Humans can hold a live animal like a hamster without overly squeezing despite the fact that its soft body undergoes impedance and size variations due to breathing and wiggling. Although the exact nature of such biological motor controllers is not known, existing literature suggests that they maintain metastable interactions with dynamic objects based on prediction rather than reaction. Most robotic gripper controllers find such tasks very challenging mainly due to hard constraints imposed on the stability of closed-loop control and inadequate rates of convergence of adaptive controller parameters. This paper presents experimental and numerical simulation results of a control law based on a relaxed stability criterion of reducing the probability of failure to maintain a stable grip on a soft object that undergoes temporal variations in its internal impedance. The proposed controller uses only three parameters to interpret the probability of failure estimated using a history of grip forces to adjust the grip on the dynamic object. Here, we demonstrate that the proposed controller can maintain smooth and stable grip tightening and relaxing when the object undergoes random impedance variations, compared with a reactive controller that involves a similar number of controller parameters.

This paper presents an innovative approach of utilising visual feedback to determine physical interaction forces with soft tissue during Minimally Invasive Surgery (MIS). This novel force sensing device is composed of a linear retractable mechanism and a spherical visual feature. The sensor mechanism can be adapted to endoscopic cameras used in MIS. As the distance between the camera and feature varies due to the sliding joint, interaction forces with anatomical surfaces can be computed based on the visual appearance of the feature in the image. Hence, this device allows the measurement of forces without introducing new stand-alone sensors. A mathematical model was derived based on validation data tests and preliminary experiments were conducted to verify the model's accuracy. Experimental results confirm the effectiveness of our vision based approach.

Robotic manipulators for Robot-assisted Minimally Invasive Surgery (RMIS) pass through small incisions into the patient's body and interact with soft internal organs. The performance of traditional robotic manipulators such as the da Vinci Robotic System is limited due to insufficient flexibility of the manipulator and lack of haptic feedback. Modern surgical manipulators have taken inspiration from biology e.g. snakes or the octopus. In order for such soft and flexible arms to reconfigure itself and to control its pose with respect to organs as well as to provide haptic feedback to the surgeon, tactile sensors can be integrated with the robot's flexible structure. The work presented here takes inspiration from another area of biology: cucumber tendrils have shown to be ideal tactile sensors for the plant that they are associated with providing useful environmental information during the plant's growth. Incorporating the sensing principles of cucumber tendrils, we have created miniature sensing elements that can be distributed across the surface of soft manipulators to form a sensor network capable of acquire tactile information. Each sensing element is a retractable hemispherical tactile measuring applied pressure. The actual sensing principle chosen for each tactile makes use of optic fibres that transfer light signals modulated by the applied pressure from the sensing element to the proximal end of the robot arm. In this paper, we describe the design and structure of the sensor system, the results of an analysis using Finite Element Modeling in ABAQUS as well as sensor calibration and experimental results. Due to the simple structure of the proposed tactile sensor element, it is miniaturisable and suitable for MIS. An important contribution of this work is that the developed sensor system can be ”loosely” integrated with a soft arm effectively operating independently of the arm and without affecting the arm's motion during bending or elongation.

This paper investigates how a walker could maintain the variability of an arbitrary set of state variables within desired margins while walking on an uncertain soft terrain. The state variables are dynamically related to the visco-elastic impedance parameters of the body on a given set of uncertain soft terrains using internal memory primitives. A rimless wheel, a walker in its simplest form, is used to perform numerical simulations based on analytical dynamic models and hardware experiments to test a novel algorithm. The rimless wheel model is widely used by the legged locomotion research community to understand basic collision and energetics during passive dynamic walking. Very often, variability of punctuated force perturbations across collisions between the legs and the ground cause uncertain steady state dynamics of walking. This leads to the existence of a finite probability that certain state variables can reach unstable regions. Such phenomenon is known as metastability of walking. In this case, we actuate the rimless wheel with a constant torque leaving it to develop any speed profile for a given visco-elastic impedance distribution of the ground and its own vertical visco-elastic impedance that pushes the rimless wheel against the ground. Here we measure the robustness of the novel algorithm by its ability to shift the distribution of collision forces to a safer region in order to minimize the probability of reaching a given critical force threshold. Our analysis shows that the generalization of the variability of walking in different regions of the internal and external visco-elastic impedance spaces can simplify the computational challenges of robust walking on uncertain visco-elastic terrains.

Maintaining the grip on an artery with a pulsating impedance, holding the steering wheel of a vehicle on a bumpy terrain, or holding a live hamster without excessive squeezing may be trivial tasks to most humans. However, a robot will find it very difficult to maintain the grip of such uncertain objects based on real-time feedback control. This paper presents a stochastic control law to maintain the grip on an uncertain object while manipulating against external forces. The radial impedance parameters of the soft object is assumed to undergo Gaussian random variations. Here we demonstrate that the proposed model free grip controller can maintain a safe grip at two diagonally opposite points of the object merely based on the statistics of the normal force. It accomplishes this by computing a probability of grip failure to adapt the compression on the soft object. A novel optimal estimation algorithm that can concurrently estimate the unknown impedance parameters of the object and the states of the coupled dynamic system is discussed as a potential tool to be used in predictive optimal impedance control on uncertain objects. Experimental results on adaptive grip control on a cylindrical tube inflated and deflated with a Gaussian random variation has been presented to validate the algorithm.

Robotic manipulators for minimally invasive surgeries have traditionally been rigid, with a steerable end effector. While the rigidity of manipulators improve precision and controllability, it limits reachability and dexterity in constrained environments. Soft manipulators with controllable stiffness on the other hand, can be deployed in single port or natural orifice surgical applications to reach a wide range of areas inside the body, while being able to passively adapt to uncertain external forces, adapt the stiffness distribution to suit the kinematic and dynamic requirements of the task, and provide flexibility for configuration control. Here, we present the design of a snake-like laboratory made soft robot manipulator of 20 mm in average diameter, which can actuate, soften, or stiffen joints independently along the length of the manipulator by combining granular jamming with McKibben actuators. It presents a comprehensive study on the relative contributions of the granule size, material type, and membrane coupling on the range, profile, and variability of stiffness.