这篇论文为为穿越未知地面的 wheeled 车辆识别土壤参数论述一种新奇技术。识别土壤参数为预言车辆列车间的挂钩拉和车轮驱动器转矩被要求，它能接着在未知地面上被用于 wheeled 车辆的 traversability 预言，拖拉控制，和表演优化。建议技术基于牛顿 Raphson 方法。一个车轮土壤相互作用模型的一种接近的形式为这个目的基于合成 Simpson 鈥檚 统治被采用。要鉴别的关键土壤参数是内部磨擦角度，砍变丑模量，和 lumped 压力下沉系数。第四个参数，结合，不与车辆列车间的挂钩拉太相关，并且在鉴定进程期间被分配平均的值。识别参数与已知的价值相比，并且出现在同意。鉴定方法相对快、柔韧。识别土壤参数能有效地被用来与好精确性预言列车间的挂钩拉和车轮驱动器转矩。识别土壤参数的使用被介绍为穿越未知地面的 wheeled 车辆设计一个 traversability 标准。关键词土壤参数 - traversability - 牛顿 Raphson - 合成 Simpson 鈥檚 统治 - wheeled 车辆这个工作被 EPSRC 部分地支持(没有。GR/S31402/01 ) 。Suksun Hutangkabodee 在 1976 在泰国出生了。他收到了 B.Eng。在来自 Chulalongkorn 大学的 mechanical engineering，曼谷，在 1999 的泰国。他从国王鈥檚 学院伦敦，在电脑辅助的 mechanical engineering 收到了他的 M.Sc 学位在 2002 的英格兰，并且当前在一样的机构向一个博士正在工作。他的研究兴趣包括车辆地面相互作用动力学和土壤参数鉴定。Yahya Hashem Zweiri 获得了他的 B.Sc。在来自乔丹乔丹的大学的 Mechanical Engineering 的度和他在从国王鈥檚 学院伦敦，的柴油机引擎建模和评价的博士学位英格兰， 2003。他当前是在在国王鈥檚 学院伦敦的 Mechanical Engineering 的部门的一个研究家伙，研究无人的地面车辆的车辆地面相互作用动力学。他也是在 Mechanical Engineering 的部门的一个助�

Soil Parameter Identification for Wheel-terrain Interaction Dynamics and Traversability Prediction

Climbing control of autonomous mobile robot with estimation of wheel slip and wheel-ground contact angle

This work addresses the problem of mapping terrain features based on inertial and LiDAR measurements to estimate navigation cost, for an autonomous ground robot. The navigation cost quantifies the degree of how easy or difficult it is to navigate through different areas. Unlike most indoor applications, where surfaces are usually human-made, flat, and structured, external environments may be unpredictable as to the types and conditions of the travel surfaces, such as traction characteristics and inclination. Attaining full autonomy in outdoor environments requires a mobile ground robot to perform the fundamental localization and mapping tasks in unfamiliar environments, but with the added challenge of unknown terrain conditions. Autonomous motion in uneven terrain has been widely explored by the research community focusing on one or more of the several factors involved aiming at both safety and efficient displacement. A fuller representation of the environment is fundamental to increase confidence and to reduce navigation costs. To this end we propose a methodology composed of five main steps: (i) speed-invariant inertial transformation; (ii) roughness level classification; (iii) navigation cost estimation; (iv) sensor fusion through Deep Learning; and (v) estimation of navigation costs for untraveled regions. To validate the methodology, we carried out experiments using ground robots in different outdoor environments with different terrain characteristics. Results show that the inertial data transformation reduces the dispersion of signal magnitude for different speeds and scenarios. Meanwhile, the roughness level classification achieved a mean accuracy of 95.4%, for the speed of 0.6 m/s. Finally, the obtained terrain maps are a faithful representation of outdoor environments allowing accurate and reliable path planning.

Three-Dimensional Mapping with Augmented Navigation Cost through Deep Learning

In the present work, a new method for the determination of the outrigger loads in mobile cranes is proposed. The approach adopted assumes the mobile crane as a rigid body and the ground having a li...

