Theory and experiments in SmartNav rover navigation

This paper describes theoretical and experimental results using the SmartNav rule-free fuzzy rover navigation system. SmartNav divides the terrain perceived by the rover into a number of circular sectors, and evaluates each sector using goal and safety preference factors to differentiate between preferred and unpreferred terrain sectors. The goal-preference factor is used to make sector evaluation based on the sector orientation relative to the designated goal position. The safety-preference factors are used to make sector evaluations on the basis of the sector local and regional terrain hazards. Three methods are developed to blend the three sector evaluations in order to find the effective preference factor for each sector. Two sector selection methods are then described in which the sector preference factors are used to find the heading command for the rover. The rover speed command is also computed based on the goal distance and safety-preference factor of the chosen sector. The above navigation steps are continuously repeated throughout the rover motion. Experimental results are presented to demonstrate the navigational capabilities of SmartNav using a commercial Pioneer 2AT rover traversing a simulated Martian terrain at the JPL Mini Mars Yard.

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Autonomous over-the-horizon navigation using LIDAR data

In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to enable autonomous navigation to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular Triangular Meshes (ITMs) are used for representing the environment, providing an accurate, yet compact, spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flat terrain and safely away from obstacles. Experiments performed in CSA’s Mars emulation terrain, validating our approach, are also presented.     

Fast approximate clearance evaluation for rovers with articulated suspension systems

We present a light‐weight body‐terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse‐kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the approximate clearance evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst‐case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments.     

Nonparametric Traversability Estimation in Partially Occluded and Deformable Terrain

Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long‐term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover's onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover‐terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover's attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near‐to‐far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover's attitude and configuration in partially occluded and deformable terrain.     

A stereo-vision system for support of planetary surface exploration

Abstract. In this paper, we present a system that was developed for the European Space Agency (ESA) for the support of planetary exploration. The system that is sent to the planetary surface consists of a rover and a lander. The lander contains a stereo head equipped with a pan-tilt mechanism. This vision system is used both for modeling the terrain and for localization of the rover. Both tasks are necessary for the navigation of the rover. Due to the stress that occurs during the flight, a recalibration of the stereo-vision system is required once it is deployed on the planet. Practical limitations make it unfeasible to use a known calibration pattern for this purpose; therefore, a new calibration procedure had to be developed that could work on images of the planetary environment. This automatic procedure recovers the relative orientation of the cameras and the pan and tilt axes, as well as the exterior orientation for all the images. The same images are subsequently used to reconstruct the 3-D structure of the terrain. For this purpose, a dense stereo-matching algorithm is used that (after rectification) computes a disparity map. Finally, all the disparity maps are merged into a single digital terrain model. In this paper, a simple and elegant procedure is proposed that achieves that goal. The fact that the same images can be used for both calibration and 3-D reconstruction is important, since, in general, the communication bandwidth is very limited. In addition to navigation and path planning, the 3-D model of the terrain is also used for virtual-reality simulations of the mission, wherein the model is texture mapped with the original images. The system has been implemented, and the first tests on the ESA planetary terrain testbed were successful.     

Visual terrain mapping for Mars exploration

One goal for future Mars missions is for a rover to be able to navigate autonomously to science targets not visible to the rover, but seen in orbital or descent images. This can be accomplished if accurate maps of the terrain are available for the rover to use in planning and localization. We describe techniques to generate such terrain maps using images with a variety of resolutions and scales, including surface images from the lander and rover, descent images captured by the lander as it approaches the planetary surface, and orbital images from current and future Mars orbiters. At the highest resolution, we process surface images captured by rovers and landers using bundle adjustment. At the next lower resolution (and larger scale), we use wide-baseline stereo vision to map terrain distant from a rover with surface images. Mapping the lander descent images using a structure-from-motion algorithm generates data at a hierarchy of resolutions. These provide a link between the high-resolution surface images and the low-resolution orbital images. Orbital images are mapped using similar techniques, although with the added complication that the images may be captured with a variety of sensors. Robust multi-modal matching techniques are applied to these images. The terrain maps are combined using a system for unifying multi-resolution models and integrating three-dimensional terrains. The result is a multi-resolution map that can be used to generate fixed-resolution maps at any desired scale.     

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.     

SmartNav: A rule‐free fuzzy approach to rover navigation

This paper introduces the basic concepts of the SmartNav rule‐free fuzzy approach to safe rover navigation through hazardous natural terrain. This novel “rule‐free” approach reduces the complexity in rover navigation where many alternative paths must be evaluated and compared. The SmartNav rover navigation architecture integrates goal attainment with both local and regional hazard avoidance. Goal and safety preference factors differentiate between preferred and unpreferred terrain sectors. The goal‐preference factor is used to make sector evaluation based on the sector orientation relative to the designated goal position. The safety‐preference factors are used to make sector evaluations on the basis of the sector local and regional hazards. These sector evaluations are blended to find the effective preference factor for each sector. The preference factors of all sectors are then compared to choose the heading command for the rover. The rover speed command is also computed based on the goal distance and safety‐preference factor of the chosen sector. The above navigation steps are continuously repeated throughout the rover motion. Numerical examples are presented to illustrate the basic concepts introduced in this paper, and tests on a commercial rover are planned. © 2005 Wiley Periodicals, Inc.     

