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.     

Gyro-based odometry associated with steering characteristics for wheeled mobile robot in rough terrain

Mobile robot used for planetary exploration performs several scientific missions over long distance travel and needs to have a high degree of autonomous mobility system because the communication delay from the Earth impedes its direct teleoperation. Localization of a mobile robot is of particular importance on the autonomous mobility. Classical localization methods such as wheel/visual odometry have been widely investigated and demonstrated, but they possess a well-known trade-off between computational cost and localization accuracy. This paper proposes an accurate gyro-based odometry method for a wheeled mobile robot in rough terrain. The robot in rough terrain is often subject to large wheel slip or vehicle sideslip related with its steering maneuver, and those slips degrade the localization accuracy. The basic approach of the proposed method is to exploit odometry data for the robot distance traveled as well as gyroscope data for the robot heading calculation; however each data-set is weighted in accordance with steering characteristics of a robot in rough terrain. The usefulness of the proposed method is examined through field experiments using a wheeled mobile robot testbed in Martian analog site. The experimental result confirms that the proposed method accurately estimates the robot trajectory.     

Estimation of Terrain Forces and Parameters for Rigid-Wheeled Vehicles

This paper provides a methodology for the estimation of resistance, thrust, and resistive torques on each wheel of a rigid-wheeled vehicle generated at the vehicle–terrain interface, and from these forces and moments, a methodology to estimate terrain parameters is presented. Terrain force estimation, which is independent of a terrain model, can infer the ability to accelerate, climb, or tow a load independent of the underlying terrain properties. When a terrain model is available, parameters of that model, such as soil cohesion, friction angle, maximum normal stress, and stress distribution parameters, are determined from estimated vehicle–terrain forces using a multiple-model estimation approach, providing parameters that relate to accepted mobility metrics. The methodology requires a standard proprioceptive sensor suite—accelerometers, rate gyros, wheel speeds, motor torques, and ground speed. Sinkage sensors are not required. Simulation results demonstrate efficacy of the method on three terrains spanning a range of soil cohesions reported in the literature.      

Design,Development, and Mobility Evaluation of an Omnidirectional Mobile Robot for Rough Terrain

Omnidirectional vehicles have been widely applied in several areas, but most of them are designed for the case of motion on flat, smooth terrain, and are not feasible for outdoor usage. This paper presents the design and development of an omnidirectional mobile robot that possesses high mobility in rough terrain. The omnidirectional robot consists of a main body with four sets of mobility modules, called an active split offset caster (ASOC). The ASOC module has independently driven dual wheels that produce arbitrary planar translational velocity, enabling the robot to achieve its omnidirectional motion. Each module is connected to the main body via a parallel link with shock absorbers, allowing the robot to conform to uneven terrain. In this paper, the design and development of the ASOC‐driven omnidirectional mobile robot for rough terrain are described. A control scheme that considers the kinematics of the omnidirectional mobile robot is presented. The mobility of the robot is also evaluated experimentally based on a metric called the ASOC mobility index. The mobility evaluation test clarifies a design tradeoff between terrain adaptability and omnidirectional mobility due to the shock absorbers. In addition, an odometry improvement technique that can reduce position estimation error due to wheel slippage is proposed. Experimental odometry tests confirmed that the proposed technique is able to improve the odometry accuracy for sharp‐turning maneuvers.     

Control of Robotic Vehicles with Actively Articulated Suspensions in Rough Terrain

Future robotic vehicles will perform challenging tasks in rough terrain, such as planetary exploration and military missions. Rovers with actively articulated suspensions can improve rough-terrain mobility by repositioning their center of mass. This paper presents a method to control actively articulated suspensions to enhance rover tipover stability. A stability metric is defined using a quasi-static model, and optimized on-line. The method relies on estimation of wheel-terrain contact angles. An algorithm for estimating wheel-terrain contact angles from simple on-board sensors is developed. Simulation and experimental results are presented for the Jet Propulsion Laboratory Sample Return Rover that show the control method yields substantially improved stability in rough-terrain.     

