The Mars Science Laboratory (MSL) Curiosity rover has driven over 29 kilometers since landing on Mars in August 2012, as of sol 3600 (September 2022). MSL Rover Planners use a variety of different driving commands and sequencing strategies to reach science targets of interest in diverse terrain. However, 490 Martian solar days (sols) and 4600 meters into the mission, the wheels began to exhibit significant wear, manifesting as cracks and breaks in their aluminum-alloy construction. Hardware mitigation was not possible due to the location of the vehicle; instead, engineers applied software techniques to extend the lifetime of the wheels. This initially led to the development of new Terrain-adaptive Wheel Speed Control software for the rover, which has proven helpful and now has been in use continuously since sol 1646 (April 2017). But some inefficiencies remained. When performing a precision approach to a goal location, the standard driving strategy alternates arcs (specified using a small number of fixed, preselected curvatures, running in open-loop motion) with closed-loop turns in place. But turn-in-place commands were found to exacerbate wheel wear rates due to their slower drive speeds and additional steering. So when a new flight software (FSW) release called R13 became possible, the project decided to implement two new commands that will help reduce unnecessary rover motion. These new mobility commands are now part of MSL's next major FSW release and offer new commanding strategies that should reduce unnecessary wheel wear and allow rover planners to generate more flexible drive paths. While similar in name to existing arcing commands, these two commands have completely new behaviors that allow the rover to maneuver in ways never possible before: dynamically choosing the precise arc curvature appropriate for the precision goal location, and using the gyro-measured heading change to terminate motion. This update removes the prior restrictions on which specific curvatures can be used, letting the rover choose whatever is appropriate given its current pose relative to the current goal. These two commands can potentially replace the turn-in-place maneuvers currently used to reach a specific goal, a known significant contributor to wheel damage in terrains with embedded rocks. The new commands were exercised during algorithm development both in simulation and also on MSL's Vehicle Systems Testbed (VSTB), an engineering model of Curiosity typically deployed in JPL's Mars Yard, an outdoor test area with a variety of terrains and slopes. Once development was complete, the VSTB was also used to conduct a formal Verification and Validation (V&V) test campaign over 5 days in the Mars Yard, testing the commands to their limits to ensure readiness for Mars. During this test campaign, the expected behavior of the commands was confirmed which led to approval for inclusion in the R13 FSW update after a review of the V&V results by a board of Mission Subject Matter Experts. We anticipate these commands will be deployed on Curiosity as part of the R13 update expected in early 2023.

The Cold Operable Lunar Deployable Arm (COLDArm project is developing an experimental robotic arm with the goal of demonstrating cold-operable actuators, sensors, and motor controllers in a lunar technology demonstration, saving energy and mass over conventionally heated systems. To support this goal, the JPL engineering team developed new flight software and avionics to demonstrate key functional manipulation capabilities. Drawing on the successful Ingenuity Mars Helicopter, significant avionics and software elements were leveraged as a starting point and modified to suit the needs of COLDArm. The COLDArm flight software uses the F Prime framework to best leverage heritage software from Ingenuity and deliver proven command and data handling capabilities. Robotics math libraries were leveraged from the Mars 2020 mission and adapted to support manipulator kinematics, collision detection, and body load propagation. A new COLDArm deployment was architected and additional components and libraries were developed to drive custom power and sensing avionics and to deliver an onboard robotic system model, capable of performing arm motions and fault protection. The COLDArm flight software runs on the RASK (Robotic Avionics and Sensor Kit) assembly, which builds upon the versatile and resilient Ingenuity avionics. The RASK avionics address COLDArm-specific requirements by adding new functionality such as a force-torque sensor interface and a 4K-resolution stereo camera pair. Like Ingenuity, the RASK avionics make extensive use of commercial-grade electronics components, which were determined to be sufficiently robust to radiation effects given the COLDArm mission profile. The COLDArm thermal environment is a key challenge, requiring the RASK design to incorporate several thermal enhancements at the schematic, layout, and enclosure design levels. This paper provides an overview of the RASK software and avionics and describes how they were adapted from Ingenuity for COLDArm and tested to ensure project requirements were met. This work contributes novel technology to the field of space robotic arm manipulation that may serve as a basis for future flight opportunities aligned with NASA scientific exploration objectives.