Evolution of a Prototype Lunar Rover: Addition of Laser-Based Hazard Detection, and Results from Field Trials in Lunar Analog Terrain

This paper presents the results of field trials of a prototype lunar rover traveling over natural terrain under safeguarded teleoperation control. Both the rover and the safeguarding approach have been used in previous work. The original contributions of this paper are the development and integration of a laser hazard detection system, and extensive field testing of the overall system. The laser system, which complements an existing stereo vision system, is based on a line-scanning laser ranger viewing the area 1 meter in front of the rover. The laser system has demonstrated excellent performance: zero misses and few false alarms operating at 4 Hz. The overall safeguarding system guided the rover 43 km over lunar analogue terrain with 0.8 failures per kilometer.     

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.     

非对称行星探测车行走系统的动力学仿真及运动性能分析

为了提高探测车在崎岖路面中的运动性能,提出了一种非对称轮式探测车行走系统.该车具有整体式超静定结构,6个车轮通过悬挂装置与车身相连,非对称分布于车体两侧,悬架结构可以主动控制来提高探测车在崎岖路面中的运动性能.采用牛顿-欧拉法建立了行走系统的动力学模型,给出了几何和速度约束方程,采用有限差分法求解由微分方程和代数方程构...     

基于自主行为智能体的月球车运动规划与控制

研究基于自主行为智能体的月球车运动规划与控制方法．在基于自主行为智能体的月球车系统结构基础上,首先设计了月球车运动规划与控制的一组基本行为,对其原理进行证明．通过行为状态机对行为进行选择,如果不能保障月球车安全性能,则由运动规划智能体学习其行为参数,并由神经网络记忆．将月球车运动规划与控制分解为行为设计与学习两个过程,使月球车控制系统易于加入先验知识．同时,月球车运动规划能够满足其机动性与地形传送性约束,保证工程开发的结构化与可实现性．该方法不仅具有实时性,而且对未知环境具有较强的适应能力．仿真研究与实验证明了该方法的有效性．     

An intelligent terrain-based navigation system for planetary rovers

A fuzzy logic framework for onboard terrain analysis and guidance towards traversable regions. An onboard terrain-based navigation system for mobile robots operating on natural terrain is presented. This system utilizes a fuzzy-logic framework for onboard analysis of the terrain and develops a set of fuzzy navigation rules that guide the rover toward the safest and the most traversable regions. The overall navigation strategy deals with uncertain knowledge about the environment and uses the onboard terrain analysis to enable the rover to select easy-to-traverse paths to the goal autonomously. The navigation system is tested and validated with a set of physical rover experiments and demonstrates the autonomous capability of the system     

Field testing of a rover guidance, navigation, and control architecture to support a ground-ice prospecting mission to Mars

This paper provides an overview of a rover guidance, navigation, and control (GN&C) architecture being developed to support a ground-ice prospecting mission to Mars. The main contribution of this paper is to detail an integrated field campaign that demonstrates the viability of the key rover GN&C techniques needed to carry out this mission. Tests were conducted on Devon Island in the Canadian High Arctic during the summer of 2009, wherein a large field robot was driven on real polygonal terrain (a landform of interest on Mars). Lessons learned and recommendations for future work are provided.     

基于模型估计的关节式月球车参数化轨迹规划*

为了在轨迹规划阶段提高月球车在三维地形中的轨迹规划精度,以被动关节式地形自适应月球车为研究对象,融合关节机器人D-H坐标建模方法构建月球车悬架运动学模型,结合数值求解方法,推导了任意崎岖三维地形中月球车姿态估计模型.在模型估计基础上利用参数化控制原理,建立了满足约束条件下被动关节式月球车在任意地形中的基于模型估计的一般性参数化轨迹生成模型.针对轮式月球车的非完整性特点,结合数值求解方法,推导了非线性模型的求解方法.最后利用仿真方法,以八轮摇杆摇臂关节式月球车为例,验证了崎岖地形中基于模型估计的轨迹生成方法的正确性,且可提高关节式月球车在崎岖地形中的规划精度.     

月球车仿真系统中若干视景真实感实现技术

虚拟现实技术的出现为月球漫游车的设计、优化等提供了新的有效手段.为在虚拟现实环境下开发月球车仿真系统,视景真实感实现技术在其中具有霞要地位.研究了真实感地形生成技术、仿真过程中的粒子系统特效技术以及车轮沉陷和车轮轨迹生成技术.基于分形技术和月面特征地形生月面高程数据;利用Muhigen Creator构建和渲染了月面几何模型;基于OpenGL Performer开发了仿真程序并应用到一种月球车运动仿真系统中.系统可在微机和SGI图形工作站运行,仿真结果显示具有良好的真实感.     

