Visual wheel sinkage measurement for planetary rover mobility characterization

Wheel sinkage is an important indicator of mobile robot mobility in natural outdoor terrains. This paper presents a vision-based method to measure the sinkage of a rigid robot wheel in rigid or deformable terrain. The method is based on detecting the difference in intensity between the wheel rim and the terrain. The method uses a single grayscale camera and is computationally efficient, making it suitable for systems with limited computational resources such as planetary rovers. Experimental results under various terrain and lighting conditions demonstrate the effectiveness and robustness of the algorithm. Christopher Brooks is a graduate student in the Mechanical Engineering department of the Massachusetts Institute of Technology. He received his B.S. degree with honor in engineering and applied science from the California Institute of Technology in 2000, and his M.S. degree from the Massachusetts Institute of Technology in 2004. He is a student collaborator on the Mars Exploration Rover science mission. His research interests include mobile robot control, terrain sensing, and their application to improving autonomous robot mobility. He is a member of Tau Beta Pi. Karl Iagnemma is a research scientist in the Mechanical Engineering department of the Massachusetts Institute of Technology. He received his B.S. degree summa cum laude in mechanical engineering from the University of Michigan in 1994, and his M.S. and Ph.D. from the Massachusetts Institute of Technology, where he was a National Science Foundation graduate fellow, in 1997 and 2001, respectively. He has been a visiting researcher at the Jet Propulsion Laboratory. His research interests include rough-terrain mobile robot control and motion planning, robot-terrain interaction, and robotic mobility analysis. He is author of the monograph Mobile Robots in Rough Terrain: Estimation, Motion Planning, and Control with Application to Planetary Rovers (Springer, 2004). He is a member of IEEE and Sigma Xi. Steven Dubowsky received his Bachelor's degree from Rensselaer Polytechnic Institute of Troy, New York in 1963, and his M.S. and Sc.D. degrees from Columbia University in 1964 and 1971. He is currently a Professor of Mechanical Engineering at M.I.T and Director of the Mechanical Engineering Field and Space Robotics Laboratory. He has been a Professor of Engineering and Applied Science at the University of California, Los Angeles, a Visiting Professor at Cambridge University, Cambridge, England, and Visiting Professor at the California Institute of Technology. During the period from 1963 to 1971, he was employed by the Perkin-Elmer Corporation, the General Dynamics Corporation, and the American Electric Power Service Corporation. Dr. Dubowsky's research has included the development of modeling techniques for manipulator flexibility and the development of optimal and self-learning adaptive control procedures for rigid and flexible robotic manipulators. He has authored or co-authored nearly 300 papers in the area of the dynamics, control and design of high performance mechanical and electromechanical systems. Professor Dubowsky is a registered Professional Engineer in the State of California and has served as an advisor to the National Science Foundation, the National Academy of Science/Engineering, the Department of Energy, and the US Army. He is a fellow of the ASME and IEEE and is a member of Sigma Xi and Tau Beta Pi.     

瓦片块四叉树动态地形层次细节算法

针对动态地形中车辙实时可视化的要求,提出并实现了一种基于图形处理器( GPU)的动态地形层次细节(LOD)算法.以基于GPU的动态地形算法作为地形形变算法,以瓦片块四叉树算法作为地形绘制算法,通过重复使用一小块顶点缓存和索引缓存,结合不同的缩放和平移因子来渲染不同细节层次的瓦片块,实现了视点相关连续LOD动态地形可视化...     

浅析数字地形测量中的野外数据采集

大比例尺数字地形测量中,野外数据的采集非常关键,针对野外作业时出现的一些问题,从数字高程模型的建立,浅谈内外业一体化数字地形测量方法中野外数据采集时应注意的几个问题,并采用陕西眉县小法仪镇1:1000数字地形测量实例图进行图形对照说明。     

基于水下地形匹配大姿态误差角捷联惯导系统误差估计方法

传统的基于线性滤波的误差估计方法常采用基于咖角法的捷联惯导系统（SINS）线性误差模型,但当姿态误差角较大时,估计效果不稳定且精度较差。为此,提出了基于无迹卡尔曼滤波（UKF）的误差估计方法。通过建立水下地形匹配辅助导航系统非线性误差模型,以地形匹配和深度压力传感器测量的位置信息和深度作为量测量,设计了扩展状态UKF滤波器,在大姿态误差角下仿真研究了其估计效果。结果表明,在大姿态误差角下,本文所提出的方法可行且具有较好的估计效果,为匹配区内修正捷联惯导系统导航误差提供了参考。     

基于PMF和TERCOM组合算法的水下地形匹配技术

南于自主式水下航行器的导航受海水环境的影响,其定位精度不高,目前较好的解决方法之一是采用水下地形匹配辅助导航系统.本文针对该系统最核心部分的匹配算法,提出了一种定位精度更高的组合算法.为了解决质点滤波(PMF)算法存在的计算量大、收敛速度慢和对初始误差比较敏感的问题,提出了一种将地形轮廓匹配(TERCOM)算法和PMF算法进行组合的方案.首先分析了搜索区域分辨率对PMF算法的影响,然后分别对以TERCOM为粗匹配、PMF为精匹配和以PMF为粗匹配、TERCOM为精匹配的两种组合算法进行了仿真比较.结果表明,第1种组合算法无论在大起伏地形还是在平坦地形中都显示出了更快的收敛性和更高的定位精度,是一种具有较高工程应用价值的算法.     