We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars. MLNav makes judicious use of machine learning to enhance the efficiency of path planning while fully respecting safety constraints. In particular, the dominant computational cost in such safety-critical settings is running a model-based safety checker on the proposed paths. Our learned search heuristic can simultaneously predict the feasibility for all path options in a single run, and the model-based safety checker is only invoked on the top-scoring paths. We validate in high-fidelity simulations using both real Martian terrain data collected by the Perseverance rover, as well as a suite of challenging synthetic terrains. Our experiments show that: (i) compared to the baseline ENav path planner on board the Perserverance rover, MLNav can provide a significant improvement in multiple key metrics, such as a 10x reduction in collision checks when navigating real Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav can successfully navigate highly challenging terrains where the baseline ENav fails to find a feasible path before timing out.

The Mars 2020 (M2020) Perseverance Rover is NASA's most advanced planetary rover mission to date. It includes a novel Sample Caching Subsystem (SCS) which will collect rock cores for possible future return to Earth, as well as an improved mobility system with enhanced autonomous navigation which will enable it to traverse faster and farther than prior rovers. The development of both systems required extensive flight software and flight hardware testing. To support this testing, we developed the Surface System Development Environment (SSDEV) and used it for a wide variety of testing. SSDEV is a bundled subset of M2020 Flight Software which runs on commercially available Linux computers and can be combined with multiple backend options for simulation and hardware control. The SSDEV architecture enabled our teams to perform much more testing of flight software and flight hardware than would have otherwise been possible. As a secondary benefit, the SSDEV-based test campaigns also helped our teams enter the operations phase of the mission with greater readiness of operations products and tools. In this paper, we summarize the motivation for SSDEV, provide an overview of the SSDEV architecture, list several examples of how SSDEV was used, and summarize lessons learned. SSDEV is not a substitute for integrated testing with flight-like avionics, but it enabled substantially more testing than would have otherwise been possible and also provided some unique benefits. We recommend architectures like SSDEV to future projects that need to perform extensive hardware and software testing using a limited set of flight-like avionics.

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.

A prototype rover carrying an astrobiology payload was developed and deployed at analog field sites to mature generalized system architectures capable of searching for biosignatures in extreme terrain across the Solar System. Specifically, the four-legged Limbed Excursion Mechanical Utility Robot (LEMUR) 3 climbing robot with microspine grippers carried three instruments: A micro-X-ray fluorescence instrument based on the Mars 2020 mission's Planetary Instrument for X-ray Lithochemistry provided elemental chemistry; a deep-ultraviolet fluorescence instrument based in Mars 2020s Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals mapped organics in bacterial communities on opaque substrates; and a near-infrared acousto-optic tunable filter-based point spectrometer identified minerals and organics in the 1.6-3.6 mu m range. The rover also carried a light detection and ranging and a color camera for both science and navigation. Combined, this payload detects astrobiologically important classes of rock components (elements, minerals, and organics) in extreme terrain, which, as demonstrated in this work, can reveal a correlation between textural biosignatures and the organics or elements expected to preserve them in a habitable environment. Across >10 field tests, milestones were achieved in instrument operations, autonomous mobility in extreme terrain, and system integration that can inform future planetary science mission architectures. Contributions include (1) system-level demonstration of mock missions to the vertical exposures of Mars lava tube caves and Mars canyon walls, (2) demonstration of multi-instrument integration into a confocal arrangement with surface scanning capabilities, and (3) demonstration of automated focus stacking algorithms for improved signal-to-noise ratios and reduced operation time.

Potential future Mars Sample Return (MSR) missions could collect planetary samples and launch them into Mars orbit; in a follow-on mission, a spacecraft could rendezvous with the orbital sample (OS) to return the samples to Earth. Due to planetary protection requirements and the need to position the OS in a preferred orientation for Earth re-entry, the rendezvous phase would present a number of technical challenges. To address these challenges, this paper presents a new end-to-end testbed elements demonstrating new technologies for 1. capture of the OS within the spacecraft, 2. orientation of the OS, and 3. stowage of the OS to a Primary Containment Vessel (PCV) and internal transfer of the PCV within the spacecraft to an Earth Return Module (ERM). The end-to-end testbed consists of a 3 DOF planar robotic arm, a capture cone volume, two interchangeable orientation mechanisms, and two interchangeable internal transfer mechanisms. To simulate zero gravity during the capture stage, a cyber-physical approach is used here that fuses simulation, hardware, and autonomy elements. During the capture stage, contact dynamics of the OS with the capture cone and robotic arm end-effector is simulated using high-fidelity multibody dynamics simulation software in-the-loop. The output of the simulation is used to control the state of the physical OS in real-time using a 3 DOF robotic gantry. Additionally, the end-effector of the robotic arm is equipped with a force-torque sensor and camera to detect contact and track the OS. In the second stage, two novel mechanisms demonstrate successful orientation of the OS. In the first orientation mechanism, wipers sweep the surface of a spherical OS to engage a positive feature, thereby manipulating the OS into a preferential orientation. In the second orientation mechanism, two sets of cups selectively engage and rotate a spherical OS about two orthogonal axes; the mechanism can be operated autonomously using computer vision or interactively with a human operator in-the-loop. Finally, in the third stage, two novel mechanisms demonstrate successful internal transfer of the OS within the spacecraft volume. The internal transfer motion requires 3 DOF (rotation, translation and release of the OS). In the first internal transfer mechanism, each DOF is independently controlled using three actuators. In the second internal transfer mechanism, the 3DOF are coupled mechanically using a single actuator.

