Vibration-Based Recognition of Wheel-Terrain Interaction for Terramechanics Model Selection and Terrain Parameter Identification for Lugged-Wheel Planetary Rovers

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel-terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel-terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel-terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel-terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.

Force Distribution and Estimation for Cooperative Transportation Control on Multiple Unmanned Ground Vehicles

This article presents an effective design of omnidirectional four-mecanum-wheeled vehicles to transport an object and track a predefined trajectory cooperatively. Furthermore, a novel design of the rotary platform is presented for multiple unmanned ground vehicles (m-UGVs) to load objects and provide better maneuverability in confined spaces during cooperative transportation. The number of unmanned ground vehicles (UGVs) is adjustable according to the object's weight and size in the proposed framework because transportation is accomplished without physical grippers. Moreover, to minimize the complexity in dealing with the interactive force between the object and UGVs, no force/torque sensor is used in the design of the control algorithm. Instead, an adaptive sliding-mode controller is formulated to cope with the dynamic uncertainties and smoothly transport an object along a desired trajectory. Thus, three external force analyses-gradient projection method, adaptive force estimation, and radial basis function neural network force estimation-are proposed for m-UGVs. In addition, the stability and the performance tracking of the m-UGV system in the presence of dynamic uncertainties using the proposed force estimation are investigated by employing the Lyapunov theory. Finally, experiments on cooperative transportation are presented to demonstrate the efficiency and efficacy of the m-UGV system.

Spatiotemporal Dynamics of the Human Critical Area (HCA) in the “Three Water Lines” Region of Northwest China and the Impact of Socioeconomic Factors between 2000 and 2020

The Human Critical Area (HCA) is an area that characterizes the surface landscape created by human beings in the Anthropocene. Based on the signatures left by major human activities over the Earth′s surface, this research demarcates an arid inland region of Northwest China, the “Three Water Lines”, into four HCA types: Agricultural Area, Built-up Area, Ecological Area, and Bare Area. This paper explores the HCA′s distribution and changes in the “Three Water Lines” region between 2000 and 2020 with land use/cover data, as well as the impact of socioeconomic factors on the HCA dynamics with statistics sourcing from authoritative yearbooks. To achieve this, the Land Use Transition Matrix is used to investigate the changes in area and distribution, while binary linear regression and stepwise multiple linear regression are applied to examine the single and joint effects of the socioeconomic factors. The main findings are as follows: (i) The four HCA types are distinguished quantitatively and by their distribution patterns. Ecological Area and Bare Area cover most (more than 90% in total) of the territory with extensive and continuous distribution. Agricultural Area is mainly found on the eastern and western parts of the region, with flat terrain, abundant water resources, and moderate temperatures. Built-up Area is the most concentrated but has an unbalanced distribution and the lowest quantity. (ii) Despite some discernible spatial and quantity changes at regional and county levels between 2000 and 2020, the general characteristics in HCA’s structure and distribution pattern have mainly remained consistent. (iii) Transitions between HCA types occur constantly, and the primary source type of the transitions differs from one another. Ecological Area and Bare Area form the sources of the most evident transitions. (iv) Agricultural Area and Built-up Area are more prone influence from some socioeconomic dynamics. By contrast, there is no evidence that socioeconomic factors directly affect Bare Area. As the first empirical study of the newly conceived concept, Human Critical Area, this paper sheds light on the renovation of geographic traditions of studying the evolution of the human-environment system through the lens of human activities-driven landscape changes.

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

Terrain Estimation for Planetary Exploration Robots

A planetary exploration rover’s ability to detect the type of supporting surface is critical to the successful accomplishment of the planned task, especially for long-range and long-duration missions. This paper presents a general approach to endow a robot with the ability to sense the terrain being traversed. It relies on the estimation of motion states and physical variables pertaining to the interaction of the vehicle with the environment. First, a comprehensive proprioceptive feature set is investigated to evaluate the informative content and the ability to gather terrain properties. Then, a terrain classifier is developed grounded on Support Vector Machine (SVM) and that uses an optimal proprioceptive feature set. Following this rationale, episodes of high slippage can be also treated as a particular terrain type and detected via a dedicated classifier. The proposed approach is tested and demonstrated in the field using SherpaTT rover, property of DFKI (German Research Center for Artificial Intelligence), that uses an active suspension system to adapt to terrain unevenness.

