Interaction Mechanics Model for Rigid Driving Wheels of Planetary Rovers Moving on Sandy Terrain with Consideration of Multiple Physical Effects

Predicting wheel‐terrain interaction with semiempirical models is of substantial importance for developing planetary wheeled mobile robots (rovers). Primarily geared toward the design of manned terrestrial vehicles, conventional terramechanics models do not provide the sufficient fidelity required for application on autonomous planetary rovers. To develop a high‐fidelity interaction mechanics model, in this study the physical effects of wheel lug, slip sinkage, wheel dimension, and load are analyzed based on experimental results, including wheel sinkage, drawbar pull, normal force, and moment, which are measured on a single‐wheel test bed. The mechanism of lug‐terrain interaction is investigated systematically to clarify the principle of increasing shear stress, conditions of forming successive shearing among adjacent lugs, and the influence on shear displacement of soil. A mathematical model for predicting the concentrated forces and torque of rigid wheels with lugs for planetary rovers moving on sandy terrain is derived by integrating the improved models of normal and shearing stress distributions. In addition to the wheel parameters, terrain parameters, and motion state variables, wheel‐terrain interaction parameters, such as the linear varying sinkage exponent, the soil displacement radius, and load effect parameters, were proposed and explicitly included in the model. In the single‐wheel experiments, the slip ratio was increased approximately from 0.05 to 0.6, and the relative errors of the predicted results using the proposed model are less than 10% for all the wheels when compared with the experimental data. The proposed model has been used in the simulation of a four‐wheeled rover, and its effectiveness is evaluated by comparing the simulation results with experimental results.     

Mars Science Laboratory Curiosity Rover Megaripple Crossings up to Sol 710 in Gale Crater

After landing in Gale Crater on August 6, 2012, the Mars Science Laboratory Curiosity rover traveled across regolith‐covered, rock‐strewn plains that transitioned into terrains that have been variably eroded, with valleys partially filled with windblown sands, and intervening plateaus capped by well‐cemented sandstones that have been fractured and shaped by wind into outcrops with numerous sharp rock surfaces. Wheel punctures and tears caused by sharp rocks while traversing the plateaus led to directing the rover to traverse in valleys where sands would cushion wheel loads. This required driving across a megaripple (windblown, sand‐sized deposit covered by coarser grains) that straddles a narrow gap and several extensive megaripple deposits that accumulated in low portions of valleys. Traverses across megaripple deposits led to mobility difficulties, with sinkage values up to approximately 30% of the 0.50 m wheel diameter, resultant high compaction resistances, and rover‐based slip up to 77%. Analysis of imaging and engineering data collected during traverses across megaripples for the first 710 sols (Mars days) of the mission, laboratory‐based single‐wheel soil experiments, full‐scale rover tests at the Dumont Dunes, Mojave Desert, California, and numerical simulations show that a combination of material properties and megaripple geometries explain the high wheel sinkage and slip events. Extensive megaripple deposits have subsequently been avoided and instead traverses have been implemented across terrains covered with regolith or thin windblown sand covers and megaripples separated by bedrock exposures.     

Data‐driven mobility risk prediction for planetary rovers

Mobility assessment and prediction continues to be an important and active area of research for planetary rovers, with the need illustrated by multiple examples of high slip events experienced by rovers on Mars. Despite slip versus slope being one of the strongest and most broadly used relationships in mobility prediction, this relationship is nonetheless far from precisely predictable. Although the literature has made significant advances in the predictability of average mobility, the other key related aspect of the problem is the risk caused by edge cases. A key contribution of this study is a metric for explicitly assessing mobility risk based on data‐driven nonparametric slip versus slope relationships. The data‐driven approach is meant to address limitations of past model‐based approaches. The metric is informed by past work in terramechanics relating drawbar pull (i.e., net traction) to slip: High slip fraction (HSF), defined as the proportion of slip data points above 20%. Another contribution is a low complexity mobility prediction framework, the autonomous soil assessment system. Field tests demonstrate that, for sand and gravel, rover trafficability becomes nonlinear and highly variable above the 20% slip threshold. HSF is shown to be a useful metric for categorizing rover‐terrain interactions into low, medium, or high risk, correctly and consistently. Furthermore, the metric is shown to be useful for early detection of potentially hazardous changes in rover‐terrain conditions. The combination of HSF with an appropriately sized queue structure for modeling slip versus slope enables an appropriate balance between responsiveness and stability.     