Trafficability Assessment of Deformable Terrain through Hybrid Wheel‐Leg Sinkage Detection

Off‐road ground mobile robots are widely used in diverse applications, both in terrestrial and planetary environments. They provide an efficient alternative, with lower risk and cost, to explore or to transport materials through hazardous or challenging terrain. However, nongeometric hazards that cannot be detected remotely pose a serious threat to the mobility of such robots. A prominent example of the negative effects these hazards can have is found on planetary rover exploration missions. They can cause a serious degradation of mission performance at best and complete immobilization and mission failure at worst. To tackle this issue, the work presented in this paper investigates the novel application of an existing enhanced‐mobility locomotion concept, a hybrid wheel‐leg equipped by a lightweight micro‐rover, for in situ characterization of deformable terrain and online detection of nongeometric hazards. This is achieved by combining an improved vision‐based approach and a new ranging‐based approach to wheel‐leg sinkage detection. In addition, the paper proposes an empirical model, and a parametric generalization, to predict terrain trafficability based on wheel‐leg sinkage and a well‐established semiempirical terramechanics model. The robustness and accuracy of the sinkage detection methods implemented are tested in a variety of conditions, both in the laboratory and in the field, using a single wheel‐leg test bed. The sinkage‐trafficability model is developed based on experimental data using this test bed and then validated onboard a fully mobile robot through experimentation on a range of dry frictional soils that covers a wide spectrum of macroscopic physical characteristics.     

Game theory basis for control of long-lived lunar/planetary surface robots

Current and future NASA robotic missions to planetary surfaces are tending toward longer duration and are becoming more ambitious for rough terrain access. For a higher level of autonomy in such missions, the rovers will require behavior that must also adapt to declining health and unknown environmental conditions. The MER (Mars Exploration Rovers) called Spirit and Opportunity have both passed 600 days of life on the Martian surface, with extensions to 1000 days and beyond depending on rover health. Changes in navigational planning due to degradation of the drive motors as they reach their lifetime are currently done on Earth for the Spirit rover. The upcoming 2009 MSL (Mars Science Laboratory) and 2013 AFL (Astrobiology Field Laboratory) missions are planned to last 300–500 days, and will possibly involve traverses on the order of multiple kilometers over challenging terrain. This paper presents a unified coherent framework called SMART (System for Mobility and Access to Rough Terrain) that uses game theoretical algorithms running onboard a planetary surface rover to safeguard rover health during rough terrain access. SMART treats rover motion, task planning, and resource management as a Two Person Zero Sum Game (TPZSG), where the rover is one player opposed by the other player called “nature” representing uncertainty in sensing and prediction of the internal and external environments. We also present preliminary results of some field studies. Terry Huntsberger is a Principal Member of the Technical Staff in the Advanced Robotic Controls Group at NASA’s Jet Propulsion Laboratory in Pasadena, CA, where he is the Manager for numerous tasks in the areas of multi-robot control systems, and rover systems for access to high risk terrain. He is an Adjunct Professor and former Director of the Intelligent Systems Laboratory in the Department of Computer Science at the University of South Carolina. His research interests include behavior-based control, computer vision, neural networks, wavelets, and biologically inspired system design. Dr. Huntsberger has published over 120 technical articles in these and associated areas. He received his PhD in Physics in 1978 from the University of South Carolina. He is a member of SPIE, ACM, IEEE Computer Society, and INNS. Abhijit Sengupta is a Senior Member of Engineering Staff in the Advanced Concepts and Architecture Group of the Jet Propulsion Laboratory in Pasadena, California. His research interest includes distributed architecture, algorithm design and fault-tolerant computing and he has more than 100 publications in these and other related areas. Prior to joining JPL in 2001, he was a Professor in the Department of Computer Science and Engineering at the University of South Carolina. He received his Ph.D. in 1976 in Electronic Engineering from the University of Calcutta.     

Seeker—Autonomous Long‐range Rover Navigation for Remote Exploration

Under the umbrella of the European Space Agency (ESA) StarTiger program, a rapid prototyping study called Seeker was initiated. A range of partners from space and nonspace sectors were brought together to develop a prototype Mars rover system capable of autonomously exploring several kilometers of highly representative Mars terrain over a three‐day period. This paper reports on our approach and the final field trials that took place in the Atacama Desert, Chile. Long‐range navigation and the associated remote rover field trials are a new departure for ESA, and this activity therefore represents a novel initiative in this area. The primary focus was to determine if current computer vision and artificial intelligence based software could enable such a capability on Mars, given the current limit of around 200 m per Martian day. The paper does not seek to introduce new theoretical techniques or compare various approaches, but it offers a unique perspective on their behavior in a highly representative environment. The final system autonomously navigated 5.05 km in highly representative terrain during one day. This work is part of a wider effort to achieve a step change in autonomous capability for future Mars/lunar exploration rover platforms.     

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

This 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.