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Planetary landing vehicle design optimization, considering effects of trajectory, guidance and environmental parameters under uncertainty
Contents include the following: 3 Listing of critical capabilities (knowledge, procedures, training, facilities) and metrics for validating that they are mission ready. Examples of critical capabilities and validation metrics: ground test and simulations. Flight testing to prove capabilities are mission ready. Issues and recommendations.
As planetary spacecraft landing system complexity increases, pre-flight performance assessments include correspondingly more detailed and complex models that represent the various environmental elements and vehicle subsystems. Specifically, the relevance and criticality of planetary terrain modeling in the context of precision landing and hazard avoidance (PL&HA) has become evident as both commercial and national agencies return to the Lunar surface and beyond.
Nuclear-electric spacebus for planetary landing missions
Safe, precise landing on planetary bodies requires knowledge of altitude and velocity, and may require active detection and avoidance of hazardous terrain. Radar offers a superior solution to both problems due to its ability to operate at any time of day, through dust and engine plumes, and ability to detect velocity coherently. While previous efforts have focused on providing near term solutions to the safe landing problem, we are designing radar velocimeters and radar imagers for missions beyond the next decade. In this paper we identify the fundamental issues within each approach, at arrive at strawman sensor designs at a center frequency at or around 160 GHz (Gband). We find that a G-band radar velocimeter design is capable of sub-10 cm/s accuracy, and a G-band imager is capable of sub-0.5 degree resolution over a 28 degree field of view. From those designs, we arrive at the key technology requirements for the development of power and low noise amplifiers, signal distribution methods, and antenna arrays that enable the construction of these next generation sensors.
Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.
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Computerized tradeoff analysis for planetary landing vehicle entry capsule and lander design optimization, emphasizing weight allocation for Mars 1973 missions
Development and testing of planetary landing parachute system
Fabrication procedure for biologically clean planetary landing module for later sterilization
Power systems related to Mars landing capsule
We describe our planned use of optical correlation in landmark navigation associated with planetary landing. Standard correlation provides 'pointing-to' information, giving the vector to the landmark from the spacecraft in the spacecraft frame. The synthetic estimation filter (SEF) provides 'pointing-from' information, estimating the vector from the landmark to the spacecraft. Digital and optical SEFs were constructed and compared using a Martian-like 3D modelboard to provide test images. The digital SEF worked reasonably well, but the optical SEF did not perform as expected. The optical SEF was implemented with a liquid crystal television (LCTV) correlator that had been used successfully in previous SEF experiments using a spacecraft model. The results suggest the SLM model for the LCTV needs further refinements. Both the digital and optical filter provided good pointing-to results. We did not plot the correlation surface for the digital SEF response, and though it is less sharp than the POF, it had only a one-pixel variation in the peak location. Surface plots for the conventional optical phase-only filter produced a correlation peak that was sharp enough to be located within two pixels.
This work presents a novel landing site relative topographic measurement model for aslant range sensor being utilized for precision planetary landing operations. The measurement model accounts for the local terrain the slant range sensor captures and leverages knowledge of the estimated landing site provided by the navigation filter. Notably, in contrast to previous works, the new model does not rely on surface normal approximation, reducing the model’s sensitivity to noisy digital elevation maps which represent the local topography. In addition to the measurement model, this work introduces a novel fault detection method, denoted the probabilistic inspection of topographic filter altitude likelihood (PITFAL) algorithm, that implements a statistical outlier rejection algorithm. PITFAL is designed for multi-beam slant range sensors, such as the Navigation Doppler LIDAR (NDL), and identifies statistically inconsistent range estimates through a consensus check on the set of apparent altitudes computed for each individual beam. These models are numerically validated by the Safe and Precise Landing Capability Evolution (SPLICE) project’s high-fidelity terrestrial and lunar lander simulations.
This work presents a novel landing site relative topographic measurement model for aslant range sensor being utilized for precision planetary landing operations. The measurement model accounts for the local terrain the slant range sensor captures and leverages knowledge of the estimated landing site provided by the navigation filter. Notably, in contrast to previous works, the new model does not rely on surface normal approximation, reducing the model’s sensitivity to noisy digital elevation maps which represent the local topography. In addition to the measurement model, this work introduces a novel fault detection method, denoted the probabilistic inspection of topographic filter altitude likelihood (PITFAL) algorithm, that implements a statistical outlier rejection algorithm. PITFAL is designed for multi-beam slant range sensors, such as the Navigation Doppler LIDAR (NDL), and identifies statistically inconsistent range estimates through a consensus check on the set of apparent altitudes computed for each individual beam. These models are numerically validated by the Safe and Precise Landing Capability Evolution (SPLICE) project’s high-fidelity terrestrial and lunar lander simulations.