Grasping and manipulating uncooperative objects in space is an emerging challenge for robotic systems. Many traditional robotic grasping techniques used on Earth are infeasible in space. Vacuum grippers require an atmosphere, sticky attachments fail in the harsh environment of space, and handlike opposed grippers are not suited for large, smooth space debris. We present a robotic gripper that can gently grasp, manipulate, and release both flat and curved uncooperative objects as large as a meter in diameter while in microgravity. This is enabled by (i) space-qualified gecko-inspired dry adhesives that are selectively turned on and off by the application of shear forces, (ii) a load-sharing system that scales small patches of these adhesives to large areas, and (iii) a nonlinear passive wrist that is stiff during manipulation yet compliant when overloaded. We also introduce and experimentally verify a model for determining the force and moment limits of such an adhesive system. Tests in microgravity show that robotic grippers based on dry adhesion are a viable option for eliminating space debris in low Earth orbit and for enhancing missions in space.

Potential Mars Sample Return (MSR) would need a robotic autonomous Orbital Sample (OS) capture and manipulation toward returning the samples to Earth. The OS would be in Martian orbit where a sample capture orbiter could find it and rendezvous with it. The orbiter would capture the OS, manipulate it to a preferential orientation for the samples, transition it through steps required to break-the-chain with Mars, stowing it in a containment vessel or an Earth Entry Vehicle and providing a redundant containment to the OS (e.g., by closing and sealing the lid of the EEV). In this paper, we discuss component technologies developed for in-laboratory evaluation and maturation of concepts toward the robotic capture and manipulation of an Orbital Sample. We discuss techniques for simulating 0-g dynamics of a spherical OS, including contact, in a laboratory setting. In this, we leverage a 5dof gantry system and, alternately, a 6dof KUKA robotic arm to simulate the OS motion. Both the gantry and robotic arm are mounted with a force-torque sensor that enable detection of contact and provide measurements to simulate, in hardware, the 0-g OS dynamics. We present results that demonstrate the validity of our approach and the extent to which we are able to simulate 0-g dynamics in a laboratory setting. We also discuss techniques for detecting and tracking the OS using optical sensors and LIDAR from near-capture distances. These are discussed in the context of individual sensors as well as fusion of multiple sensor readings. Results of hardware experiments with different sensors are presented. Further, we discuss an uncertainty quantification based physics modeling capability for quantitative evaluation of different concepts for OS capture and manipulation. The computational models are based on high-fidelity multibody dynamics simulations of the OS, robotic elements and their contact mechanics. We present results that demonstrate our effective use of computational simulations in a complementary manner to hardware experiments. Finally, we present a cyber-physical approach to concurrently fusing hardware elements, computational simulation elements and autonomy software to effectively and rapidly simulate end-to-end systems concepts for end-to-end orbital sample capture and manipulation system concepts.

This paper introduces a new four-limbed robot, LEMUR 3, that has demonstrated climbing on cliff faces and smooth glass. Each limb on the robot consists of seven identical actuators in a serial chain. Each limb terminates in a single axis force sensor that allows various end effectors to be mounted and connected to the robot's power and communication system. Microspine grippers were used for climbing the rocky surface and gecko adhesive grippers were used for the glass solar panels. All other hardware and much of the software was common for the two demonstrations. The robot's mechanical, electrical, and software systems, various gripping devices, and field demonstrations are described. Limbed mobility is of interest to JPL and NASA because of its potential to access extreme terrain, including that on Mars and in microgravity environments.