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This viewgraph presentation reviews realtime Autonomous Landing Hazard Avoidance Technology (ALHAT) technology for the return to the Moon. The topics inclde: 1) ALHAT Background; 2) Safe and Precise Landing; 3) ALHAT Mission Phases; 4) Terminal Descent Phase; 5) Lighting; 6) Lander Tolerance; 7) HDA Sensor Performance; and 8) HDA Terrain Sensors.
The Mars Sample Return campaign conducted four successful helicopter drop tests of the Earth Entry System at the Utah Test and Training Range on October, 2023. The tests acquired data for the 52.5 deg sphere cone geometry that can be used to develop models for the terminal descent aerodynamics and flight dynamics used to model ground impact conditions. The acquired test data included both sensor data outputs from an on-board inertial measurement unit, global positioning system, and video cameras as well as atmospheric measurements from weather balloons. This data was processed using a Kalman filter/smoother to reconstruct the capsule’s trajectory and aerodynamics. An equation-error method was used to reconcile the aerodynamics by solving for a set of dispersions in the aerodynamic database that form a best-fit to the reconstructed aerodynamics. The reconstructed trajectories were compared with flight simulations generated using Program to Optimize Simulated Trajectories II. The results indicate that the simulation underpredicted the vehicle oscillation amplitudes. The reconstruction exhibits a persistent low-amplitude oscillation that does not damp out as predicted.
The Mars Sample Return campaign conducted four successful helicopter drop tests of the Earth Entry System at the Utah Test and Training Range on October, 2023. The tests acquired data for the 52.5 deg sphere cone geometry that can be used to develop models for the terminal descent aerodynamics and flight dynamics used to model ground impact conditions. The acquired test data included both sensor data outputs from an on-board inertial measurement unit, global positioning system, and video cameras as well as atmospheric measurements from weather balloons. This data was processed using a Kalman filter/smoother to reconstruct the capsule’s trajectory and aerodynamics. An equation-error method was used to reconcile the aerodynamics by solving for a set of dispersions in the aerodynamic database that form a best-fit to the reconstructed aerodynamics. The reconstructed trajectories were compared with flight simulations generated using Program to Optimize Simulated Trajectories II. The results indicate that the simulation underpredicted the vehicle oscillation amplitudes. The reconstruction exhibits a persistent low-amplitude oscillation that does not damp out as predicted.
Terminal Descent Radars (TDR), or landing radars, have been an integral element of Guidance, Navigation and Control (GN\&C) sensor suites of robotic exploration missions to the Moon and Mars. As plans for new, exciting exploration missions to the Moon, Mars and other planetary bodies are being developed, there is a need for a new generation of TDRs that are smaller, consume less power and are less expensive than previous sensors. The challenge of designing such a landing sensor is twofold: the first is to have well-vetted software tools that allow us to explore the design space for a particular mission scenario and analyze performance of relevant radar architectures. The second challenge is to reduce mass and power requirements of a landing radar without compromising reliability and performance. New design approaches that address these challenges need to be tested and demonstrated in realistic Entry-Descent-Landing (EDL)/Deorbit-Descent-Landing (DDL) scenarios. In this paper, we describe a TDR testbed developed at the Jet Propulsion Laboratory. The testbed is a closed-loop design, analysis and verification capability used to design and evaluate the next generation of landing radars for a variety of EDL/DDL scenarios.
Terminal Descent Radars (TDR), or landing radars, have been an integral element of Guidance, Navigation and Control (GN\&C) sensor suites of robotic exploration missions to the Moon and Mars. As plans for new, exciting exploration missions to the Moon, Mars and other planetary bodies are being developed, there is a need for a new generation of TDRs that are smaller, consume less power and are less expensive than previous sensors. The challenge of designing such a landing sensor is twofold: the first is to have well-vetted software tools that allow us to explore the design space for a particular mission scenario and analyze performance of relevant radar architectures. The second challenge is to reduce mass and power requirements of a landing radar without compromising reliability and performance. New design approaches that address these challenges need to be tested and demonstrated in realistic Entry-Descent-Landing (EDL)/Deorbit-Descent-Landing (DDL) scenarios. In this paper, we describe a TDR testbed developed at the Jet Propulsion Laboratory. The testbed is a closed-loop design, analysis and verification capability used to design and evaluate the next generation of landing radars for a variety of EDL/DDL scenarios.
