Engineering Papers⌕ Search

Engineering topics

Rankin, Arturo

Publications and source records attributed to Rankin, Arturo.

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

Assessing Mars Curiosity Rover Wheel Damage

An alarming rate of wheel skin cracks were first observed on the Mars Science Laboratory (MSL) Curiosity rover about 14 months after the start of its surface mission. Nine years later (as of August 2, 2021), Curiosity has four broken wheelgrousers, three on the left middle wheel and one on the right middle wheel. There are a substantial number of wheel skin cracks on the left front, left middle, and right middle wheels such that the number of grousers on each that are consideredat risk of breaking are 10, 7, and 11, respectively. Although the current level of wheel damage does not significantly limit Curiosity’s mission objectives, a higher damage rate could impact surface operations, so the damage rate is closely monitored. OnNovember 30, 2013, the MSL Surface Operations team began periodically acquiring images of Curiosity’s wheels. In this paper, we describe the process the Mobility Operations team uses to assess wheel damage, the current state of Curiosity’swheels, and how the wheel damage assessments have influenced driving guidelines and wheel imaging frequency.

Rink, Kimberly↗
NASA NTRS2022

Visual Odometry Thinking While Driving for the Curiosity Mars Rover's Three-Year Test Campaign: Impact of Evolving Constraints on Verification and Validation

Over the first 9 years of the Mars Science Laboratory (MSL) Curiosity rover's surface mission, more than 87\% of its driving has been performed using Visual Odometry (VO). The benefits of using VO during driving are that it minimizes rover position uncertainty and can be used to monitor wheel slip, halting a drive if excessive wheel slip is occurring. The VO implementation on board Curiosity acquires and processes VO images in between drive steps while the rover is stationary. A VO Thinking While Driving (VTWD) flight software capability has been developed which enables the processing of VO images during rover driving, increasing the distance Curiosity can drive with VO in a given time period up to as much as 1.75x total distance. Verification and Validation (V\&V) of the capability has been challenging due to impacts from the COVID-19 pandemic and unavailability of the JPL Mars Yard outdoor test site. The VTWD V\&V test procedures were modified to use a small indoor space with Mars-like terrain. This paper describes the 3 year V\&V effort under challenging conditions to approve the VTWD capability for use on the Curiosity rover.

Rankin, Arturo↗
NASA NTRS2021

Integration of an Arm Kinematics Hot Patch onboard the Curiosity Rover

NASA's Mars Science Laboratory (MSL) mission has updated the Curiosity rover's flight software multiple times since landing on Mars on August 6, 2012. The most common patching method has been a hot patch, in which running flight software is modified after being copied into RAM from its persistent storage. The latest hot patch to be installed on Curiosity fixed an issue in the robotic arm software that computes generalized inverse kinematics. Additional unit testing performed since the start of the surface mission revealed that this software can sometimes produce erroneous solutions.The cause was identified as numerical instability in a quartic root finder. When the inputs to that solver are not well conditioned, floating-point numerical issuescan cause erroneous roots to be reported. In theory, this could result in the robotic arm turret instruments being commanded to unintended positions, for example, below the terrain surface. Out of approximately 3.7 million unit test cases, 97.2\% of the position errors were below 5 mm. However, there were 16 test cases where theposition error was greater than 20 cm, and the maximum position error was 1.2 meters.The patch was uploaded to Curiosity on sol 2642 (January 11, 2020) after the solution was developed, re-implemented as a hot patch, and validated and verified using Earth-based Curiosity testbeds. A checkout test of the patch was performed on Curiosity on sol 2657, and nominal use of the patch began on sol 2658. In this paper, we describe the steps that led to integrating the arm kinematic hot patch into Curiosity's flight software, from the discovery of the bug to the nominal use of the patch in flight.

Maimone, Mark↗
NASA NTRS2020

Rimmed Wheel Performance on the Mars Science Laboratory Scarecrow Rover

The Mars Science Laboratory (MSL) Curiosity rover experienced increasing wheel damage beginning in October 2013. While the wheels were designed to operate with considerable damage, the rate at which damage was occurring was unexpected and raised concerns regarding wheel life expectancy. As of Sol 2555 (10-14-19), there are two broken grousers on the left middle wheel, and one broken grouser on the right middle wheel. One possible scenario, albeit remote, is that enough grousers break on a wheel such that unconstrained portions of the wheel could contact the cable running from the rover motor controller assembly to the wheel's drive actuator. If the cable to a drive actuator is damaged, that wheel may no longer respond to commands. To make progress towards a navigation goal position, that wheel would need to be dragged. To mitigate the risk of damaging a cable running to a wheel’s drive actuator, the unconstrained portion of a wheel could be strategically shed by performing driving maneuvers on an immovable rock. What would remain after wheel shedding is a rimmed wheel (the outer 1/3 of the wheel). We studied the feasibility of remotely commanding the rover to perform the shed maneuver on one of its front wheels. To inform whether or not to shed the wheels, we tested the performance of driving on one or more rimmed wheels in flight. This led to a two-month test campaign in the Jet Propulsion Laboratory (JPL) Mars Yard using the Scarecrow testbed rover. Driving and steering performance was characterized on a variety of terrain types and slopes in a worst-case rimmed wheeled configuration. Test results indicate that if wheel shedding could be successfully executed in flight, Curiosity could continue to drive indefinitely on rimmed wheels.