剖切的力量：自然场地的认知与表达

剖切,对场地的探索具有巨大的潜力。从1807年洪堡的“自然之图”,到1909年苏格兰规划大师盖迪斯的“峡谷剖面”,再到美国宾夕法尼亚大学阿努教授在剖切研究上的一系列创新实践,全球学者在剖切方面的探索一直不断演进。如何引导学生认知自然场地,引发对场地自然属性与社会关系的相互关联的思考,培养跨尺度的思维,成为风景园林专业教师面临的挑战。以中国美术学院建筑艺术学院风景园林系的专业开端课程为例,通过在梅家坞开展的一系列以“剖切”为核心的场地教学,使学生加深对场地的理解。在教学中发现,场地实地测量,强调基于身体、感受和知觉的观察与分析方法,把场地地形、植被等与身体感知相联系的可视化研究等,对于场地认知有着巨大的帮助。由此探索在自然场地中处理地形、土地和土壤的生态实践智慧,以期为中国的风景园林教育提供借鉴。     

某山谷型尾矿库提高库容利用率的方法探析

介绍了一个山谷型尾矿库提高库容利用率的设计实例,通过对主坝坝前和库尾两个区域进行放矿方式优化,可以最大化地提高库容利用率,加大尾矿存储量,延长尾矿库的服务年限。在该工程的基础上,提出了放矿方式优化技术的适用库型条件,分析了平推放矿法、库两侧水下放矿法及支沟放矿法三种放矿方式优化措施的技术要点。     

Rigid multi-body kinematics of shovel crawler-formation interactions

Large capacity shovels are deployed in surface mining operations for achieving economic bulk production targets. These shovels use crawler tracks for effective terrain engagement in these environments. Shovel reliability, maintainability, availability and efficiency depend on the service life of the crawler tracks. In rugged and challenging terrains, crawler wear, tear, cracks and failure are extensive resulting in prolonged downtimes with severe economic implications. In particular, crawler shoe wear, tear, cracks and fatigue failures can be expensive in terms of maintenance costs and production losses. No fundamental research has been undertaken to understand the crawler-formation interactions in challenging and rugged terrains in surface mining operations. This study forms the foundations for providing long-term solutions to crawler failure problems. The kinematic equations governing the crawler-formation interactions have been formulated to characterise the crawler motions during shovel production. These equations capture the motions governing the link pin joint, oil sand terrain joint and driving constraints based on the multi-body rigid theory. Crawler propel is achieved by using prescribed velocities along a translational degree of freedom (DOF) and a translational and rotational DOF. The crawler kinematic solutions show that the 3-D crawler–terrain model results in 132 DOFs and requires dynamic modelling to obtain the unknown degrees of freedom. A 3-D virtual prototype model is built to capture the crawler-formation interaction in MSC ADAMS based on the rigid body crawler kinematics. The virtual prototype simulator is supplied with mass properties of crawler shoe, mass, stiffness and damping characteristics of oil sand and external loads due to machine weight and contact forces to obtain the time variation of position, velocity and acceleration for the crawler–terrain engagement for given driving constraints. The results from the driving constraints yield a non-linear longitudinal motion of the crawler track assembly. The crawler track lateral and vertical displacements during translation-only motion fluctuates with maximum magnitudes of 0.7 and 3.6 cm. Similarly the fluctuating longitudinal, lateral and vertical velocities and accelerations have maximum magnitudes of 0.22, 0.046 and 0.56 m/s and 7.41, 1.73, and 34.9 m/s², respectively. This research provides a strong foundation for further study on developing flexible crawler track model for predicting crawler shoes dynamic stress distributions, cracks development and propagation and fatigue analysis during shovel operations.     

面向飞行推演的双尺度地形特征提取方法研究

针对飞行推演应用中的海量地形数据建模问题,提出了一种新的双尺度地形特征提取方法。该方法将地形特征划分为大尺度地形特征和小尺度地形特征,根据应用需求,动态提取原始海量地形数据中的大、小两个尺度地形特征点集,形成高精度拟合原始地形的地形特征点集,增强了大场景地形特征建模的适应性和合理性。计算证明了本方法的有效性与先进性。     

基于多目标局部变异-自适应量子粒子群优化算法的复杂地形多传感器优化部署

对复杂地形下的多传感器部署问题进行研究,提出了基于多目标局部变异-自适应量子粒子群优化（LM-AQPSO）算法的多传感器多目标优化部署方法。该方法对复杂地形进行多属性网格建模,给出了传感器探测模型和优化目标。引进局部变异和参数自适应策略对量子粒子群优化算法进行改进,并提出了基于LM-AQPSO的多目标Pareto最优解集优化算法。考虑多目标部署需求,构建了基于Pareto最优解集的多传感器优化部署模型。仿真实验结果表明：相对于经典的改进非支配排序遗传算法,所提算法优化的Pareto最优解有着更好的收敛性和分布性,且寻优时间更短;所提模型能有效解决多目标多传感器部署问题,并能同时提供更多的决策方案。