Rule–based reasoning and neural network perception for safe off–road robot mobility

Operational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a mobility and navigation context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein inertial sensing of safety status and vision–based neural network perception of terrain quality are used to infer safe speeds of traversal. Results of initial field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of robot mobility and navigation risks.     

星球车松软星壤的轮腿式探测系统:Ⅱ-感知车轮

为增强星球车松软星壤的穿越通过能力,提出了轮壤交互接触信息的感知车轮设计。该车轮是一种星球车的前置轮腿式探测系统(WOLS)的关键部分,可实现动态轮壤交互的关键量测量(轮壤作用力/力矩、轮壤接触角和车轮沉陷量)。研究分析了轮壤力学的关键测量参量及其分组,完成了感知车轮的硬件设计和集成,提出了待测参数的在线测量模型和方法,通过标定校准和实车测试验证了该感知车轮的性能。     

摇臂-转向架式行星探测车移动系统的准静态分析

行星探测车移动系统是探测车整体系统的关键部分之一，摇臂—转向架式移动系 统是一种对地形具有自适应能力、机动性较强的系统．本文在研究摇臂—转向架式移动系统 机械结构的基础上，对其进行了准静态分析，所得的结构参数可作为分析和优化探测车移动 系统概念设计的主要工具．     

Effect of gravity in wheel/terrain interaction models

Predicting the motion of wheeled robots in unstructured environments is an important and challenging problem. The study of planetary exploration rovers on soft terrain introduces the additional need to consider the effect of nonterrestrial gravitational fields on the forces and torques developed at the wheel/terrain interface. Simply reducing the wheel load under Earth's gravity overestimates the traveled distance and predicts better performance than is actually observed in reduced‐gravity measurements. In this paper, we study the effect of gravity on wheel/terrain interaction. Experiments were conducted to assess the effect of reduced gravity on the velocity profile of the soil under the wheel, as well as on the traction force and sinkage developed by the wheel. It was shown that in the velocity field of the soil, the decay of the tangential velocity component becomes gradual with reducing gravity, and the decay of the normal to rim velocity is slower in Lunar gravity. It was also found that wheel flexibility can have an important effect on the dynamics as the contact patch and effective radius vary periodically. These results were then used together with traditional semiempirical terramechanics models to determine and validate the simulated drawbar pull values. The developed simulation model includes the effect of wheel flexibility, dynamic sinkage, and gravity.     

Independent traction control for uneven terrain using stick-slip phenomenon: application to a stair climbing robot

Mobile robots are being developed for building inspection and security, military reconnaissance, and planetary exploration. In such applications, the robot is expected to encounter rough terrain. In rough terrain, it is important for mobile robots to maintain adequate traction as excessive wheel slip causes the robot to lose mobility or even be trapped. This paper proposes a traction control algorithm that can be independently implemented to each wheel without requiring extra sensors and devices compared with standard velocity control methods. The algorithm estimates the stick-slip of the wheels based on estimation of angular acceleration. Thus, the traction force induced by torque of wheel converses between the maximum static friction and kinetic friction. Simulations and experiments are performed to validate the algorithm. The proposed traction control algorithm yielded a 40.5% reduction of total slip distance and 25.6% reduction of power consumption compared with the standard velocity control method. Furthermore, the algorithm does not require a complex wheel-soil interaction model or optimization of robot kinematics.     

Autonomous science during large‐scale robotic survey

Today's planetary exploration robots rarely travel beyond the yesterday imagery. However, advances in autonomous mobility will soon permit single‐command site surveys of multiple kilometers. Here scientists cannot see the terrain in advance, and explorer robots must navigate and collect data autonomously. Onboard science data understanding can improve these surveys with image analysis, pattern recognition, learned classification, and information‐theoretic planning. We report on field experiments near Amboy Crater, California, that demonstrate fundamental capabilities for autonomous surficial mapping of geologic phenomena with a visible near‐infrared spectrometer. We develop an approach to “science on the fly'' that adapts the robot's exploration using collected instrument data. We demonstrate feature detection and visual servoing to acquire spectra from dozens of targets without human intervention. The rover interprets spectra onboard, learning spatial models of science phenomena that guide it toward informative areas. It discovers spatial structure (correlations between neighboring regions) and cross‐sensor structure (correlations between different scales). The rover uses surface observations to reinterpret satellite imagery and improve exploration efficiency. © 2011 Wiley Periodicals, Inc.     

