL images only up to Sol 390 remains comparable to a model trained by images up to Sol 1689 on the test data after Sol 1689. Fourth, we reimplemented SPOC with a light-weight convolutional neural network (CNN), MobileNetV2, which typically runs within tens of milliseconds (ms) on mobile processors such as Qualcomm’s Snapdragon. Finally, we released the AI4Mars dataset to the public to encourage open innovation.
This next wave of lunar exploration will be fundamentally different than the past. The goal today is long term human presence in Earth orbit, exploration around the Moon, exploration anywhere on the Moon, and challenging exploration missions beyond the Moon. Sustainability is the key to enabling human expansion across the solar system and bringing back to Earth new knowledge and opportunities, and requires the early engagement of scientific, international, and U.S. commercial interests, innovations, and new approaches. The Gateway’s agile acquisition strategy will shape the entire system life cycle, from design and analysis through production, verification, launch, logistics and operations. This strategy will encourage new ways of doing business to accommodate new techniques, technologies and approaches; improving affordability and maximizing Gateway utility. The full range of acquisition authorities and contracting mechanisms available to NASA will be considered and appropriately tailored in response to the unique demands of each procurement activity. Gateway will provide scientific and research benefits, in addition to providing a sustainable staging location for crewed lunar surface landings and technology demonstrations that help NASA develop the knowledge and technology needed for Mars missions. These payloads are expected to provide operational and scientific benefit for the duration of their stay on Gateway, as the hardware will provide data during the transit to the Gateway and while in lunar orbit as changes in solar activity occur over time. Gateway is designed to support a variety of utilization activities during both crewed and uncrewed periods and is planned for continuous operations throughout its lifetime. These operations include demonstrating key exploration capabilities in a relevant deep space environment, conducting science beyond the influence of Earth, and providing opportunities for Gateway partners and customers for deep space exploration. Located in cislunar orbit, the Gateway capabilities can be further leveraged to enhance lunar and Mars exploration. With the Gateway and other elements of the Artemis Program, NASA is encouraging new ways of doing business to accommodate new techniques, technologies and approaches, consider affordability, and encourage innovation from U.S. industry.
Missions to solar system bodies must meet increasingly ambitious objectives requiring highly reliable “precision landing”, and “hazard avoidance” capabilities. To meet these needs, we have developed two lidar sensor systems that can be used individually or in concert depending on the mission requirements. One is Navigation Doppler Lidar (NDL) capable of providing vehicle precision vector velocity and ground-relative altitude and the other is an Imaging Flash Lidar for Terrain Relative Navigation and Hazard Detection and Avoidance. Using both lidar sensors together will enable landing anywhere and under any lighting condition. The NDL performance has been extensively characterized through numerous ground and aircraft flight tests and will be soon demonstrated on two lunar landing missions (mid-2023). The Flash Lidar performance is currently being assessed onboard a drone and will soon be tested on helicopter and fixed-wing aircraft platforms. This paper describes both lidar sensors and their expected operation on landing vehicles.
This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.
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NASA needs for entry, descent, and landing call for improved Precision Landing and Hazard Avoidance (PL&HA) technologies. SPLICE continues to develop, mature, demonstrate, and infuse these technologies as a portfolio. SPLICE will achieve TRL 5 on a hazard detection lidar mapping sensor and TRL 6 on two key flight software libraries for advanced guidance and hazard detection. A High-Performance Space Computing surrogate multiprocessor (ARM A53) integrates sensors and FSW. This portfolio of technologies may be infused separately or fully integrated.
Addressing the hazards and defining solutions will require a combination of human health and performance and engineering solutions. These solutions will be balanced with acceptable risks imposed on the crew and mission parameters such as duration, vehicle designs, operational considerations, and cost.
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The Artemis missions will land astronauts on the lunar surface to leverage the unmatched capabilities of human explorers. These landings will commence long-term exploration and utilization of the Moon by NASA, industry, and international partners for the benefit of all.
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The NASA Engineering and Safety Center (NESC) was requested to support the development of a governing philosophy to provide a consistent and systematic approach for fracture control certification of additive manufacturing (AM) hardware that cannot be reliably inspected. The overall goal of the NESC assessment was to make progress toward a framework for general certification of un-inspectable fracture critical AM hardware and toward approaches for evaluating risks associated with these components. This report contains the results of the assessment.
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This talk presents many of the lessons learned from several decades of conducting remote vehicle operations in space and with unmanned aircraft. The talk is intended to stimulate thought and discussion.
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Unmanned Aircraft Systems (UAS) are increasingly used to perform crucial commercial activities such as various types of inspections (crops, railroads, and bridges), surveillance, and package delivery. Regulators have become interested in developing UAS Traffic Management (UTM) systems. One promising framework for UTM allocates airspace to UAS operators via an auction. To succeed, an airspace auction must be economically efficient, fair, scalable, incentive-aligned, simple, and capable of continuously modeling airspace and sharing bid status and pricing information. This paper introduces the first airspace auction mechanism that meets these criteria. In the process, we introduce new spatial-temporal fairness constraints and a new abstraction for communicating airspace pricing information, the airspace price field. We evaluate our mechanism on UAS delivery scenarios taken from a Japan Aerospace Exploration Agency(JAXA) study and show that it scales to 1000s of bids.