Planetary Exploration Rovers Research Articles

Trajectory Analysis and Modeling on Longitudinal/lateral Traversing of Slope Terrain for Planetary Exploration Rover

Abstract For a planetary exploration rover, the trafficability is affected by a variety of factors, such as ground hardness, slippage, slope angle on terrain, and so on. In this paper, we focus the development of an analysis model of multi-body dynamics, which consists of forces acting on each wheel based on a wheel and terrain interaction, and a rover dynamics model. From the proposed analysis model, the dynamic behavior for planetary exploration rover on the various inclined terrain during the longitudinal and lateral traversing is predicted. Moreover, we investigate the influence of the steering configuration and load distribution used for the compensation strategies on inclined deformable terrain. These simulation results imply that the appropriate compensation strategy should be adopted so that the rover successfully traverse slope terrain and achieve path following tasks.

Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges

AbstractFuture lunar/planetary exploration missions will demand mobile robots with the capability of reaching more challenging science targets and driving farther per day than the current Mars rovers. Among other improvements, reliable slippage estimation and compensation strategies will play a key role in enabling a safer and more efficient navigation. This paper reviews and discusses this body of research in the context of planetary exploration rovers. Previously published state‐of‐the‐art methods that have been validated through field testing are included as exemplary results. Limitations of the current techniques and recommendations for future developments and planetary missions close the survey.

Vision-Based Behavior Planning for Lunar or Planetary Exploration Rover on Flat Surface

Lunar or planetary exploration rovers are expected to have the ability to move across an area as wide as possible in an unknown environment during a limited mission period. Hence, they need an efficient navigation method. Most of the surface of the moon or planets consists of flat ground, sand, and scattered rocks. In a simple flat sandy terrain with some rocks, rough route planning is sufficient for a lunar or planetary rover to avoid obstacles and reach an assigned point. This paper proposes an efficient vision-based planning scheme for exploration rovers on a flat surface with scattered obstacles. In the proposed scheme, dangerous areas are robustly extracted by processing image data, and the degree of danger is defined. A rough routing plan and sensing plan are simultaneously constructed based on the dangerous-area extraction results. The effectiveness of the proposed scheme is discussed based on the results of some simulations and simple experiments.

Traversability-Based RRT*for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT*) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT*algorithm directly samples a point (node) from the LIDAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT*. This process improves conventional RRT*in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

Unsupervised classification of slip events for planetary exploration rovers

This paper introduces an unsupervised method for the classification of discrete rovers’ slip events based on proprioceptive signals. In particular, the method is able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip). The proposed method is based on aggregating the data over time, since high level concepts, such as high and low slip, are concepts that are dependent on longer time perspectives. Different features and subsets of the data have been identified leading to a proper clustering, interpreting those clusters as initial models of the prospective concepts. Bayesian tracking has been used in order to continuously improve the parameters of these models, based on the new data. Two real datasets are used to validate the proposed approach in comparison to other known unsupervised and supervised machine learning methods. The first dataset is collected by a single-wheel testbed available at MIT. The second dataset was collected by means of a planetary exploration rover in real off-road conditions. Experiments prove that the proposed method is more accurate (up to 86% of accuracy vs. 80% for K-means) in discovering various levels of slip while being fully unsupervised (no need for hand-labeled data for training).

Slippage and immobilization detection for planetary exploration rovers via machine learning and proprioceptive sensing

AbstractThis paper presents a new methodology where machine learning is used for detecting various levels of slip in the context of planetary exploration robotic missions. This methodology aims at employing proprioceptive rover sensor signals. Consequently, no operational complexity is added to the rover's commanding and it is independent of lighting conditions. Two supervised learning methods (Support Vector Machines and Artificial Neural Networks) are compared to two unsupervised learning approaches (K‐means and Self‐Organizing Maps (SOM)). Physical experiments using a single‐wheel testbed equipped with an MSL spare wheel and a real planetary exploration rover validate the implemented methodology. Performance is evaluated in terms of well‐known metrics both considering single data points and subsets of consecutive data points (moving median filter). Computation time and storage requirements are also examined. One of the SOM‐based algorithms, semantic SOM method, demonstrates a proper balance between the benefits of supervised learning algorithms (high success rate, >96%) and the advantages of unsupervised learning methods (low storage requirements, 5 kb, and no need of manually‐labeled training data). This paper also addresses the most convenient placement of IMU sensors on the rover chassis such that slippage detection is maximized.