The navigation filter architecture successfully deployed on the Morpheus flight vehicle is presented. The filter was developed as a key element of the NASA Autonomous Landing and Hazard Avoidance Technology (ALHAT) project and over the course of 15 free fights was integrated into the Morpheus vehicle, operations, and flight control loop. Flight testing completed by demonstrating autonomous hazard detection and avoidance, integration of an altimeter, surface relative velocity (velocimeter) and hazard relative navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman flter software, and landing within 2 meters of the vertical testbed GPS-based navigation solution at the safe landing site target. Morpheus followed a trajectory that included an ascent phase followed by a partial descent-to-landing, although the proposed filter architecture is applicable to more general planetary precision entry, descent, and landings. The main new contribution is the incorporation of a sophisticated hazard relative navigation sensor-originally intended to locate safe landing sites-into the navigation system and employed as a navigation sensor. The formulation of a dual-state inertial extended Kalman filter was designed to address the precision planetary landing problem when viewed as a rendezvous problem with an intended landing site. For the required precision navigation system that is capable of navigating along a descent-to-landing trajectory to a precise landing, the impact of attitude errors on the translational state estimation are included in a fully integrated navigation structure in which translation state estimation is combined with attitude state estimation. The map tie errors are estimated as part of the process, thereby creating a dual-state filter implementation. Also, the filter is implemented using inertial states rather than states relative to the target. External measurements include altimeter, velocimeter, star camera, terrain relative navigation sensor, and a hazard relative navigation sensor providing information regarding hazards on a map generated on-the-fly.
During the Mars Exploration Rovers (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combines measurements from a descent camera, a radar altimeter and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm uses altitude and attitude measurements to rectify image data to level ground plane. Feature selection and tracking is employed in the rectified data to compute the horizontal motion between images. Differences of motion estimates are then compared to inertial measurements to verify correct feature tracking. DIMES combines sensor data from multiple sources in a novel way to create a low-cost, robust and computationally efficient velocity estimation solution, and DIMES is the first use of computer vision to control a spacecraft during planetary landing. In this paper, the detailed implementation of the DIMES algorithm and the results from the two landings on Mars are presented.
A second generation, compact, real-time, air-cooled 3-D imaging Flash Lidar sensor system, developed from a number of cutting-edge components from industry and NASA, is lab characterized and helicopter flight tested under the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) project. The ALHAT project is seeking to develop a guidance, navigation, and control (GN&C) and sensing system based on lidar technology capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The Flash Lidar incorporates a 3-D imaging video camera based on Indium-Gallium-Arsenide Avalanche Photo Diode and novel micro-electronic technology for a 128 x 128 pixel array operating at a video rate of 20 Hz, a high pulse-energy 1.06 μm Neodymium-doped: Yttrium Aluminum Garnet (Nd:YAG) laser, a remote laser safety termination system, high performance transmitter and receiver optics with one and five degrees field-of-view (FOV), enhanced onboard thermal control, as well as a compact and self-contained suite of support electronics housed in a single box and built around a PC-104 architecture to enable autonomous operations. The Flash Lidar was developed and then characterized at two NASA-Langley Research Center (LaRC) outdoor laser test range facilities both statically and dynamically, integrated with other ALHAT GN&C subsystems from partner organizations, and installed onto a Bell UH-1H Iroquois "Huey" helicopter at LaRC. The integrated system was flight tested at the NASA-Kennedy Space Center (KSC) on simulated lunar approach to a custom hazard field consisting of rocks, craters, hazardous slopes, and safe-sites near the Shuttle Landing Facility runway starting at slant ranges of 750 m. In order to evaluate different methods of achieving hazard detection, the lidar, in conjunction with the ALHAT hazard detection and GN&C system, operates in both a narrow 1deg FOV raster-scanning mode in which successive, gimbaled images of the hazard field are mosaicked together as well as in a wider, 4.85deg FOV staring mode in which digital magnification, via a novel 3-D superresolution technique, is used to effectively achieve the same spatial precision attained with the more narrow FOV optics. The lidar generates calibrated and corrected 3-D range images of the hazard field in real-time and passes them to the ALHAT Hazard Detection System (HDS) which stitches the images together to generate on-the-fly Digital Elevation Maps (DEM's) and identifies hazards and safe-landing sites which the ALHAT GN&C system can then use to guide the host vehicle to a safe landing on the selected site. Results indicate that, for the KSC hazard field, the lidar operational range extends from 100m to 1.35 km for a 30 degree line-of-sight angle and a range precision as low as 8 cm which permits hazards as small as 25 cm to be identified. Based on the Flash Lidar images, the HDS correctly found and reported safe sites in near-real-time during several of the flights. A follow-on field test, planned for 2013, seeks to complete the closing of the GN&C loop for fully-autonomous operations on-board the Morpheus robotic, rocket-powered, free-flyer test bed in which the ALHAT system would scan the KSC hazard field (which was vetted during the present testing) and command the vehicle to landing on one of the selected safe sites.
Two flash lidars, integrated from a number of cutting-edge components from industry and NASA, are lab characterized and flight tested for determination of maximum operational range under the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project (in its fourth development and field test cycle) which is seeking to develop a guidance, navigation, and control (GN&C) and sensing system based on lidar technology capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The flash lidars incorporate pioneering 3-D imaging cameras based on Indium-Gallium-Arsenide Avalanche Photo Diode (InGaAs APD) and novel micro-electronic technology for a 128 x 128 pixel array operating at 30 Hz, high pulse-energy 1.06 micrometer Nd:YAG lasers, and high performance transmitter and receiver fixed and zoom optics. The two flash lidars are characterized on the NASA-Langley Research Center (LaRC) Sensor Test Range, integrated with other portions of the ALHAT GN&C system from partner organizations into an instrument pod at NASA-JPL, integrated onto an Erickson Aircrane Helicopter at NASA-Dryden, and flight tested at the Edwards AFB Rogers dry lakebed over a field of human-made geometric hazards during the summer of 2010. Results show that the maximum operational range goal of 1 km is met and exceeded up to a value of 1.2 km. In addition, calibrated 3-D images of several hazards are acquired in real-time for later reconstruction into Digital Elevation Maps (DEM's).