Visual Contact Angle Estimation and Traction Control for Mobile Robot in Rough-Terrain

For a wheeled mobile robot traversing a rough terrain, knowledge of terrain variables is very important for developing effective traction control algorithms. A key variable of the most prevalent information that should be taken into account is the contact angle between the robot wheels and the ground. This paper presents an algorithm for visual estimation of wheel-ground contact angle on uneven terrain. We call it the Visual Contact Angle Estimation (VCAE) method. Given a white LED light source, a monocular camera is required to be mounted on the front wheel and the rear wheel respectively, with a field of view containing the wheel-ground contact interface and its location relative to the wheel is known and fixed during robot travel. This arrangement is used to measure the contact angle with an edge detection strategy. Then a traction control methodology based on multi-objective optimization is presented. This exploits the wheel-ground contact angle obtained in the VCAE system to improve ground traction and reduce power consumption. Simulation and experiment results for a wheeled robot traversing a symmetrical uneven testbed demonstrate the effectiveness of the VCAE method and traction control algorithms.     

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。     

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.     

Rough terrain mapping and classification for foothold selection in a walking robot

Although legged locomotion over a moderately rugged terrain can be accomplished by employing simple reactions to the ground contact information, a more effective approach, which allows predictively avoiding obstacles, requires a model of the environment and a control algorithm that takes this model into account when planning footsteps and leg movements. This article addresses the issues of terrain perception and modeling and foothold selection in a walking robot. An integrated system is presented that allows a legged robot to traverse previously unseen, uneven terrain using only onboard perception, provided that a reasonable general path is known. An efficient method for real‐time building of a local elevation map from sparse two‐dimensional (2D) range measurements of a miniature 2D laser scanner is described. The terrain mapping module supports a foothold selection algorithm, which employs unsupervised learning to create an adaptive decision surface. The robot can learn from realistic simulations; therefore no a priori expert‐given rules or parameters are used. The usefulness of our approach is demonstrated in experiments with the six‐legged robot Messor. We discuss the lessons learned in field tests and the modifications to our system that turned out to be essential for successful operation under real‐world conditions. © 2011 Wiley Periodicals, Inc.     

Path planning for a tracked robot traversing uneven terrains based on tip-over stability

In the real-world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability-based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high-stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability-based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability-based path planning framework can effectively plan the high-quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.     

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.     

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．     

CAMPOUT: a control architecture for tightly coupled coordination of multirobot systems for planetary surface exploration

Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.     

An Efficient Algorithm for Identification of Robot Parameters Including Drive Characteristics

A procedure for estimating the dynamic model parameters of an n degree-of-freedom (DOF) robot is presented. The characteristics of the drive dynamics are included in the modelling process. In this manner, the estimated dynamic model can facilitate the design of control laws for actual implementation. Due to a regrouping procedure, the estimation model for the model parameters is formulated in an upper block triangular form. This special structure can be exploited to obtain a computational efficient estimation algorithm for the model parameters. An integral form of the estimation algorithm is then developed to eliminate the need of using joint accelerations which tend to be noisy. The complete procedure does not require prior knowledge of the manipulator's geometric parameters. Furthermore the robot manipulator needs not follow some restrictive test trajectories. The experimental results obtained from a 4-DOF SCARA robot are presented to illustrate the practical application of the method.     

Mobility characterization for autonomous mobile robots using machine learning

This paper presents a supervised learning approach to improving the autonomous mobility of wheeled robots through sensing the robot’s interaction with terrain ‘underfoot.’ Mobility characterization is cast as a hierarchical task, in which pre-immobilization detection is achieved using support vector machines in time to prevent full immobilization, and if a pre-immobilization condition is detected, the associated terrain feature affecting mobility is identified using a Hidden Markov model. These methods are implemented using a hierarchical, layered control scheme developed for the Yeti robot, a 73-kg, four-wheeled robot designed to perform autonomous medium-range missions in polar terrain. The methodology is motivated by the difficultly of visually recognizing terrain features that impact mobility in low contrast terrain. The efficacy of the approach is evaluated using data from a suite of proprioceptive sensors. Real-time implementation shows that Yeti can consistently detect pre-immobilization conditions, stop in time to avoid unrecoverable immobilization, identify the terrain feature presenting the mobility challenge, and execute an escape sequence to retreat from the condition.     