Graser, Evan↗
NASA NTRS2020

Driving Curiosity: Mars Rover Mobility Trends During the First Seven Years

NASA’s Mars Science Laboratory (MSL) mission landed the Curiosity rover on Mars on August 6, 2012. As of August 6, 2019 (sol 2488), Curiosity has driven 21,318.5 meters over a variety of terrain types and slopes, employing multiple drive modes with varying amounts of onboard autonomy. Curiosity’s drive distances each sol have ranged from its shortest drive of 2.6 centimeters to its longest drive of 142.5 meters, with an average drive distance of 28.9 meters. Real-time human intervention during Curiosity drives on Mars is not possible due to the latency in uplinking commands and downlinking telemetry, so the operations team relies on the rover’s flight software to prevent an unsafe state during driving. Over the first seven years of the mission, Curiosity has attempted 738 drives. While 622 drives have completed successfully, 116 drives were prevented or stopped early by the rover’s fault protection software. The primary risks to mobility success have been wheel wear, wheel entrapment, progressive wheel sinkage (which can lead to rover embedding), and terrain interactions or hardware or cabling failures that result in an inability to command one or more steer or drive actuators. In this paper, we describe mobility trends over the first 21.3km of the mission, operational aspects of the mobility fault protection, and risk mitigation strategies that will support continued mobility success for the remainder of the mission.

Rankin, Arturo↗
NASA NTRS2018

Traction Control Design and Integration Onboard the Mars Science Laboratory Curiosity Rover

The Mars Science Laboratory (MSL) Curiosity rover experienced increasing wheel damage beginning in October 2013. While the wheels were designed to operate with considerable damage, the rate at which damage was occurring was unexpected and raised concerns regarding wheel lifetime. The Jet Propulsion Laboratory (JPL) has now developed and deployed new software on Curiosity that reduces the forces acting on the wheels. Our new Traction Control algorithm adapts each wheel’s speed to fit the terrain it drives over. It does not rely on any a priori knowledge of the terrain, and instead leverages the rover’s measured attitude rates and suspension angles, together with a rigid-body kinematics model, to estimate the real-time wheel-terrain contact angles and ideal, no-slip wheel angular rates. In addition, free-floating “wheelies” are detected and autonomously corrected. In this paper, we describe the algorithm, its ground testing campaign and associated challenges, and finally its validation and performance in flight. Ground test data demonstrates reductions in the forces acting on the wheels and validates the wheelie-damping capability. Secondary benefits in some terrains include a reduction in heading deviations while climbing rocks, with a reduction in slip in certain sandy terrains. Preliminary validation from flight data confirms these findings.

Maimone, Mark↗
NASA NTRS2017

Systems and Methods for Automated Vessel Navigation Using Sea State Prediction

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

Huntsberger, Terrance L.↗
NASA NTRS2015

Systems and Methods for Automated Vessel Navigation Using Sea State Prediction

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

Huntsberger, Terrance L.↗
NASA NTRS2011

Daytime Water Detection Based on Sky Reflections

A water body s surface can be modeled as a horizontal mirror. Water detection based on sky reflections and color variation are complementary. A reflection coefficient model suggests sky reflections dominate the color of water at ranges > 12 meters. Water detection based on sky reflections: (1) geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground (2) predicts if the ground pixel is water based on color similarity and local terrain features. Water detection has been integrated on XUVs.

water detections↗
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

TIR cameras can be used for day/night Unmanned Ground Vehicle (UGV) autonomous navigation when stealth is required. The quality of uncooled TIR cameras has significantly improved over the last decade, making them a viable option at low speed Limiting factors for stereo ranging with uncooled LWIR cameras are image blur and low texture scenes TIR perception capabilities JPL has explored includes: (1) single and dual band TIR terrain classification (2) obstacle detection (pedestrian, vehicle, tree trunks, ditches, and water) (3) perception thru obscurants

calibration↗
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.

terrain classification↗
NASA NTRS2010

Evaluating the Performance of Unmanned Ground Vehicle Water Detection

Water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation over cross-country terrain. During the Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) developed a set of water detection algorithms that are used to detect, localize, and avoid water bodies large enough to be a hazard to a UGV. The JPL water detection software performs the detection and localization stages using a forward-looking stereo pair of color cameras. The 3D coordinates of water body surface points are then output to a UGV's autonomous mobility system, which is responsible for planning and executing safe paths. There are three primary methods for evaluating the performance of the water detection software. Evaluations can be performed in image space on the intermediate detection product, in map space on the final localized product, or during autonomous navigation to characterize the avoidance of a variety of water bodies. This paper describes a methodology for performing the first two types of water detection performance evaluations.

stereo vision↗