This handbook describes a two-semester senior design course sponsored by the NASA Office of Education, the Exploration Systems Mission Directorate (ESMD), and the NASA Space Grant Consortium. The course was developed and implemented by the Mechanical and Aerospace Engineering Department (MAE) at Utah State University. The course final outcome is a packaged senior design course that can be readily incorporated into the instructional curriculum at universities across the country. The course materials adhere to the standards of the Accreditation Board for Engineering and Technology (ABET), and is constructed to be relevant to key research areas identified by ESMD. The design project challenged students to apply systems engineering concepts to define research and training requirements for a terrestrial-based lunar landing simulator. This project developed a flying prototype for a Lunar or Planetary Surface Landing Research Vehicle (LPSRV). Per NASA specifications the concept accounts for reduced lunar gravity, and allows the terminal stage of lunar descent to be flown either by remote pilot or autonomously. This free-flying platform was designed to be sufficiently-flexible to allow both sensor evaluation and pilot training. This handbook outlines the course materials, describes the systems engineering processes developed to facilitate design fabrication, integration, and testing. This handbook presents sufficient details of the final design configuration to allow an independent group to reproduce the design. The design evolution and details regarding the verification testing used to characterize the system are presented in a separate project final design report. Details of the experimental apparatus used for system characterization may be found in Appendix F, G, and I of that report. A brief summary of the ground testing and systems verification is also included in Appendix A of this report. Details of the flight tests will be documented in a separate flight test report. This flight test report serves as a complement to the course handbook presented here. This project was extremely ambitious, and achieving all of the design and test objectives was a daunting task. The schedule ran slightly longer than a single academic year with the complete design closure not occurring until early April. Integration and verification testing spilled over into late May and the first flight did not occur until mid to late June. The academic year at Utah State University ended on May 8, 2010. Following the end of the academic year, testing and integration was performed by the faculty advisor, paid research assistants, and volunteer student help
A robust trajectory optimization approach for guidance algorithm gain selection for powered descent and landing is developed. This approach uses a genetic algorithm to determine optimal guidance algorithm parameters while incorporating uncertainty information from linear covariance analysis. The optimal guidance algorithm parameters are determined while accounting for environment, navigation, and vehicle property uncertainty and sensor suite fidelity. As a demonstration of this method, the optimal gains for the fractional polynomial powered descent guidance are found for the braking phase of a robotic lunar landing mission. Scenarios with differing sensor suites and sensor qualities are considered, with objective functions to minimize variability in propellant usage or terminal position. Results show that the optimal guidance algorithm gains for a given trajectory differ based on the sensor suite, and optimal guidance algorithm gains may result in up to 20% performance improvements over the baseline in propellant usage and landed accuracy.
Here, the author takes an advocacy position for the proposed Mars Global Network Mission (GNM); it is not intended to be an objective review, although both pros and cons are presented in summary. The mission consists of launches from earth in the '96, '98, and '01 opportunities on Delta-class launch vehicles (approx. 1000 kg injected to Mars in 8 to 10 ft diameter shroud). The trans Mars boost stage injects a stack of small independent, aeroshelled spacecraft. The stack separates from the boost stage and each rigid (as opposed to deployable) aeroshell flies to Mars on its own, performing midcourse maneuvers as necessary. Each spacecraft flies a unique trajectory which is targeted to achieve approach atmospheric interface at the desired latitude and lighting conditions; arrival times may vary by a month or more. A direct entry is performed, there is no propulsive orbit capture. The aeroshelled rough-landers are targeted to achieve a desired attitude and entry flight path angle, and then follow a passive ballistic trajectory until terminal descent. Based on sensed acceleration (integrated to deduce altitude), the aft aeroshell skirt is jettisoned; a short later a supersonic parachute is deployed. The ballistic coefficient of the parachute is sized to achieve terminal velocity at about 8 km. However the parachute is not deployed until a few Km above the surface to minimize wind-induced drift. The nose cap descent imaging begins, a laser altimeter also measures true altitude. Based on range and range rate to the surface, the parachute is jettisoned and the lander uses descent engines to achieve touchdown velocity. A contact sensor shuts down the motors to avoid cratering, and the lander rough-lands at less than 5 m/sec. The remaining aeroshell and a deployable bladder attenuate landing loads and minimize the possibility of tip over. Science instruments are deployed and activated, and the network is established.
