Planetary Rovers Research Articles

Enabling Faster Locomotion of Planetary Rovers With a Mechanically-Hybrid Suspension

Enabling Faster Locomotion of Planetary Rovers With a Mechanically-Hybrid Suspension

Human-in-the-Loop Assisted Trafficability Prediction for Planetary Rover on Soft Dangerous Terrains

Human-in-the-Loop Assisted Trafficability Prediction for Planetary Rover on Soft Dangerous Terrains

Analysis of Cation Composition in Dolomites on the Intact Particles Sampled from Asteroid Ryugu.

Characterization of the elemental distribution of samples with rough surfaces has been strongly desired for the analysis of various natural and artificial materials. Particularly for pristine and rare analytes with micrometer sizes embedded on specimen surfaces, non-invasive and matrix effect-free analysis is required without surface polishing treatment. To satisfy these requirements, we proposed a new method employing the sequential combination of two imaging modalities, i.e., microenergy-dispersive X-ray fluorescence (micro-XRF) and Raman micro-spectroscopy. The applicability of the developed method is tested by the quantitative analysis of cation composition in micrometer-sized carbonate grains on the surfaces of intact particles sampled directly from the asteroid Ryugu. The first step of micro-XRF imaging enabled a quick search for the sparsely scattered and micrometer-sized carbonates by the codistributions of Ca2+ and Mn2+ on the Mg2+- and Fe2+-rich phyllosilicate matrix. The following step of Raman micro-spectroscopy probed the carbonate grains and analyzed their cation composition (Ca2+, Mg2+, and Fe2+ + Mn2+) in a matrix effect-free manner via the systematic Raman shifts of the lattice modes. The carbonates were basically assigned to ferroan dolomite bearing a considerable amount of Fe2+ + Mn2+ at around 10 atom %. These results are in good accordance with the assignments reported by scanning electron microscopy-energy-dispersive X-ray spectroscopy, where the thin-sectioned and surface-polished Ryugu particles were applicable. The proposed method requires neither sectioning nor surface polishing; hence, it can be applied to the remote sensing apparatus on spacecrafts and planetary rovers. Furthermore, the non-invasive and matrix effect-free characterization will provide a reliable analytical tool for quantitative analysis of the elemental distribution on the samples with surface roughness and chemical heterogeneity at a micrometer scale, such as art paintings, traditional crafts with decorated shapes, as well as sands and rocks with complex morphologies in nature.

Vibration-Based Recognition of Wheel-Terrain Interaction for Terramechanics Model Selection and Terrain Parameter Identification for Lugged-Wheel Planetary Rovers.

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel-terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel-terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel-terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel-terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.

DEM simulation and continuation algorithm of granular physical field for planetary wheel-terrain interaction

Terramechanics models are important in the research and development of planetary rovers. However, current models emphasize the motion of wheels and bulk soil parameters, with limited investigation of movement of soil particles. In this study, discrete element methods (DEM) were used to gain in-depth insights into wheel-terrain interaction from granular perspective. Experiments and DEM simulations of single-wheel tests were performed under fixed sinkage conditions to examine the behavior of wheel and granular medium. A convex hull-based continuation method for the granular physical field was proposed, including velocity, bulk density, and pressure fields. This method demonstrated higher efficacy in handling the physical fields of granular medium with boundaries. The distribution characteristics of the physical field of the granular medium for different slip ratios were analyzed to elucidate the reasons for alterations in the drawbar pull. The physical fields provide an analytical basis for understanding wheel-terrain interactions from a soil viewpoint.

HORIZON DETECTION using Machine Learning

This project focuses on utilizing machine learning techniques for horizon detection, with applications ranging from planetary rover localization to flight control and port security. Traditional methods like GPS are unavailable in certain scenarios, making horizon detection a critical visual cue for precise position and orientation estimation. The proposed approach involves Canny edge detection, followed by the classification of edges as either "horizon" or "non-horizon" using a Support Vector Machine (SVM) trained on ground truth data and SIFT (Scale-Invariant Feature Transform) features. When applied to new images, this process identifies a consistent horizon line, offering a robust and adaptable solution for addressing \complex real-world challenges.

TTA-GEP: Terrain Traversability Analysis with Geometry and Environmental Perception for the Path Planning of Planetary Rovers

Terrain traversability analysis (TTA), the key to the navigation of planetary rovers, is significant to the safety of the rover. Therefore, owing to its complexity, the Martian terrain is worth analysing comprehensively based on the terrain variability and hazard level. In this work, we propose a novel method for terrain traversability analysis for the path planning of planetary rovers by integrating Martian terrain geometry features with terrain semantic information, which includes geometry and environmental perception (GEP). Specifically, we deploy semantic segmentation to classify common terrain types, such as rocks, bedrocks, and sand, obtaining semantic information as one part of terrain traversability analysis at the same time. Simultaneously, the point cloud is generated by using binocular images from the planetary rover navigation camera (Navcam) to construct a 2.5D elevation map of the environment to analyse the geometric characteristics of the terrain. Besides, we implement path planning based on the results of TTA-GEP. Overall, our proposed method improves the performance of the terrain traversability analysis and reduces the risk of planetary rovers while detecting in an unstructured environment.

