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

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.

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.

Scientific exploration of challenging planetary analog environments with a team of legged robots

The interest in exploring planetary bodies for scientific investigation and in situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. In addition, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and postmission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25°, loose soil, and unstructured terrain, highlighting their advantages compared with wheeled rover systems. We successfully verified the approach in analog deployments at the Beyond Gravity ExoMars rover test bed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach.

Interactive method research of dual mode information coordination integration for astronaut gesture and eye movement signals based on hybrid model

The lightweight human-robot interaction model with high real-time, high accuracy, and strong anti-interference capability can be better applied to future lunar surface exploration and construction work. Based on the feature information inputted from the monocular camera, the signal acquisition and processing fusion of the astronaut gesture and eye-movement modal interaction can be performed. Compared with the single mode, the human-robot interaction model of bimodal collaboration can achieve the issuance of complex interactive commands more efficiently. The optimization of the target detection model is executed by inserting attention into YOLOv4 and filtering image motion blur. The central coordinates of pupils are identified by the neural network to realize the human-robot interaction in the eye movement mode. The fusion between the astronaut gesture signal and eye movement signal is performed at the end of the collaborative model to achieve complex command interactions based on a lightweight model. The dataset used in the network training is enhanced and extended to simulate the realistic lunar space interaction environment. The human-robot interaction effects of complex commands in the single mode are compared with those of complex commands in the bimodal collaboration. The experimental results show that the concatenated interaction model of the astronaut gesture and eye movement signals can excavate the bimodal interaction signal better, discriminate the complex interaction commands more quickly, and has stronger signal anti-interference capability based on its stronger feature information mining ability. Compared with the command interaction realized by using the single gesture modal signal and the single eye movement modal signal, the interaction model of bimodal collaboration is shorter about 79% to 91% of the time under the single mode interaction. Regardless of the influence of any image interference item, the overall judgment accuracy of the proposed model can be maintained at about 83% to 97%. The effectiveness of the proposed method is verified.