The entry operational sequence (OPS 3) begins approximately 2 hours prior to the deorbit maneuver and continues through atmospheric entry, terminal area energy management (TAEM), approach and landing, and rollout. During this flight phase, the navigation state vector is estimated by the Space Shuttle Orbiter onboard navigation system. This estimate is computed using a six-element sequential Kalman filter, which blends inertial measurement unit (IMU) delta-velocity data with external navaid data. The external navaids available to the filter are tactical air navigation (TACAN), barometric altimeter, and microwave scan beam landing system (MSBLS). Attention is given to the functional design of the Orbiter navigation system, the descent navigation sensors and measurement processing, predicted Kalman gains, correlation coefficients, and current flights navigation performance.
LEAVES (Lofted Environmental Atmospheric Venus Sensors) is a design exercise with the goal of dramatically decreasing the cost of obtaining prioritized chemical and physical data in planetary atmospheres. Through the application of a swarm approach this concept parallelizes atmospheric exploration, with geographic coverage far exceeding what is possible with conventional monolithic platforms or sondes. Each unit in the swarm is exceptionally compact, with a powered payload mass of only a few tens of grams and a high-drag, semi-rigid structure that acts to slow each probe as it descends through the atmosphere. This structural design can collapse into a planar form to allow for efficient stowage prior to arrival at the target body. With a total per-unit mass of only 120 g, a fleet of 100 (or more) units can be very reasonably accommodated on a carrier spacecraft.Science operations, which begin when the LEAVES probes reach an altitude of 100 km, are targeted for the cloud-bearing region of Venus' atmosphere. During the roughly 9 hour, terminal velocity descent through the atmosphere, LEAVES collects data of the state and composition of the atmosphere in parallel across multiple units. These data would represent an unprecedented constraint on the distribution and concentration of targeted chemical species, and the detection of local and regional variations in both chemistry and physical properties.A novel and compelling result of this exercise was that the same optimization that produced a structure with an exceptionally low areal mass density (0.126 kg/m2) also resulted in a probe that can be deployed directly from an aerobraking orbit (~140 km at 5 km/s) without the need for aeroshell protection. This translates to a tremendous mass savings and gives LEAVES the flexibility to be carried as a secondary payload aboard either a descending surface probe or an orbital radar mapper. Because such missions are under active development or have already been proposed (but not flown), we infer that LEAVES is well positioned as a technology
In this paper, we provide a design description of the X-33 avionics architecture. The X-33 is an autonomous Single Stage to Orbit (SSTO) launch vehicle currently being developed by Lockheed Martin for NASA as a technology demonstrator for the VentureStar Reusable Launch Vehicle (RLV). The X-33 avionics provides autonomous control of die vehicle throughout takeoff, ascent, descent, approach, landing, rollout, and vehicle safing. During flight the avionics provides communication to the range through uplinked commands and downlinked telemetry. During pre-launch and post-safing activities, the avionics provides interfaces to ground support consoles that perform vehicle flight preparations and maintenance. The X-33 Avionics is a hybrid of centralized and distributed processing elements connected by three dual redundant Mil-Std 1553 data buses. These data buses are controlled by a central processing suite located in the avionics bay and composed of triplex redundant Vehicle Mission Computers (VMCs). The VMCs integrate mission management, guidance, navigation, flight control, subsystem control and redundancy management functions. The vehicle sensors, effectors and subsystems are interfaced directly to the centralized VMCs as remote terminals or through dual redundant Data Interface Units (DIUs). The DIUs are located forward and aft of the avionics bay and provide signal conditioning, health monitoring, low level subsystem control and data interface functions. Each VMC is connected to all three redundant 1553 data buses for monitoring and provides a complete identical data set to the processing algorithms. This enables bus faults to be detected and reconfigured through a voted bus control configuration. Data is also shared between VMCs though a cross channel data link that is implemented in hardware and controlled by AlliedSignal's Fault Tolerant Executive (FTE). The FTE synchronizes processors within the VMC and synchronizes redundant VMCs to each other. The FTE provides an output-voting plane to detect, isolate and contain faults due to internal hardware or software faults and reconfigures the VMCs to accommodate these faults. Critical data in the 1553 messages are scheduled and synchronized to specific processing frames in order to minimize data latency. In order to achieve an open architecture, military and commercial off-the-shelf equipment is incorporated using common processors, standard VME backplanes and chassis, the VxWorks operating system, and MartixX for automatic code generation. The use of off-the-shelf tools and equipment helps reduce development time and enables software reuse. The open architecture allows for technology insertion, while the distributed modular elements allow for expansion to increased redundancy levels to meet the higher reliability goals of future RLVs.
Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.