Reinforcement learning-based stable jump control method for asteroid-exploration quadruped robots

Unlike the spherical gravitational field of planets and other large solar system bodies, the gravitational field of asteroids is irregular and weak. It is challenging for a planetary rover to obtain sufficient traction forces in this environment. However, this gravitational environment is suitable for legged robots with jumping ability, but it also imposes higher demands on control methods. Therefore, this study aimed to address the problem of jump control method for asteroid-exploration quadruped robots. As the robot jumps off the surface of an asteroid, it would fly for a certain amount of time because of the low gravitational acceleration. The prolonged flight phase underscores the significance of the robot's take off and attitude control. A model-free stable jumping control method was devised in this study. This method can satisfy the control requirements for takeoff, attitude adjustment, and soft landing by using end-to-end multi-agent reinforcement learning (MARL). MARL is more advantageous than single-agent reinforcement learning in dealing with composite motion control problems under similar observation conditions. A simulated training environment was established, incorporating models of the gravitational field, task partitioning for jumping, and design of reward functions, including jump trajectory planning. The efficacy of the proposed jump control method for a quadruped robot was successfully demonstrated in the gravitational environment of an irregular rod-shaped asteroid, 216 Kleopatra.

Evidence of shallow basaltic lava layers in Von Kármán crater from Yutu-2 Lunar Penetrating Radar

Yutu-2 – the rover from the Chang’E-4 mission – is the longest operational Lunar rover, and the first rover to land on the far side of the Moon. It is the second planetary rover to be equipped with ground-penetrating radar (GPR), one of the few in-situ geophysical techniques used in planetary exploration. Since landing in 2019, Yutu-2 has travelled more than 1 kilometre in the Von Kármán (VK) crater, and has been able to investigate the dielectric properties of the shallow Lunar ejecta using its on-board high frequency GPR channels. In this paper, we use advanced signal processing and frequency attributes to infer a detailed dielectric structure of the first ≈30 metres of the subsurface, providing valuable information on the ilmenite content of the landing site. Both the dielectric properties and the ilmenite content suggest a shallow sequence of Imbrian basaltic layers overlaying a low-ilmenite ejecta blanket.

Vision-Based Virtual Simulation Platform for Planetary Rovers

In order to evaluate the feasibility of planetary exploration missions, it is imperative to construct planetary terrain environments on the ground. Nevertheless, the implementation of this method is characterized by time-consuming and challenging factors. By contrast, resorting to simulation approaches represents a cost-effective and high-efficiency alternative, which can facilitate the simulation validation of planetary rover exploration missions in an effective manner. In this paper, a modular planet rover simulation platform is proposed; by building and overlaying multiple feature layers corresponding to the surface of the planet, we realize the simulation of high-resolution fine terrain and through adjusting the terrain parameters to meet the needs of different simulated terrain. The experimental results show that we have built a scene to satisfy the requirements of visual effects and physically realistic characteristics of simulation. Using an improved PatchMatch stereomethod to recover images captured real time by a planetary rover navigation camera in a virtual environment provided more complete three-dimensional terrain data for subsequent simulation validation of local path planning. Finally, a simulation environment that combines high-fidelity visual effects and kinematic characteristics supports visualizing the simulation platform: we propose a path planning method using global planning combined with local obstacle avoidance, and we obtain the optimal path that satisfies the kinematic constraints.

Planetary scientific target detection via deep learning: A case study for finding shatter cones in Mars rover images

AbstractPast, present, and forthcoming planetary rover missions to Mars and other planetary bodies are equipped with a large number of scientific cameras. The very large number of images resulting from this, combined with tight time constraints for navigation, measurements, and analyses, pose a major challenge for the mission teams in terms of scientific target evaluation. Shatter cones are the only macroscopic evidence for impact‐induced shock metamorphism and therefore impact craters on Earth. The typical features of shatter cones, such as striations and horsetail structures, are particularly suitable for machine learning methods. The necessary training images do not exist for such a case; therefore, we pursued the approach of producing them artificially. Using PRo3D, a viewer developed for the interactive exploration and geologic analysis of high‐resolution planetary surface reconstructions, we virtually placed shatter cones in 3‐D background scenes processed from true Mars rover imagery. We use PRo3D‐rendered images of such scenes as training data for machine learning architectures. Terrestrial analog studies in Ethiopia supported our lab work and were used to test the resulting neural network of this feasibility study. The result showed that our approach with shatter cones in artificial Mars rover scenes is suitable to train neural networks for automatic detection of shatter cones. In addition, we have identified several aspects that can be used to improve the training of the neural network and increase the recognition rate. For example, using background data with a higher resolution in order to have equal resolution of object (shatter cone) and Martian background and increase the number of objects that can be placed in the training data set. Also using better lighting reconstructions and a better radiometric adaption between object and Martian background would further improve the results.

Design and analysis of a high-speed planetary rover with an adaptive suspension and spring damper

The development of deep space exploration has led to new exploration demands that cannot be met by traditional planetary rovers. This paper proposes a new wheeled rover that combines the advantages of passive all-wheel attachment and vibration reduction at high speeds. This rover can achieve vibration reduction in the vertical direction from ground excitation, preventing the instability of pitch and roll. Flexible wheels were used to reduce the high-frequency excitation of the ground on the rover. The design concept and mechanical principle of the proposed rover were comprehensively analyzed. A quasi-static kinematic model was developed to analyze the overstepping performance of the rover with this new design. Further, the response characteristics of the system to the ground excitation were investigated by vibration equations established using the Lagrangian method. The optimal parameters were obtained using a genetic algorithm to optimize the system with multiple objectives. Furthermore, the overstepping performance, all-wheel attachment, and vibration reduction of the planetary rover were verified through multi-body simulation. The proposed rover has considerable potential for missions such as the Mars sample return and planetary station construction.

Autonomous robotics is driving Perseverance rover's progress on Mars.

NASA's Perseverance rover uses robotic autonomy to achieve its mission goals on Mars. Its self-driving autonomous navigation system (AutoNav) has been used to evaluate 88% of the 17.7-kilometer distance traveled during its first Mars year of operation. Previously, the maximum total autonomous distance evaluated was 2.4 kilometers by the Opportunity rover during its 14-year lifetime. AutoNav has set multiple planetary rover records, including the greatest distance driven without human review (699.9 meters) and the greatest single-day drive distance (347.7 meters). The Autonomous Exploration for Gathering Increased Science (AEGIS) system analyzes wide-angle imagery onboard to autonomously select targets for observations by the SuperCam instrument, a multimode sensor suite capable of millimeter-scale geochemical and mineralogical analysis. AEGIS enables observations of scientifically interesting targets during or immediately after long drives without the need for ground communication. OnBoard Planner (OBP) is a scheduling capability planned for operational use in September 2023 that has the potential to reduce energy usage by up to 20% and complete drive and arm-contact science campaigns in 25% fewer days on Mars. This paper presents an overview of the AutoNav, AEGIS, and OBP capabilities used on Perseverance.

Learning-Based End-to-End Navigation for Planetary Rovers Considering Non-Geometric Hazards

Learning-Based End-to-End Navigation for Planetary Rovers Considering Non-Geometric Hazards

Slip ratio of rigid wheels on TRI-1Lunar Simulant

Research work is conducted on performance evaluation of rover/rover wheel mobility in terms of slip ratio and motion resistance, which helps in solving and to overcome the present existing problems on a deformable soil terrain (flat/sloppy). The aim of this paper is to analyze the slip ratio and motion resistance of rigid wheels with various diameters, widths, lug height and different number of lugs, travelling on TRI-1 or Tiruchirappalli – 1 Lunar Soil Simulant. Slip ratio has more impact on the traveling performance of planetary rover which is a trafficability parameter. Hence, trafficability studies are required to understand –its affects, variation, and improvement of mobility from its parameter’s behavior. This paper brings out a best suitable combination of wheel that travels on TRI-1 Simulant, i.e., lugged wheels (small and large) with lug height 15 mm with 16 number of lugs and both wheels (plain small and plain large) are most suitable on Tiruchirappalli – 1 Lunar Soil Simulant with its maximum travelling performance and better mobility within the cases examined.

MARCEL: Mobile active rover chassis for enhanced locomotion

AbstractTo extend planetary exploration beyond the current limitations of wheeled vehicles while preserving reliability, simplicity, and efficiency, actuation can be judiciously incorporated into the locomotion system. Based on a static analysis, we propose a new four‐wheeled chassis concept for planetary rovers that can traverse more challenging terrain with the help of two internal active joints. These joints are arranged as follows: a vertical pivot articulates the chassis around its center while a bogie allows the rear wheels to rotate around the longitudinal axis of the vehicle. We also introduce a control method that uses a two‐stage procedure to produce an interpretable controller based on a policy devised by reinforcement learning. This way, we eliminate the black box made of a neural network and facilitate the transfer from simulation to reality. The resulting controller efficiently harnesses the internal mobility of the chassis to climb over obstacles in a sequenced manner while relying only on proprioceptive data provided by the chassis. A rover prototype named MARCEL has been built and tested experimentally. Contrary to any state‐of‐the‐art six‐wheeled passive chassis, the proposed locomotion system and its associated control has proven to be able to overcome solid step obstacles as tall as the diameter of the wheels with a edge and a friction coefficient as low as 0.5. This simple but capable design will enable future missions to explore more challenging areas while providing better guarantees in the face of unforeseen difficulties that could arise.