Progress towards robotic exploration of extreme terrain

A high degree of mobility, reliability, and efficiency are needed for autonomous exploration of extreme terrain. These requirements have guided the development of the Ambler, a six-legged robot designed for planetary exploration. To address issues of efficiency and mobility, the Ambler is configured with a stacked arrangement of orthogonal legs and exhibits a unique circulating gait, where trailing legs recover directly from rear to front. The Ambler is designed to stably traverse a 30 degree slope while crossing meter sized features. The same three principles have provided many constraints on the design of a software system that autonomously navigates the Ambler through natural terrain using 3-D perception and a combined deliberative/reactive architecture. The software system has required research advances in real-time control, perception of rugged terrain, motion planning, task-level control, and system integration. This paper presents many of the factors that influenced the design of the Ambler and its software system. In particular, important assumptions regarding the mechanism, perception, planning, and control are presented and evaluated in light of experimental and theoretical research of this project.     

Navigating a mobile robot by a traversability field histogram.

This paper presents an autonomous terrain navigation system for a mobile robot. The system employs a two-dimensional laser range finder (LRF) for terrain mapping. A so-called "traversability field histogram" (TFH) method is proposed to guide the robot. The TFH method first transforms a local terrain map surrounding the robot's momentary position into a traversability map by extracting the slope and roughness of a terrain patch through least-squares plane fitting. It then computes a so-called "polar traversability index" (PTI) that represents the overall difficulty of traveling along the corresponding direction. The PTIs are represented in a form of histogram. Based on this histogram, the velocity and steering commands of the robot are determined. The concept of a virtual valley and an exit condition are proposed and used to direct the robot such that it can reach the target with a finite-length path. The algorithm is verified by simulation and experimental results.     

Development of snake-like robot ACM-R8 with large and mono-tread wheel

One of the important advantages of an active wheeled snake-like robots is that it can access narrow spaces which are inaccessible to other types of robot (such as crawlers, walking robots), since snake-like robots have an elongated, narrow body. Additionally, in areas with rubble, snake-like robots can traverse rough terrain and large obstacles since its body can conform to the terrain’s contours. ‘ACM-R8’ is a new snake-like robot which can climb stairs and reach doorknobs in addition to the features explained above. To fulfill these functions, the design of this robot incorporates several key features: joints with parallel link mechanism, mono-tread wheels with internal structure, force sensors and ‘swing-grousers’ which were developed to improve step climbability. In this paper, the design and control methods are described. Experiments confirmed high mobility on stairs and steps, with the robot succeeding in overcoming a step height of 600 mm, despite the height of the robot being just 300 mm.     

Robust estimation of walking robots velocity and tilt using proprioceptive sensors data fusion

Availability of the instantaneous velocity of a legged robot is usually required for its efficient control. However, estimation of velocity only on the basis of robot kinematics has a significant drawback: the robot is not in touch with the ground all the time, or its feet may twist. In this paper we introduce a method for velocity and tilt estimation in a walking robot. This method combines a kinematic model of the supporting leg and readouts from an inertial sensor. It can be used in any terrain, regardless of the robot’s body design or the control strategy applied, and it is robust in regard to foot twist. It is also immune to limited foot slide and temporary lack of foot contact.     

基于自学习中枢模式发生器的仿人机器人适应性行走控制

为了克服传统中枢模式发生器(Central pattern generator, CPG)关节空间控制方法的复杂性和局限性, 本文基于自学习中枢模式发生器模型, 提出了一套在线调制和融合多传感器信息的仿人机器人环境自适应行走控制方法.算法难点在于如何在机器人的工作空间将自学习CPG用于工作空间轨迹生成, 并使CPG参数直接和步态模式相关联.本文提出了利用自学习CPG来学习和实时生成机器人质心轨迹和脚掌轨迹的方法, 在线调节机器人步长、抬腿高度和步行速度等关键参数.参考生物反射行为, 利用传感反馈信息激发CPG以产生具有环境适应性的工作空间轨迹, 提升行走质量. 控制系统的参数通过优化算法来进一步改善行走性能.相比于传统的CPG关节空间法, 本文所采用的自学习CPG工作空间法不仅极大简化了CPG网络结构而且提高了仿人机器人行走的适应性.最后, 通过仿人机器人坡面适应性行走的仿真和实验, 验证了所提出控制策略的可行性和有效性.