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes, without the need for predefined configurations.     

月球车驱动轮爬坡性能的影响因素研究

研究了驱动轮的滑转率、结构参数和载荷对月球车爬坡性能的影响。针对月表复杂路况下车辆通过性较差问题,为提高月球车爬坡性能,基于车辆地面力学中土壤的承压和剪切特性理论,建立了刚性驱动轮与月面斜坡松软月壤间的相互作用模型。通过车轮实例对月球车爬坡性能进行预测和分析。实验结果表明,增大驱动轮的滑转率、半径、宽度、轮刺高度和减小载荷能够增强月球车爬坡能力和提高车轮驱动效率,但随着爬坡能力的增强,车轮驱动效率呈现先增大后减小的趋势。研究结果为月球车轮结构设计以及控制策略的研究提供了参考。     

Systematic kinematics analysis and balance control of high mobility rovers over rough terrain

The paper proposes a systematic method for kinematics modeling, analysis and balance control of a general high mobility wheeled rover traversing uneven terrain. The method is based on the propagation of position and orientation velocities starting from the rover reference frame and going through various joints and linkages to the wheels. The concept of an extended DH table is introduced for rovers and mobile robots, and equations of the motion are set up in a compact form. Actuation kinematics and balance control are formulated for rovers traversing bumpy terrains. To illustrate the proposed kinematics modeling and balancing, the method is applied to a high mobility rover and simulation results with various terrains are presented.     

Improved Rover State Estimation in Challenging Terrain

Given ambitious mission objectives and long delay times between command-uplink/data-downlink sessions, increased autonomy is required for planetary rovers. Specifically, NASA's planned 2003 and 2005 Mars rover missions must incorporate increased autonomy if their desired mission goals are to be realized. Increased autonomy, including autonomous path planning and navigation to user designated goals, relies on good quality estimates of the rover's state, e.g., its position and orientation relative to some initial reference frame. The challenging terrain over which the rover will necessarily traverse tends to seriously degrade a dead-reckoned state estimate, given severe wheel slip and/or interaction with obstacles. In this paper, we present the implementation of a complete rover navigation system. First, the system is able to adaptively construct semi-sparse terrain maps based on the current ground texture and distances to possible nearby obstacles. Second, the rover is able to match successively constructed terrain maps to obtain a vision-based state estimate which can then be fused with wheel odometry to obtain a much improved state estimate. Finally the rover makes use of this state estimate to perform autonomous real-time path planning and navigation to user designated goals. Reactive obstacle avoidance is also implemented for roaming in an environment in the absence of a user designated goal. The system is demonstrated in soft soil and relatively dense rock fields, achieving state estimates that are significantly improved with respect to dead reckoning alone (e.g., 0.38 m mean absolute error vs. 1.34 m), and successfully navigating in multiple trials to user designated goals.     

Terrain characterization and classification with a mobile robot

This paper introduces novel methods for terrain classification and characterization with a mobile robot. In the context of this paper, terrain classification aims at associating terrains with one of a few predefined, commonly known categories, such as gravel, sand, or asphalt. Terrain characterization, on the other hand, aims at determining key parameters of the terrain that affect its ability to support vehicular traffic. Such properties are collectively called “trafficability.” The proposed terrain classification and characterization system comprises a skid‐steer mobile robot, as well as some common and some uncommon but optional onboard sensors. Using these components, our system can characterize and classify terrain in real time and during the robot's actual mission. The paper presents experimental results for both the terrain classification and characterization methods. The methods proposed in this paper can likely also be implemented on tracked robots, although we did not test this option in our work.     

Range‐dependent Terrain Mapping and Multipath Planning using Cylindrical Coordinates for a Planetary Exploration Rover

This paper presents terrain mapping and path‐planning techniques that are key issues for autonomous mobility of a planetary exploration rover. In this work, a LIDAR (light detection and ranging) sensor is used to obtain geometric information on the terrain. A point cloud of the terrain feature provided from the LIDAR sensor is usually converted to a digital elevation map. A sector‐shaped reference grid for the conversion process is proposed in this paper, resulting in an elevation map with cylindrical coordinates termed as C²DEM. This conversion approach achieves a range‐dependent resolution for the terrain mapping: a detailed terrain representation near the rover and a sparse representation far from the rover. The path planning utilizes a cost function composed of terrain inclination, terrain roughness, and path length indices, each of which is subject to a weighting factor. The multipath planning developed in this paper first explores possible sets of weighting factors and generates multiple candidate paths. The most feasible path is then determined by a comparative evaluation between the candidate paths. Field experiments with a rover prototype at a Lunar/Martian analog site were performed to confirm the feasibility of the proposed techniques, including the range‐dependent terrain mapping with C²DEM and the multipath‐planning method.     