Robotic Detector with Rocker Bogie Mechanism

The Rocker Bogie Mechanism (RBM) is widely used in planetary rovers for its ability to traverse challenging terrain. However, the presence of metal debris in the terrain can pose a significant threat to the rover's mobility and instrumentation. Therefore, a metal detector integrated with the RBM is necessary to avoid any potential damage. This paper presents the design and development of a metal detector for the RBM, which includes the selection of suitable components, the development of the detection algorithm, and the integration with the RBM. The detector's performance is evaluated in both simulation and experimental settings, demonstrating the successful detection of metallic objects and their location.

Terrain classification using mars raw images based on deep learning algorithms with application to wheeled planetary rovers

Scene information plays a crucial role in motion control, attitude perception, and path planning for wheeled planetary rovers (WPRs). Terrain recognition is the fundamental component of scene recognition. Due to the rich information, visual sensors are usually used in terrain classification. However, teleoperation delay prevents WPRs from using visual information efficiently. End-to-end learning method of deep learning (DL) that does not need complex image preprocessing was proposed to deal with this issue. This paper first built a terrain dataset (consists of loose sand, bedrock, small rock, large rock, and outcrop) using real Mars images to directly support You Only Look Once (YOLOv5) to test its performance on terrain classification. Because the capability of end-to-end training scheme is positively correlated with dataset, the performance of YOLOv5 can be significantly improved by exploiting orders of magnitude more data. The best combination of hyperparameters and models was achieved by slightly tuning YOLOv5, and data augmentation was also applied to optimize its accuracy. Furthermore, its performance was compared with two other end-to-end network architectures. Deep learning algorithms can be used in the future planetary exploration missions, such as WPRs autonomy improvement, traversability analysis, and avoiding getting trapped.

A High-Temperature Accelerometer with Excellent Performance Based on the Improved Graphene Aerogel.

A high-temperature accelerometer plays an important role for ensuring normal operation of equipment in aerospace, such as monitoring and identifying abnormal vibrations of aircraft engines. Phase transitions of piezoelectric crystals, mechanical failure and current leakage of piezoresistive/capacitive materials are the prominent inherent limitations of present high-temperature accelerometers working continuously above 973 K. With the rapid development of aerospace, it is a great challenge to develop a new type of vibration sensor to meet the crucial demands at high temperature. Here we report a high-temperature accelerometer working with a contact resistance mechanism. Based on the improved graphene aerogel (GA) prepared by a modulated treatment process, the accelerometer can operate continuously and stably at 1073 K and intermittently at 1273 K. The developed sensor is lightweight (sensitive element <5 mg) and has high sensitivity (an order of magnitude higher than MEMS accelerometers) and wide frequency response range (up to 5 kHz at 1073 K) with marked stability, repeatability and low nonlinearity error (<1%). These merits are attributed to the excellent and stable mechanical properties of the improved GA in the range of 299-1073 K. The accelerometer could be a promising candidate for high-temperature vibration sensing in space stations, planetary rovers and others.

A computational tool for conceptual design and optimization of planetary rovers

The design process of a Mars rover is driven by multiple design constraints, namely overall mass, power consumption and volume (dimensions). Various systems, such as mobility, manipulation, handling, power, thermal, communication, navigation, avionics and science instruments, together make a complete rover vehicle and they should function collectively to perform a given task. Each of the subsystems can be thought of as modular building blocks that are integrated together to form a fully functional rover vehicle. When approaching the design of such a vehicle, the designer should take into account of cross design dependencies existent between different subsystems and technology limitations. Performing any particular task, would lead to many design possibilities. Choosing the final design from many feasible solutions is arguably a daunting task. In order to make this process simple and convenient, as well as to understand the design non-linearity existing in this solution space, the authors have employed a systems engineering approach to develop a tool comprising subsystem models. The subsystem models comprise parametric and physics-based models. For designing suitable user-defined objectives, these models when integrated with Genetic Algorithm forms an effective tool to support design trade-offs during the conceptual design process. This integrated modeling and optimization approach is thought to be efficient in identifying rover system concepts.