Soft robots' ability to safely navigate complex environments motivates the development of algorithms for accurate environmental interaction assessment, enabling greater autonomy. Specifically, strain-based shape and force estimation of continuum robots with embedded soft sensors poses an open challenge mainly owing to continuous softness, anisotropic deformation, and non-linear properties. Mathematical description of deformable soft bodies and accurate estimation of external forces are crucial for achieving controllable and intelligent behaviors of these robots. In this paper, a kinetostatic strain-based modeling for rod-driven soft robots (RDSR) with embedded stretch sensors is proposed, which incorporates local strains, actuation variables, and external interactions. The strain model enables full shape estimation of the robot and prediction of strain variations in soft bodies. Building on this, we develop a force estimator based on predicted and measured sensor and actuator lengths to evaluate 3D external forces, accounting for both orthogonal and tangential components relative to the backbone. Moreover, we introduce a methodology using a novel ellipsoid representation to handle tangential forces that may become insensitive in certain singular configurations. This estimator allows us to either disregard such forces when they do not influence deformation or estimate them when they become observable. Our simulations and experiments demonstrate how this approach can be used to analyze the robot's configuration and successfully estimate external forces. Finally, it is demonstrated that when the continuum arm follows trajectories with higher strain sensitivity, tangential force estimation is significantly improved.

Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increasingly employed to operate in unstructured and unpredictable environments. Soft robots are inherently compliant to safely interact with environments but their high nonlinearity exacerbates control difficulties. Meta-learning provides a powerful tool for fast online model adaptation because it can learn an efficient model from data across different environments. Thus, this work applies the idea of meta-learning for the control of soft robotics. In particular, a target-oriented proactive search strategy is firstly performed to collect environment-specific data efficiently when a new interaction environment occurs. Then meta-learning exploits past experience to train a data-driven probabilistic model prior, and the model prior is online updated to be fast adapted to the new environment. Lastly, a model-based optimal control policy is utilized to drive the robot to desired performance. Our approach controls a soft robotic manipulator to achieve the desired position and contact force simultaneously when interacting with unknown changing environments. Overall, this work provides a viable control approach for soft robots to interact with unknown environments.

Colorectal cancer incidence has been steadily rising worldwide. Magnetic colonoscopes provide new approaches to conduct colon inspection and treatment. This paper presents a novel electromagnetically actuated soft-tethered colonoscope to achieve precise and stable orientation control. An inflated balloon is designed to eliminate the unpredictable disturbance of the floating tether. A 2OR Pseudo-Rigid-Body (PRB) model of the soft tether is developed to analyze the relationship between the tether deflection and applied force and torque. A closed-loop control framework is constructed with visual position feedback. Experiments are first conducted to validate the assumption of the PRB model and the efficacy of the magnetic field model. Then, trajectory tracking tasks and disturbance rejection tests are performed to validate the feasibility of the proposed solution and closed-loop control. Results show that the colonoscope can stably and accurately orient to the desired orientation with an absolute mean position error of less than 0.5 mm and an average velocity of 3.5 mm/s. The distal tip can quickly re-stabilize to the desired orientation even when a large disturbance exists.

Crawling through various terrains has been a long research interest. In recent years, quite a number of soft crawling robots have been developed. However, locomoting in an elastic, humid, and slippery environment remains a challenge. In nature, gastropods, such as snails, live in humid environment and could crawl through all kinds of surface conditions by using wet adhesion. In the wet adhesive locomotion, the mucus is crucial in adhering the gastropod while allowing forward motion. Previously, we presented one snail-like soft robot that mimics the gastropods. In this work, we propose a second version and present a theoretical model of the mucus simulant. In addition, the dynamic model of the soft robot’s wet adhesive locomotion is developed for the first time. Results show that the speed of the current version is 5 times than that of the previous one through the optimization of design. Also shown by the results that the mucus helps to speed up the robot by at least 2.7 times.