Billions of vascular access procedures are performed annually worldwide, serving as a crucial first step in various clinical diagnostic and therapeutic procedures. For pediatric or elderly individuals, whose vessels are small in size (typically 2 to 3 mm in diameter for adults and <1 mm in children), vascular access can be highly challenging. This study presents an image-guided robotic system aimed at enhancing the accuracy of difficult vascular access procedures. The system integrates a 6-DoF (Degrees of Freedom) robotic arm with a 3-DoF end-effector, ensuring precise navigation and needle insertion. Multi-modal imaging and sensing technologies have been utilized to endow the medical robot with precision and safety, while ultrasound (US) imaging guidance is specifically evaluated in this study. To evaluate in vivo vascular access in submillimeter vessels, we conducted ultrasound-guided robotic blood drawing on the tail veins (with a diameter of 0.7 ± 0.2 mm) of 40 rats. The results demonstrate that the system achieved a first-attempt success rate of 95%. The high first-attempt success rate in intravenous vascular access, even with small blood vessels, demonstrates the system's effectiveness in performing these procedures. This capability reduces the risk of failed attempts, minimizes patient discomfort, and enhances clinical efficiency.

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.Note to Practitioners—This work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each approach are summarized, and we discuss the possible future of this area.

Deformable object manipulation is a challenging problem due to its complex deformable properties. With the development of artificial intelligence, learning-based methods have shown outstanding performance in robotic manipulation. Previous works have investigated the manipulation of deformable objects via Reinforcement Learning (RL) in simulation. However, they approximate object deformation with particles, using particle states as observations, which are unavailable in reality. To address these issues, we utilize Vision-Based Tactile Sensors (VBTSs) as the end-effector to manipulate and observe the deformable objects. In this work, we develop a new contact simulation environment for deformable objects, including elastic, plastic, and elastoplastic. We utilize RL strategies and expert demonstrations to train agents in the simulation. Finally, we build a real experimental platform to complete the sim-to-real tasks and robustness testing. Our work introduces an innovative strategy that utilizes high-resolution VBTSs for contact simulation and manipulation of deformable objects. The experimental results show superior performances of deformable object manipulation with the proposed method.

Modular soft robot arms (MSRAs) are composed of multiple modules connected in a sequence, and they can bend at different angles in various directions. This capability allows MSRAs to perform more intricate tasks than single-module robots. However, the modular structure also induces challenges in accurate planning and control. Nonlinearity and hysteresis complicate the physical model, while the modular structure and increased DOFs further lead to cumulative errors along the sequence. To address these challenges, we propose a versatile configuration space planning and control strategy for MSRAs, named$S2C2A$(State to Configuration to Action). Our approach formulates an optimization problem,$S2C$(State to Configuration planning), which integrates various loss functions and a forward model based on biLSTM to generate configuration trajectories based on target states. A configuration controller$C2A$(Configuration to Action control) based on biLSTM is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. We validate our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position and orientation control and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work focuses on addressing MSRA challenges, such as more accurate physical models.

Endoscopic Submucosal Dissection (ESD) is a minimally invasive procedure designed to remove precancerous and cancerous lesions from the gastrointestinal (GI) tract. Given the GI tract’s tortuous and narrow shape, along with the need for varied movements during dissection, this requires highly flexible and compact instruments, making flexible continuum robots suitable candidates. In this paper, we propose a novel two-segment continuum robot system named DESectBot, featuring a diameter of 5.5 mm and a total length of the active bending module of 48 mm, while the robot’s total length exceeds 1 m. We designed a novel joint combination structure called the spatial cross-curved disk skeleton for the robot, which addresses the mechanical coupling problem between flexible robot actuators. The DESectBot boasts six degrees of freedom, and its kinematic modeling has been derived and utilized in the closed-loop control of the DESectBot. The validation of the DESectBot was conducted through a two-stage test: first, the decoupling performance of the DESectBot was validated. The results show that when one active bending segment bends, the other segment remains almost uninfluenced, with a maximum variation of 1.15 degrees, demonstrating the robot’s effective decoupling capability. Secondly, the accuracy of DESectBot was validated through trajectory-following experiments. The results reveal that the average tracking error for both trajectories is less than 2 mm, and the maximum tracking error is below 2.5 mm. Taking marking, one of the ESD procedures with a 5mm tolerance, as an example, the DESectBot has the potential to be utilized for ESD procedure.