50 years of rovers for planetary exploration: A retrospective review for future directions

This paper contributes an extensive review of the history from the former robotics to the present providing a particular emphasis on innovative technical contents of robotic exploration vehicles. To this end, a comprehensive study with a representative collection of 100 mobile robots along the history was performed for which a robot’s statistical profile was obtained considering aspects such as weight, size, number of wheels, and speed of movement. In addition, an exhaustive bibliometric analysis has been conducted over 8120 contributions between 1963 and 2015. The study on the scientific literature found that, thought mobile robotics is a research field being displaced by other disciplines of higher scientific return (e.g., humanoid robots, unmanned aircraft systems or intelligent autonomous vehicles), it is nevertheless confirmed the continuity of mobile robotics with the aim of developing advanced science at the expense of forthcoming space exploration missions. Therefore, this paper attempts to address what is the current state-of-the-art and what are the future challenges set in mobile robotics.

惑星探査ローバの円弧走行経路追従制御の研究

惑星探査ローバの円弧走行経路追従制御の研究

Experimental Study and Modeling of Wheel’s Steering Sinkage for Planetary Exploration Rovers

Experimental Study and Modeling of Wheel’s Steering Sinkage for Planetary Exploration Rovers

A novel sandwich-type traveling wave piezoelectric tracked mobile system

In this paper, a novel sandwich-type traveling wave piezoelectric tracked mobile system was proposed, designed, fabricated and experimentally investigated. The proposed system exhibits the advantages of simple structure, high mechanical integration, lack of electromagnetic interference, and lack of lubrication requirement, and hence shows potential application to robotic rovers for planetary exploration. The tracked mobile system is comprised of a sandwich actuating mechanism and a metal track. The actuating mechanism includes a sandwich piezoelectric transducer and two annular parts symmetrically placed at either end of the transducer, while the metal track is tensioned along the outer surfaces of the annular parts. Traveling waves with the same rotational direction are generated in the two annular parts, producing the microscopic elliptical motions of the surface particles on the annular parts. In this situation, if the pre-load is applied properly, the metal track can be driven by friction force to achieve bidirectional movement. At first, the finite element method was adopted to conduct the modal analysis and harmonic response analysis of the actuating mechanism, and the vibration characteristics were measured to confirm the operating principle. Then the optimal driving frequency of the system prototype, namely 35.1kHz, was measured by frequency sensitivity experiments. At last, the mechanical motion characteristics of the prototype were investigated experimentally. The results show that the average motion speeds of the prototype in dual directions were as 72mm/s and 61.5mm/s under the excitation voltage of 500VRMS, respectively. The optimal loading weights of the prototype in bi-directions were 0.32kg and 0.24kg with a maximum speed of 59.5mm/s and 61.67mm/s at the driving voltage of 300VRMS, respectively.

Autonomous Terrain Classification With Co- and Self-Training Approach

Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82% accuracy with only three labeled images.

Ontology-Based Self-Reconfiguring Guidance, Navigation, and Control for Planetary Rovers

Certain limitations exist in autonomous software and guidance, navigation, and control architectures developed for extraterrestrial planetary exploration rovers in regard to fault tolerance, changes in environment, and changes in rover capabilities. To address these limitations, this paper outlines a self-reconfiguring guidance, navigation, and control architecture, using an ontology-based rational agent to enable autonomous reconfiguration of mission goals, software architecture, software components, and the control of hardware components during the run time. This new architecture was evaluated through implementation onboard a rover and tested in challenging, Mars-like environments, both simulated and real world, and was found to be highly reliable, fault tolerant, and adaptable.

On the Design of Attitude-Heading Reference Systems Using the Allan Variance.

The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV).

Identifying mechanical property parameters of planetary soil using in-situ data obtained from exploration rovers

Identifying the mechanical property parameters of planetary soil based on terramechanics models using in-situ data obtained from autonomous planetary exploration rovers is both an important scientific goal and essential for control strategy optimization and high-fidelity simulations of rovers. However, identifying all the terrain parameters is a challenging task because of the nonlinear and coupling nature of the involved functions. Three parameter identification methods are presented in this paper to serve different purposes based on an improved terramechanics model that takes into account the effects of slip, wheel lugs, etc. Parameter sensitivity and coupling of the equations are analyzed, and the parameters are grouped according to their sensitivity to the normal force, resistance moment and drawbar pull. An iterative identification method using the original integral model is developed first. In order to realize real-time identification, the model is then simplified by linearizing the normal and shearing stresses to derive decoupled closed-form analytical equations. Each equation contains one or two groups of soil parameters, making step-by-step identification of all the unknowns feasible. Experiments were performed using six different types of single-wheels as well as a four-wheeled rover moving on planetary soil simulant. All the unknown model parameters were identified using the measured data and compared with the values obtained by conventional experiments. It is verified that the proposed iterative identification method provides improved accuracy, making it suitable for scientific studies of soil properties, whereas the step-by-step identification methods based on simplified models require less calculation time, making them more suitable for real-time applications. The models have less than 10% margin of error comparing with the measured results when predicting the interaction forces and moments using the corresponding identified parameters.

Autonomous rock classification using Bayesian image analysis for Rover-based planetary exploration

A robust classification system is proposed to support autonomous geological mapping of rocky outcrops using grayscale digital images acquired by a planetary exploration rover. The classifier uses 13 Haralick textural parameters to describe the surface of rock samples, automatically catalogues this information into a 5-bin data structure, computes Bayesian probabilities, and outputs an identification.The system has been demonstrated using a library of 30 digital images of igneous, sedimentary and metamorphic rocks. The images are 3.5×3.5cm2 in size and composed of 256×256 pixels with 256 grayscale levels. They are first converted to gray level co-occurrence matrices which quantify the number of times adjacent pixels of similar intensity are present. The Haralick parameters are computed from these matrices. When all 13 parameters are used, classification accuracy, defined using an empirical scoring system, is 65% due to a large number of false positives. When the number of parameters and the choice of parameter is optimized, classification accuracy increases to 80%. The best results were achieved with 3 parameters that can be interpreted visually (angular second moment, contrast, correlation) together with two statistical parameters (sum of squares variance and difference variance) and a parameter derived from information theory (information measure of correlation II).The system has been kept simple not to draw excessive computational power from the rover. It could, however, be easily extended to handle additional parameters such as images acquired at different wavelengths.

Real-time visual sinkage detection for planetary rovers

Identifying wheel sinkage for planetary exploration rovers can give a critical insight about the terrain traversability and in particular the characteristics of deformable soils that the rover travels on. This paper presents a monocular vision based approach that can detect and estimate the sinkage of a hybrid legged wheel in real time and robustly, with little sensitivity to changing operational conditions. The proposed method involves color-space segmentation that identifies the leg contour and consequently depth of wheel sinkage into the regolith. In addition, it enables dynamic analysis of the sinkage, hence making detection of non-geometric hazards possible while the rover moves. Extensive field trials have been conducted on natural deformable terrain. The experimental results demonstrate that the average discrepancy against annotated images is less than 1%.

