Lunar rock investigation and tri-aspect characterization of lunar farside regolith by a digital twin

Yutu-2 rover conducted an exciting expedition on the 41st lunar day to investigate a fin-shaped rock at Longji site (45.44°S, 177.56°E) by extending its locomotion margin on perilous peaks. The varied locomotion encountered, especially multi-form wheel slippage, during the journey to the target rock, established unique conditions for a fin-grained lunar regolith analysis regarding bearing, shear and lateral properties based on terramechanics. Here, we show a tri-aspect characterization of lunar regolith and infer the rock's origin using a digital twin. We estimate internal friction angle within 21.5°-42.0° and associated cohesion of 520-3154 Pa in the Chang'E-4 operational site. These findings suggest shear characteristics similar to Apollo 12 mission samples but notably higher cohesion compared to regolith investigated on most nearside lunar missions. We estimate external friction angle in lateral properties to be within 8.3°-16.5°, which fills the gaps of the lateral property estimation of the lunar farside regolith and serves as a foundational parameter for subsequent engineering verifications. Our in-situ spectral investigations of the target rock unveil its composition of iron/magnesium-rich low-calcium pyroxene, linking it to the Zhinyu crater (45.34°S, 176.15°E) ejecta. Our results indicate that the combination of in-situ measurements with robotics technology in planetary exploration reveal the possibility of additional source regions contributing to the local materials at the Chang'E-4 site, implying a more complicated geological history in the vicinity.

Autonomous robotic exploration with simultaneous environment and traversability models learning

In this study, we address generalized autonomous mobile robot exploration of unknown environments where a robotic agent learns a traversability model and builds a spatial model of the environment. The agent can benefit from the model learned online in distinguishing what terrains are easy to traverse and which should be avoided. The proposed solution enables the learning of multiple traversability models, each associated with a particular locomotion gait, a walking pattern of a multi-legged walking robot. We propose to address the simultaneous learning of the environment and traversability models by a decoupled approach. Thus, navigation waypoints are generated using the current spatial and traversability models to gain the information necessary to improve the particular model during the robot's motion in the environment. From the set of possible waypoints, the decision on where to navigate next is made based on the solution of the generalized traveling salesman problem that allows taking into account a planning horizon longer than a single myopic decision. The proposed approach has been verified in simulated scenarios and experimental deployments with a real hexapod walking robot with two locomotion gaits, suitable for different terrains. Based on the achieved results, the proposed method exploits the online learned traversability models and further supports the selection of the most appropriate locomotion gait for the particular terrain types.

Slip Estimation Model for Planetary Rover Using Gaussian Process Regression

Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models for China’s Mars rover, Zhurong, using Gaussian process regression (GPR). The model was able to predict not only the average value of the longitudinal (slip_x) and lateral slip (slip_y), but also the maximum possible value that slip_x and slip_y could reach. The training data were collected on two simulated soils, TYII-2 and JLU Mars-2, and the GA-BP algorithm was applied as a comparison. The analysis results demonstrated that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. The properties of the Martian soil near the Zhurong landing site were closer to the JLU Mars-2 simulated soil. The proposed GPR model had high estimation accuracy and estimation potential in slip value, and a 95% confidence interval that the rover could reach during motion. This work was part of a research effort aimed at ensuring the safety of Zhurong. The slip value may be used in subsequent path tracking research, and the slip confidence interval will be able to help guide path planning.

Slip Estimation for Mars Rover Zhurong Based on Data Drive

China’s Mars rover Zhurong successfully landed on Mars on 15th May 2021, and it is currently conducting an exploration mission on the Red Planet. This paper develops slip estimation models for the Mars rover Zhurong based on the data drive approach. Data were obtained by Zhurong’s validator ground indoor tests, and the test site was equipped with a low-gravity simulation device and simulated Mars soil to simulate the Mars conditions as much as possible. The obtained slip models trained by BP and GA-BP algorithms were applied to estimate Zhurong’s longitudinal (slip_x) and lateral slip (slip_y) on Mars, and the slip estimation values were used to display Zhurong’s actual driving path. The analyzed results prove that the GA-BP slip models perform better than the BP models, and can both be applied for correcting Zhurong’s path. The proposed models have high potential in guiding the path planning and monitoring of the slip for the Mars rover Zhurong.

A 2-year locomotive exploration and scientific investigation of the lunar farside by the Yutu-2 rover

The lunar nearside has been investigated by many uncrewed and crewed missions, but the farside of the Moon remains poorly known. Lunar farside exploration is challenging because maneuvering rovers with efficient locomotion in harsh extraterrestrial environment is necessary to explore geological characteristics of scientific interest. Chang'E-4 mission successfully targeted the Moon's farside and deployed a teleoperated rover (Yutu-2) to explore inside the Von Kármán crater, conveying rich information regarding regolith, craters, and rocks. Here, we report mobile exploration on the lunar farside with Yutu-2 over the initial 2 years. During its journey, Yutu-2 has experienced varying degrees of mild slip and skid, indicating that the terrain is relatively flat at large scales but scattered with local gentle slopes. Cloddy soil sticking on its wheels implies a greater cohesion of the lunar soil than encountered at other lunar landing sites. Further identification results indicate that the regolith resembles dry sand and sandy loam on Earth in bearing properties, demonstrating greater bearing strength than that identified during the Apollo missions. In sharp contrast to the sparsity of rocks along the traverse route, small fresh craters with unilateral moldable ejecta are abundant, and some of them contain high-reflectance materials at the bottom, suggestive of secondary impact events. These findings hint at notable differences in the surface geology between the lunar farside and nearside. Experience gained with Yutu-2 improves the understanding of the farside of the Moon, which, in return, may lead to locomotion with improved efficiency and larger range.

Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization

The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of ~97% over 650 m drives on rough terrain.

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.

An Estimator for the Kinematic Behaviour of a Mobile Robot Subject to Large Lateral Slip

In this paper, the effects of wheel slip compensation in trajectory planning for mobile tractor-trailer robot applications are investigated. Firstly, a kinematic model of the proposed robot architecture is marked out, then an experimental campaign is done to identify if it is possible to kinematically compensate trajectories that otherwise would be subject to large lateral slip. Due to the close connection to the experimental data, the results shown are valid only for Epi.q, the prototype that is the main object of this manuscript. Nonetheless, the base concept can be usefully applied to any mobile robot subject to large lateral slip.

Simulation Method for the Transport System of a Small-Sized Reconfigurable Mobile Robot

This study focuses on the features of transport (locomotion) systems of mobile mini-robots (MMR), i.e., small unmanned ground vehicles of a portable type measuring several tens of centimeters and weighing no more than 15 kg. A distinctive feature of the considered MMR is the possibility of its both structural reconfiguration (i.e., the ability to function in two options—tracked and wheeled) and geometric reconfiguration in the tracked option (i.e., chassis geometry variation). Thus, the transport system of such a mobile robot is divided into two components: a locomotion subsystem and a chassis geometry variation subsystem. The article examines the factors that are necessary for the correct mathematical description of such a small-sized and relatively high-speed transport system. A method for constructing a computer model of the transport system as an electromechanical device only is proposed. Such a computer model of the MMR transport system is developed for two types of chassis configurations: tracked and wheeled. The experimental studies performed and the comparison of the experimental and simulated data obtained showed their close convergence, within 5 to 7%. Thus, it is shown that the revealed features of the MMR transport systems along with the proposed method for their computer model development considering these features make it possible to increase the accuracy and adequacy of the MMR motion simulation in comparison with previously known approaches used in the computation of larger vehicles. The results obtained make it possible to consider the proposed computer model of the transport system as an electromechanical component of the complete model of a mobile robot.

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.

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.

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.