Efficient autonomous navigation for planetary rovers with limited resources

Rovers operating on Mars require more and more autonomous features to fulfill their challenging mission requirements. However, the inherent constraints of space systems render the implementation of complex algorithms an expensive and difficult task. In this paper, we propose an architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the ExoMars path following navigation approach to enhance the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform significantly longer traverses planned by operators on the ground. The efficient navigation approach has been implemented and tested during field test campaigns on a planetary analogue terrain. The experiments evaluated the proposed architecture by autonomously completing several traverses of variable lengths while avoiding hazards. The approach relies only on the optical Localization Cameras stereo bench, a sensor that is found in all current rovers, and potentially allows for computationally inexpensive long‐range autonomous navigation in terrains of medium difficulty.     

基于滑转率的六轮纵列式月球车驱动控制研究

基于车轮滑转率和车轮地面力学,研究了月球车在松软月面行驶时的车轮过度下陷问题.将 月球车车轮下陷和车轮—土壤作用力表达为车轮滑转率的函数,结合车辆地面力学理论并考 虑纵列式车轮多通过性土壤参数的修正,建立了月球车的动力学模型.判断车轮是否发生过 度下陷的标准为土壤所提供给驱动轮的土壤推力能否克服土壤对车轮的阻力.利用建立的动 力学模型,计算出能够保证车轮不会过度下陷的期望滑转率.考虑到月球车动力学系统的非 线性和不确定性,设计了以车轮滑转率为状态变量的滑模驱动控制器.仿真结果表明,采用 该控制器可以较快地跟踪期望滑转率,避免车轮的过度滑转下陷,保证月球车能够在软质路 面上正常行驶.     

滑转率对月球车车轮驱动力学特性的影响分析

应用车辆地面力学理论研究滑转率对月球车车轮挂钩牵引力、驱动效率以及功率消耗的影响.建立刚性车轮与松软月壤交互作用的动力学模型.通过实例对月球车车轮驱动动力学特性进行仿真分析.研究结果表明,车轮的挂钩牵引力、驱动效率以及驱动能耗均受到车轮滑转率的制约.存在一个最优的滑转率区间,在此区间内车轮可获得较大的挂钩牵引力、较高的驱动效率以及较低的驱动能耗.求取轮、地相对速度,对月球车车轮的地面摩擦力功率进行了估算.     

Attitude-based dynamic and kinematic models for wheels of mobile robot on deformable slope

Deformable slope is a type of terrain that wheeled mobile robots (WMRs) and ground unmanned vehicles (GUVs) may have to traverse to accomplish their mission tasks. However, the associated terramechanics for wheels with arbitrary posture is rarely studied. In this paper, based on wheel attitude, dynamics of the wheel–terrain interaction for a rigid wheel on deformable slope is investigated. Through introducing the angular geometry of wheel attitude into terramechanics theory, a generalized dynamic model is developed, involving two inclination angles of slope and three attitude angles of wheel steering axis. Two representative cases are studied: the wheel runs straight forward and perpendicular to the slope, and the wheel is in a steering maneuver with an inclined steering axis. A generalized kinematic model for wheel–terrain contact point and wheel center is also provided, which analytically explicates that trajectory of wheel motion is coupled with wheel attitude while driven by angular rates. The proposed attitude-based models are valid for arbitrary wheel–terrain geometry and can lead to control purpose directly. Effectiveness of the models is confirmed by simulating the influences from attitude to wheel mechanics and motion.     

Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers

Future planetary exploration missions will require wheeled mobile robots ("rovers") to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper, an online estimation method that identifies key terrain parameters using on-board robot sensors is presented. These parameters can be used for traversability prediction or in a traction control algorithm to improve robot mobility and to plan safe action plans for autonomous systems. Terrain parameters are also valuable indicators of planetary surface soil composition. The algorithm relies on a simplified form of classical terramechanics equations and uses a linear-least squares method to compute terrain parameters in real time. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for various soil types.