Kinematics-Based Simulation and Animation of Articulated Rovers Traversing Uneven Terrains

The paper develops a simulation and animation environment for high-mobility rovers based on kinematic modeling. Various kinematic chains starting from the rover body to the wheels are analyzed and aggregated to obtain the model of the rover body motion in terms of the wheel motions. This model is then used to determine the actuations of the joints, wheels speed, and steering motors to achieve a desired motion of the rover over uneven terrain while avoiding loss of balance and tip-over. The simulation environment consists of a number of modules, including terrain and trajectory generation, and kinematic models for rover actuation and navigation. The animation of the rover motion over various terrains is developed, which allows observing the rover from various viewpoints and interacting with the system through a graphical user interface. The performance of the overall system is demonstrated by modeling a high-mobility space exploration rover, and the responses of the rover on uneven terrains are provided, which show the usefulness of the proposed modeling, simulation, and animation scheme.

Recovery System Based on Exploration-Biased Genetic Algorithm for Stuck Rover in Planetary Exploration

Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit stuck areas, a kind of immobilized situation, by testing the explored behaviors. We develop a rover-type space probe, which has a stabilizer with two movable joints like an arm, and learns how to use them by employing EGA.

To evaluate the effectiveness of the recovery system based on the EGA, the following two field experiments are conducted with the proposed rover: (i) a small field test, including a stuck area created artificially in a park; and (ii) a large field test, including several stuck areas in Black Rock Desert, USA, as an analog experiment for planetary exploration. The experimental results reveal the following implications: (1) the recovery system based on the EGA enables our rover to exit stuck areas by an appropriate sequence of motions of the two movable joints; and (2) the success rate of getting out of stuck areas is 95% during planetary exploration.

Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments

The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.

Unique and accurate soil parameter identification for air-cushioned robotic vehicles

On-line identification of soil parameters is a pre-condition of operating performance optimization and control for unmanned ground vehicles (UGV). Inverse calculation from measured vehicular operating parameters is a prevalent methodology. However, it inherently suffers from a multiple-solution problem caused by the coupling of soil parameters in terramechanics equations and an accuracy problem caused by the influences of state noise and measurement noise. These problems in tractive-force-related soil parameters identification were addressed here for air-cushioned vehicles (ACV) by taking advantage of their additional degree of control freedom in vertical force. To be specific, a g-function algorithm was proposed to solve the multiple-solution problem from reproductive tractive force equations; de-noising techniques consisting of mean-effect strategies, sampling points selection and sample rearrangement were employed to solve the accuracy problem. A series of experiments were conducted to evaluate these techniques at different noise levels and in different soil conditions. They got satisfactory results in terms of data utilization ratio, identification accuracy and performance stability. The contribution of the paper lies in inventing a novel algorithm for unique and accurate identification of tractive-force-related soil parameters without making any simplification to the original terramechanics equation and with robustness to variations of noise level and soil condition.

Development of a Small, Lightweight Rover with Elastic Wheels for Lunar Exploration

Rovers are one of the most important vehicles used for conducting planetary exploration missions. This paper focuses on a small, lightweight rover that can be used for lunar exploration. It should be noted that, with a small rover, it is difficult to traverse loose soil such as that on the lunar surface. The rocks that cover the lunar surface, moreover, hinder the traversal of a small, lightweight rover. We develop a small, lightweight rover having 2 configurations to solve these tasks. One configuration involves the installation of elastic wheels whose 2 form changes depending on the surface that the rover traverses. The other configuration involves passive suspension using differential gears. We perform running experiments on rovers with these configurations. Experimental results prove that elastic wheels are more efficient than rigid wheels for traversing loose soil. We also found, moreover, that the proposed rover shows good climbing performance. We thus have shown the efficiency of the proposed small, lightweight rover in this study.

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction

This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown soil parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the soil parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified soil parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.