Classifying terrain properties for planetary exploration rovers based on a combined distance measure with dynamic texture model

Non-geometric terrain properties surrounding a planetary exploration rover can be exploited to improve autonomous mobility of the rover. This paper shows a method to classify non-geometric terrain properties using image sequences obtained from onboard cameras. Our method is based on a Dynamic Texture analysis, which is a technique to estimate scene motion in image sequences. Using Dynamic Textures, we can incorporate a motion cue to classify not only soil types but also the velocities of a rover relative to terrain surface representing slippage due to terrains. First, we briefly show a learning algorithm for Dynamic Textures. Then we propose a combined distance measure for classification. Combining distance measures is useful to handle different properties of terrain, that is, a static property such as soil types and a dynamic property such as the velocities. The effectiveness of the combined distance measure for improving classification performance is demonstrated through experimental runs of two rover testbeds on sand pits.

The Right Path: Comprehensive Path Planning for Lunar Exploration Rovers

This article presents a comprehensive path-planning method for lunar and planetary exploration rovers. In this method, two new elements are introduced as evaluation indices for path planning: 1) determined by the rover design and 2) derived from a target environment. These are defined as the rover's internal and external elements, respectively. In this article, the rover's locomotion mechanism and insolation (i.e., shadow) conditions were considered to be the two elements that ensure the rover's safety and energy, and the influences of these elements on path planning were described. To examine the influence of the locomotion mechanism on path planning, experiments were performed using track and wheel mechanisms, and the motion behaviors were modeled. The planned paths of the tracked and wheeled rovers were then simulated based on their motion behaviors. The influence of the insolation condition was considered through path plan simulations conducted using various lunar latitudes and times. The simulation results showed that the internal element can be used as an evaluation index to plan a safe path that corresponds to the traveling performance of the rover's locomotion mechanism. The path derived for the tracked rover was found to be straighter than that derived for the wheeled rover. The simulation results also showed that path planning using the external element as an additional index enhances the power generated by solar panels under various insolation conditions. This path-planning method was found to have a large impact on the amount of power generated in the morning/evening and at high-latitude regions relative to in the daytime and at low-latitude regions on the moon. These simulation results suggest the effectiveness of the proposed pathplanning method.

A New INS/VNS Integrated Navigation Method for Planetary Exploration Rover

在以运动参数误差为状态量、视觉导航与惯导导航相对运动参数差为观测量 的传统惯性/视觉组合导航方法中, 为解决相对运动参数同时与前后两个时 刻状态相关的问题, 采用将前一时刻位置和姿态误差增广到状态量中的方法, 并且假设增广的状态量为常值, 导致状态模型中引入了较大的误差. 基于 真实位置、姿态建立观测量误差模型, 导致观测量同时与前后两个时刻的状 态相关. 本文以惯导误差方程为状态模型, 采用四元数差形式的相对运动 参数差作为观测量, 基于上一时刻组合导航位置、姿态估计值建立观测量误 差模型, 实现了状态的增广, 并使得量测信息仅与当前时刻的位置误差和平 台失准角相关, 克服了状态模型误差较大的问题. 月面仿真和地面模拟实验 均表明, 该方法能够达到较高的位置和姿态估计精度.

Study on an 8-Wheel Suspension to Enhance the Hill-Climbing Performance for a Planetary Exploration Rover

Planetary exploration rovers are likely to make a trip on a winding and sloping road of irregular surfaces to the destination in order to accomplish scientific missions. One of the key technologies for rovers is a suspension for traveling and performing exploration missions; the suspension is an essential area of technology for a stable movement of a rover. In this study, an 8-wheel suspension is designed to enable efficient climbing of slopes on a passage to the destination. For the two front wheels among the eight wheels, the moment at the pivot connecting two wheels is derived when the distance between the wheels and the torque of wheels are same. A test experiment was performed to compare the magnitude of moment according to the change in tilt angle and the position of the pivot. Finally, a suspension design considering the position of the pivot was proposed to enhance the hill-climbing performance.