This paper presents a method for comprehensive 6D estimation of tip wrench for generally deflected static elastic rods. Current methods for load estimation are restricted to estimating lateral (point or distributed) forces for (quasi-)planar deformation; estimation of tangential force and moment, i.e., the full 6D wrench, remains largely unreliable due to the ill-posed nature of the problem. To address this challenge, this paper begins by proposing a high-fidelity static rod model that leverages Koopman Operator theory. Building on this model and utilizing shape feedback, a computationally efficient three-step wrench estimator is proposed: (i) a Koopman-UKF local moment observer, (ii) a static equilibrium solver, and (iii) a rod model propagator. Then, a 2D wrench screw system, identified as the insensible wrench in the initial estimation, elucidates error sources and informs strategies to enhance accuracy by incorporating additional feedback, such as the rod tip material frame and base axial force. Ultimately, the framework delivers accurate 6D tip wrench estimation with quantified uncertainty. Simulation and experimental evaluations validate its effectiveness, demonstrating mean errors of$53.14\,$mN (1.94%) and$2.65\,$mNm (7.18%) for a$159\,$mm-long Nitinol tube undergoing complex out-of-plane deformations, outperforming three replicated state-of-the-art methods. Additionally, its applicability to more complex continuum robots is demonstrated through load estimation on a Parallel Continuum Robot.

Therapies targeting neurodegenerative diseases via brain ventricles and spinal parenchyma face delivery challenges. