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NASA NTRS2004

Mars Exploration Rovers navigation results

The twin Mars Exploration Rovers, Spirit and Opportunity, were launched on June 10, 2003, and July 8, 2003, from Cape Canaveral, Florida. Spirit and Opportunity were targeted for landings at Gusev Crater (arrival on January 4, 2004) and Meridiani Planum (arrival on January 25, 2004). The primary navigation challenge was to deliver each spacecraft to the desired atmospheric entry interface point with sufficient accuracy such that each lander would touch down within a specified landing ellipse (about 70 km x 5 km) determined to be safe for landing and also judged to be scientifically interesting. In order to achieve landing within the target ellipse, precise control of the inertial entry flight path angle (FPA) at atmospheric entry was required. The maximum allowable errors in FPA following TCM-5 (trajectory correction maneuver #5) at Entry (E) -2 days were +/-0.12 deg(3 sigma) for Spirit and +/-0.14 deg(3 sigma) for Opportunity. Achieving these entry delivery accuracies necessitated significant improvements to the interplanetary navigation system used for MER. These improvements included new processes and software for orbit determination, propulsive maneuver design, and entry, descent, and landing (EDL) trajectory simulation. The actual achieved atmospheric entry accuracies for Spirit and Opportunity significantly exceeded the requirements. At the navigation data cutoff for the TCM-5 final design, the orbit determination FPA knowledge error was 0.028 deg(3 sigma) for Spirit and 0.035 deg(3 sigma) for Opportunity. Because of exceptionally accurate navigation performance, TCM-5 (E - 2 days) and TCM-6 (E - 4 hours) were canceled for both Spirit and Opportunity. The actual landing locations (determined from in-situ Doppler tracking between the MER rovers and the Mars Odyssey orbiter) differed from the target landing points by 10.1 km (downtrack) for Spirit and 24.6 km (downtrack) for Opportunity. The majority of the landing position offsets for both landers was primarily caused by variations in atmosphere and spacecraft aerodynamic modeling from what was predicted. The amount of the landing position offset caused by navigation-only errors was only 3.3 km (uptrack) for Spirit and 9.7 km (downtrack) for Opportunity.

Mars Exploration Rover (MER)↗
NASA NTRS2004

Mars Exploration Rovers navigation results

The twin Mars Exploration Rovers, Spirit and Opportunity, were launched on June 10, 2003(dagger), and July 8, 2003, from Cape Canaveral, Florida. Spirit and Opportunity were targeted for landings at Gusev Crater (arrival on January 4, 2004) and Meridiani Planum (arrival on January 25, 2004). The primary navigation challenge was to deliver each spacecraft to the desired atmospheric entry interface point with sufficient accuracy such that each lander would touch down within a specified landing ellipse (about 70 km x 5 km) determined to be safe for landing and also judged to be scientifically interesting. In order to achieve landing within the target ellipse, precise control of the inertial entry flight path angle (FPA) at atmospheric entry was required. The maximum allowable errors in FPA following TCM-5 (trajectory correction maneuver #5) at Entry (E) - 2 days were +/-0.12(deg) (3(sigma)) for Spirit and +/-0.14(deg) (3(sigma)) for Opportunity. Achieving these entry delivery accuracies necessitated significant improvements to the interplanetary avigation system used for MER. These improvements included new processes and software for orbit determination, propulsive maneuver design, and entry, descent, and landing (EDL) trajectory simulation. The actual achieved atmospheric entry accuracies for Spirit and Opportunity significantly exceeded the requirements. At the navigation data cutoff for the TCM-5 final design, the orbit determination FPA knowledge error was +/-0.028(deg) (3(sigma) ) for Spirit and +/-0.035(deg) (3(sigma)) for Opportunity. Because of exceptionally accurate navigation performance, TCM-5 (E - 2 days) and TCM-6 (E - 4 hours) were canceled for both Spirit and Opportunity. The actual landing locations (determined from in-situ Doppler tracking between the MER rovers and the Mars Odyssey orbiter) differed from the target landing points by 10.1 km (downtrack) for Spirit and 24.6 km (downtrack) for Opportunity. The majority of the landing position offsets for both landers was primarily caused by variations in atmosphere and spacecraft aerodynamic modeling from what was predicted. The amount of the landing position offset caused by navigation-only errors was only 3.3 km (uptrack) for Spirit and 9.7 km (downtrack) for Opportunity.

Mars Exploration Rover (MER)↗
NASA NTRS1990

Vision-based planetary rover navigation

NASA and JPL have developed a testbed 'planetary rover' vehicle with sufficient power supplies, sensors, and computational resources for the demonstration of semiautonomous navigation. Attention is presently given to this vehicle's vision-based navigation techniques. The proposed design and its variants allow advantage to be taken of enormous quantities of both spatial and temporal information that are normally wasted, by sampling very fine detail over the full focal plane area to precisely determine those parts of the image that are accurately at the focus range of the pinhole array used. This should generate accurate and reliable real-time range information in a wide variety of natural scenes, with little or no computation.

Wilcox, Brian H.↗
NASA NTRS2021

Machine Learning Based Path Planning for Improved Rover Navigation

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Yue, Yisong↗
NASA NTRS2024

Surface Gravimetry Using Rover Navigation Systems

This prototype seeks to demonstrate the utility of repurposing a Micro-ElectroMechancical (MEMS) Inertial measurement Unit (IMU) to perform surface gravimetry on a rover. Gravimetry is a common analytical tool used for probing density distributions in the subsurface of a planetary body. Historically, extraterrestrial gravimetry has been confined to orbital platforms. While orbital surveys allow for the construction of global gravity models, the spatial resolution of the data is constrained by the platform’s orbital altitude and high inherent speed. Data collected at or near the surface would increase spatial resolution and allow finer-scale crustal structure to be resolved. To date, there have been only two extraterrestrial surface gravity surveys: the Apollo 17 Traverse Gravimeter Experiment and a survey using the MEMS accelerometers contained within the Curiosity rover’s IMUs. The Curiosity survey highlighted the potential of using MEMS technology to perform planetary gravimetry, albeit with lower sensitivities than traditional surface gravimeters. MEMS accelerometers are included on every rover platform as part of the IMU navigation systems. MEMS accelerometers have low mass, cost, and power requirements while being robust across a range of environments, whereas traditional gravimeters are fragile, costly, and relatively massive (≥8kg versus ≈50g for MEMS IMUs). Thus, the emergence of MEMS gravimeters provides a low-risk and cost-effective method for performing planetary surface gravimetry [3,4]. Here, we present a method to recalibrate the MEMS accelerometers in rover IMUs to collect gravimetric measurements. Such measurements could assist current and future rover missions and support wider efforts to mature MEMS gravimeters.

C S Lawson↗
NASA NTRS2012

Tele-Operated Lunar Rover Navigation Using Lidar

Near real-time tele-operated driving on the lunar surface remains constrained by bandwidth and signal latency despite the Moon s relative proximity. As part of our work within NASA s Human-Robotic Systems Project (HRS), we have developed a stand-alone modular LIDAR based safeguarded tele-operation system of hardware, middleware, navigation software and user interface. The system has been installed and tested on two distinct NASA rovers-JSC s Centaur2 lunar rover prototype and ARC s KRex research rover- and tested over several kilometers of tele-operated driving at average sustained speeds of 0.15 - 0.25 m/s around rocks, slopes and simulated lunar craters using a deliberately constrained telemetry link. The navigation system builds onboard terrain and hazard maps, returning highest priority sections to the off-board operator as permitted by bandwidth availability. It also analyzes hazard maps onboard and can stop the vehicle prior to contacting hazards. It is robust to severe pose errors and uses a novel scan alignment algorithm to compensate for attitude and elevation errors.

Pedersen, Liam↗
NASA NTRS2022

Multi-Mission Terrain Classifier for Safe Rover Navigation and Automated Science

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.

Ono, Masahiro↗
NASA NTRS1998

Mars Rover Navigation Results Using Sun Sensor Heading Determination

Upcoming missions to the surface of Mars will use mobile robots to traverse long distances from the landing site. To prepare for these missions, the prototype rover, Rocky 7, has been tested in desert field trials conducted with a team of planetary scientists. While several new capabilities have been demonstrated, foremost among these was sun-sensor based traversal of natural terrain totaling a distance of one kilometer. This paper describes navigation results obtained in the field tests, where cross-track error was only 6% of distance traveled. Comparison with previous results of other planetary rover systems shows this to be a significant improvement.

Volpe, Richard↗
NASA NTRS2015

Challenges of Rover Navigation at the Lunar Poles

Observations from Lunar Prospector, LCROSS, Lunar Reconnaissance Orbiter (LRO), and other missions have contributed evidence that water and other volatiles exist at the lunar poles in permanently shadowed regions. Combining a surface rover and a volatile prospecting and analysis payload would enable the detection and characterization of volatiles in terms of nature, abundance, and distribution. This knowledge could have impact on planetary science, in-situ resource utilization, and human exploration of space. While Lunar equatorial regions of the Moon have been explored by manned (Apollo) and robotic missions (Lunokhod, Cheng'e), no surface mission has reached the lunar poles.

Robotics↗
NASA NTRS1993

Lunar rover navigation concepts

With regard to the navigation of mobile lunar vehicles on the surface, candidate techniques are reviewed and progress of simulations and experiments made up to now are described. Progress that can be made through precursor investigations on Earth is considered. In the early seventies the problem was examined in a series of relevant tests made in the California desert. Meanwhile, Apollo rovers made short exploratory sorties and robotic Lunokhods traveled over modest distances on the Moon. In these early missions some of the required methods were demonstrated. The navigation problem for a lunar traverse can be viewed in three parts: to determine the starting point with enough accuracy to enable the desired mission; to determine the event sequence required to reach the site of each traverse objective; and to redetermine actual positions enroute. The navigator's first tool is a map made from overhead imagery. The Moon was almost completely photographed at moderate resolution by spacecraft launched in the sixties, but that data set provides imprecise topographic and selenodetic information. Therefore, more advanced orbital missions are now proposed as part of a resumed lunar exploration program. With the mapping coverage expected from such orbiters, it will be possible to use a combination of visual landmark navigation and external radio and optical references (Earth and Sun) to achieve accurate surface navigation almost everywhere on the near side of the Moon. On the far side and in permanently dark polar areas, there are interesting exploration targets where additional techniques will have to be used.

Burke, James D.↗
NASA NTRS2007

Terrain Adaptive Navigation for Mars Rovers

A navigation system for Mars rovers in very rough terrain has been designed, implemented, and tested on a research rover in Mars analog terrain. This navigation system consists of several technologies that are integrated to increase the capabilities compared to current rover navigation algorithms. These technologies include: goodness maps and terrain triage, terrain classification, remote slip prediction, path planning, high-fidelity traversability analysis (HFTA), and slip-compensated path following. The focus of this paper is not on the component technologies, but rather on the integration of these components. Results from the onboard integration of several of the key technologies described here are shown. Additionally, the results from independent demonstrations of several of these technologies are shown. Future work will include the demonstration of the entire integrated system described here.

traversabiity analysis↗