This article reviews recent advances in intelligent robotic ultrasound imaging systems. We begin by presenting the commonly employed robotic mechanisms and control techniques in robotic ultrasound imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. We conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives

The biological world serves as a vast reservoir of inspiration for human innovation, particularly in the realm of robotics designed to navigate intricate granular environments. Among these creatures adept at burrowing, the mole stands out due to its exceptional body structure and unparalleled efficiency, surpassing most engineering burrowing systems. Consequently, the mole serves as an ideal model for designing highly efficient burrowing robots. This study introduces a mole‐inspired robot burrowing with forelimbs, specifically designed for planetary soil exploration. By closely examining the distinctive body morphology of moles, a forelimb burrowing mechanism is devised through biomimetic mapping, and its kinematics is thoroughly analyzed. A robot prototype is developed and an experimental setup is constructed to evaluate its burrowing performance. A series of tests are conducted to assess the capabilities of the robot, including forelimb burrowing, robot crawling, and robot burrowing. The results demonstrate that the proposed mole‐inspired burrowing robot is capable of crawling and burrowing to a certain depth using its forelimbs. Although it exhibits some upward displacement, this issue can be mitigated by modifying the head configuration and adjusting the forelimb posture to effectively overcome the vertical stress and lift force exerted by granular soils due to intensity gradient.

Mole‐Inspired Robot Burrowing with Forelimbs for Planetary Soil Exploration

Lunar drilling is conducted in extreme environments, where limited heat dissipation pathways may cause overheating during drilling, potentially leading to destructive effects on the drill tool. To address the challenge of rapid temperature change in lunar regolith drilling, this paper presents a spatiotemporal feature fusion method for the real-time prediction of drilling temperature inside a lunar regolith simulant. The method aims to accurately predict the temperature of eight positions on the drill tool in the next second based on seven state parameters during drilling from the previous 20 seconds. It utilizes deep learning techniques to combine the Bi-LSTM and self-attention mechanism to fully exploit past and future information to extract spatiotemporal features from time series data and focus on the crucial features for temperature prediction. Additionally, a proposed early stopping-Bayesian hyperparameter optimization method is incorporated to effectively adjust the hyperparameters of the model. Experimental results demonstrate the superior performance of this method on data from drilling experiments with lunar regolith simulant in a vacuum chamber, significantly outperforming other models in statistical terms and meeting real-time prediction requirements. Furthermore, it exhibits robustness and generalization capability in practical applications on two different robotic drills. This research provides reliable support for predicting drilling temperature in future lunar exploration missions and reveals the spatiotemporal relationship in the thermophysical field during drilling. It holds crucial significance in guiding mission planners and operators to proactively implement appropriate measures in advance, ensuring the smooth operation of the drilling apparatus and comprehending the generation, conduction, and dissipation mechanisms of heat during lunar regolith drilling. 

Real-time temperature prediction of lunar regolith drilling based on ATT-Bi-LSTM network

Abstract The Lunar Instrumentation for Subsurface Thermal Exploration with Rapidity (LISTER) is the first robotically deployed subsurface thermal probe that actively excavates lunar regolith and makes scientific measurements at multiple depths. It was deployed in Mare Crisium on Blue Ghost Mission 1 in 2025 March, reached 1 m depth, and measured the temperature and thermal conductivity of the regolith at eight different depths. The previous depth record for robotic in situ thermal measurements of the Moon was 8 cm. LISTER utilizes the pneumatic excavation technique, blowing a jet of pressurized nitrogen gas, as it pushes its thermal probe into the regolith. The gas expands rapidly in the lunar vacuum and lofts regolith clumps and clasts out of the hole. When the probe reaches one of the depths targeted for thermal measurements, LISTER stops blowing gas and pushes the probe into the yet-to-be-excavated bottom-hole regolith. It completes the necessary thermal measurements in 2 hr and then resumes excavation to the next targeted depth. LISTER detects obstacles on its way by monitoring the weight on bit (WOB). When the WOB exceeds a programmable threshold, LISTER attempts to dislodge the obstacle by automatically performing a piston-like vibratory movement (“dithering”), while it keeps blowing gas. Finally, video-monitoring the ejecta from LISTER’s excavation operations is useful in studying the stratigraphy of the regolith. 

The Lunar Instrumentation for Subsurface Thermal Exploration with Rapidity (LISTER) on Blue Ghost Mission 1 to Mare Crisium. I. Pneumatic Excavation of Regolith

Numerical investigation of water ice sublimation in the lunar soil during the drilling, brushing and transfer process

On December 2, 2020, a 2-m class robotic drill onboard the Chinese Chang’E 5 lunar lander successfully penetrated 1 m into the lunar regolith and collected 259.72 g of samples. This paper presents the design and development, terrestrial tests, and lunar sampling results of the robotic drill. First, the system design of the robotic drill, including its engineering objectives, drill configuration, drilling and coring methods, and rotational speed determination, was studied. Subsequently, a control strategy was proposed to address the geological uncertainty and complexity of the lunar surface. Terrestrial tests were conducted to assess the sampling performance of the robotic drill under both atmospheric and vacuum conditions. Finally, the results of drilling on the lunar surface were obtained, and the complex geological conditions encountered were analyzed. The success of the Chinese Chang’E 5 lunar sample-return mission demonstrates the feasibility of the proposed robotic drill. This study can serve as an important reference for future extraterrestrial robotic regolith-sampling missions.

Robotic drilling for the Chinese Chang’E 5 lunar sample-return mission

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Robotic drilling for the Chinese Chang’E 5 lunar sample-return mission