A graphical approach for the determination of outrigger loads in mobile cranes

Simultaneous surface scanning and stability analysis of wheeled mobile robots using a new spatial sensitive shield sensor

In this paper, a new, simple and cheap sensor is proposed to detect the multipoint contact of a typical robotic wheel. The new sensor empowers a wheeled robot to scan the surface and to find stability margins during real-time locomotion without camera or laser sensors. Furthermore, it enhances the ability of the real-time solution of the dynamic equations. The new sensor is based on the total resistance of a circuit and can be classified into two types. In the first type, the resistive circuit includes a conductive path, a direct voltage source and some resistors by various values. The main advantage of this type of the new sensor is to determine the exact locations of multipoint contact only by means of an input data detecting the voltage of a resistor. The implementation of the new idea is easy to use and to be experimented. It can be used to improve the control process, especially on the rough surfaces and to enhance the locomotion stability. The second type of the new sensor contains a continuous resistive belt with higher accuracy than the first type. The algorithm of the multipoint contact detection is explained and the kinematics relations of the robot are obtained. The surface is scanned during the robot locomotion, and the error of the estimated surface profile is calculated. Finally, the static stability margins are extracted using the new sensor data.

A New Resistive Belt Sensor for Multipoint Contact Detection of Robotic Wheels

In this paper, a new adjustable segmented wheel is proposed whose platform is composed of a complex closed-loop scissor mechanism wrapped over adjustable spokes. The new platform composed of four proposed wheels is specified to avoid halting, to increase traction force and to adjust body orientation while interacting soft or composite terrains. The soil contact model (SCM), extended from Bekker’s theory is implemented to derive the wheel-soil interaction formulations. Furthermore, the Lagrange approach is used to derive the unmanned ground vehicle (UGV) dynamics including interaction forces. The best configurations of adjustable wheel are tabulated according to the terrain properties and the motion types. Finally, the proposed UGV is compared with a typical ordinary UGV to investigate traction forces and halting avoidance. Based on the simulation results and primary tests of the experimental setup, it can be inferred that this new mechanism can effectively adapt itself to different terrain conditions in order to pass the soft concave regions, climb the soft obstacles and move on the composite terrains.

New Adaptive Segmented Wheel for Locomotion Improvement of Field Robots on Soft Terrain

This research proposes a new extendable platform for an unmanned
ground vehicle to overcome the obstacle climbing issue The new platform
is basically established on scissor mechanism principles which have been
innovated to achieve long and rigid displacement A couple of scissor
mechanisms are embedded in the rover platform adjusting the mass center of rover respect to the rear and front wheels Accordingly, it yields geometric control of the contact forces, which can simultaneously reduce the
slip of the wheels and increase the performance of the obstacle climbing
up To demonstrate the performance of the proposed platform, the 3D
kinematics is derived Subsequently, the stick-slip Euler-Lagrange dynamics
is derived and a three-level controller including the torque optimization is
implemented to simulate the rover facing obstacles Finally, without any
hardware prototyping, the extendable rover is simulated and compared
with a typical fixed-geometry rover to show the enhancement of the
climbing ability by using the proposed concept Moreover, controlling the
normal contact forces of the wheels yields the slip reduction, which subsequently, increases the traction force

Expanding scissor-based UGV for large obstacles climbing

the first part of this paper presents an approach for characterization of the essential terra mechanic and dynamic parameters of the wide robotic wheels moving on soft soil using the soil contact model (SCM) method This method will be used in a complex optimization algorithm based on the direct search approach to find the optimized shape of the wheels whereby the rovers can properly steer and move The sinkage of the wheel, the lateral and longitudinal interaction forces, the pressure distribution and some new measures such as effective radius will be investigated as the optimization cost functions to achieve the elite shapes According to the results of this optimization, the complete spatial dynamics of a four-wheeled rover will be extracted in part II of this paper Using the optimized wheels, the rover will be empowered to rove on the rough and soft soil or sand Indeed, the main idea of the part I of this paper is to dynamically optimize the shape of the super wide wheels according to the terra mechanic principles of the wheel-soil interaction

Arman Mardani

Papers

Simultaneous surface scanning and stability analysis of wheeled mobile robots using a new spatial sensitive shield sensor

A New Resistive Belt Sensor for Multipoint Contact Detection of Robotic Wheels

New Adaptive Segmented Wheel for Locomotion Improvement of Field Robots on Soft Terrain

Expanding scissor-based UGV for large obstacles climbing

Terramechanics-based performance enhancement of the wide robotic wheel on the soft terrains, Part I: wheel shape optimization