Colonoscopy is a medical procedure used to examine the inside of the colon for abnormalities, such as polyps or cancer. Traditionally, this is done by manually inserting a long, flexible tube called a colonoscope into the colon. However, this method can cause pain, discomfort, and even the risk of perforation. To address these shortcomings, advancements in technology are needed to develop safer, more intelligent colonoscopes. This paper presents the design, control and evaluation of a self-growing soft robotic colonoscope, leveraging the evertion principle. The device features a tube with an 18 mm diameter, constructed from stretchable fabric, which grows 1.6 m at the tip under pressurization. A pneumatically driven, elastomer-based manipulator enables omni-directional steering over$180^\circ$at the tip. An airtight base houses motors and spools that control the material and regulate growth speed. The robot operates in two modes: teleoperation via joysticks and autonomous navigation using sensor inputs, such as a tip-mounted camera. Thoroughin-vitroexperiments are conducted to assess the system's functionality and performance. Results illustrate that the robot can achieve locomotion in confined spaces such as a colon phantom, while exerting contact forces averaging less than 0.3 N. Our soft robot shows potential for improving the safety and autonomy of colonoscopies, while reducing discomfort to patients.

Soft continuum robots, fabricated from elastomeric materials, offer unparalleled flexibility and adaptability, making them ideal for applications such as minimally invasive surgery and inspections in constrained environments. With the miniaturization of imaging technologies and the development of novel control algorithms, these devices provide exceptional opportunities to visualize the internal structures of the human body. However, there are still challenges in accurately estimating external forces applied to these systems using current technologies. Adding additional sensors is challenging without compromising the softness of the device. This work presents a visual deformation-based force sensing framework for soft continuum robots. The core idea behind this work is that point loads lead to unique deformation profiles in an actuated soft-bodied robot. We introduce a Convolutional Neural Network-based tip force estimation method that utilizes arbitrarily placed camera images and actuation inputs to predict applied tip forces. Experimental validation was performed using the STIFF-FLOP robot, a pneumatically actuated soft robot developed for minimally invasive surgery. Our vision-based force estimation model demonstrated a sensing precision of 0.05 N in the XY plane during testing, with data collection and training taking only 70 minutes.

Soft continuum robots, characterized by their inherent compliance and dexterity, are increasingly pivotal in applications requiring delicate interactions with the environment such as the medical field. Despite their advantages, challenges persist in accurately modeling and controlling their shape during interactions with surrounding objects. This is because of the difficulty in modeling the large degrees of freedom in soft-bodied objects that become more active during interactions. In this study, we present a deep visual model to predict the interaction shapes of a soft continuum robot in contact with surrounding objects. By formulating this task as a forward-statics problem, the model uses the initial state images containing the object configuration and future actuation values to predict interactive state images of the robot under this actuation condition. We developed and tested the model in both simulated and physical environments, explored the model’s predictive capabilities using monocular and binocular views, and tested the model’s generalization ability on different datasets. Our results show that deep learning methods are a promising tool for solving the complex problem of predicting the shape of a soft continuum robot interacting with the environment, requiring no prior knowledge about the system dynamics and explicit mapping of the environment. This study paves the way for future explorations in robot-environment interaction modeling and the development of more adaptable interaction shape control strategies.

Soft robotic structures may play a major role in the 4th industrial revolution. Researchers have successfully demonstrated the advantages of soft robotics over traditional robots made of rigid links and joints in many application areas. Variable stiffness links (VSL) and joints (VSJ) have been investigated to achieve on-demand forces and, at the same time, be inherently safe in interactions with humans. However, a thorough characterisation of soft and rigid robotic components is still required. This paper investigates the influence of antagonistically actuated, stiffness-controllable joint-link units (JLUs) on the performance of collaborative robots (i.e. stiffness, load capacity, repetitive precision) and characterizes the difference compared with rigid units. A JLU is made of a combination of a VSL, a VSJ, and their rigid counterparts. Experimental results show that the VSL has minor differences in terms of stiffness (0.62 ∼ 0.95), output force (0.93 ∼ 0.94), and repetitive precision compared with the rigid link. For the VSJ, our results show a significant gap compared with the servo motor with regards to maximum stiffness (0.14 ∼ 0.21) and repetitive position precision (0.07 ∼ 0.25). However, similar performance on repetitive force precision and better performance on the maximum output force (1.54 ∼ 1.55 times) are demonstrated.

Soft continuum robots are highly flexible and adaptable, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and maneuverability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space control of the end-effector, but few works have explored full-body control. This study presents a novel image-based deep learning approach for closed-loop kinematic shape control of soft continuum robots. The method combines a local inverse kinematics formulation in the image space with deep convolutional neural networks for accurate shape control that is robust to feedback noise and mechanical changes in the continuum arm. The shape controller is fast and straightforward to implement; it takes only a few hours to generate training data, train the network, and deploy, requiring only a web camera for feedback. This method offers an intuitive and user-friendly way to control the robot's 3-D shape and configuration through teleoperation using only 2-D hand-drawn images of the desired target state without the need for further user instruction or consideration of the robot's kinematics.

Soft robotic manipulators have been created and investigated for a number of applications due to their advantages over rigid robots. In minimally invasive surgery, for instance, soft robots have successfully demonstrated a number of benefits due to the compliant and flexible nature of the material they are made of. However, these type of robots struggle with performing tasks that require on-demand stiffness i.e. exerting higher forces to the surrounding environment. A number of semi-active and active mechanisms have been investigated to change and control the stiffness of soft robotic manipulators. Embedding these mechanisms in soft manipulators for spacerestricted applications can be challenging though.To better understand the inherent passive stiffness properties of soft manipulators, we propose a screw theory-based stiffness analysis for fluidic-driven continuum soft robotic manipulators. First, we derive the forward kinematics based on a parameter-based piece-wise constant curvature model. It is worth noting, our stiffness analysis can be conducted based on any freespace forward kinematic model. Then our stiffness analysis and mapping methodology is conducted based on screw theory. Initial results of our approach demonstrate the feasibility comparing computational and experimental data.

A dynamic model of a soft fibre-reinforced fluidic elastomer is presented and experimentally verified, which can be used for model-based controller design. Due to the inherent visco-(hyper)elastic characteristics and nonlinear time-dependent behaviour of soft fluidic elastomer robots, analytic dynamic modelling is challenging. The fibre reinforced noninflatable soft fluidic elastomer robot used in this paper can produce both planar and spatial movements. Dynamic equations are developed for both cases. Parameters, related to the viscoelastic behaviour of the robot during elongation and bending motion, are identified experimentally and incorporated into our model. The modified dynamic model is then validated in experiments comparing the time responses of the physical robot with the corresponding outputs of the simulation model. The results validate the accuracy of the proposed dynamic model.

Due to increasing complexity of systems in industrial applications, decoupling control of interconnected or cascaded systems has been discussed a lot recent years. A traditional way to deal with the system is to decouple the system states so that the states of each subsystem can be dealt with independently. This works theoretically well in most cases, however, for systems with high dimensions or with constraints on specific subsystems, this method may not be efficient or flexible enough in practical applications. In view of this, this paper proposes a backpropagation algorithm for decoupling control of a linear time invariant cascaded system with inner feedback loops, which simplifies the complicated system into several simpler ones, so that the system can be dealt with more efficiently and flexibly. Accordingly, the stability of the system is analyzed in a feedforward way, establishing the sufficient as well as necessary conditions for asymptotic stability of the system. Simulation experiment on the steering module of a steer-by-wire system verifies the effectiveness of the proposed algorithm. Finally, future researches following this paper are pointed out to perfect the algorithm proposed.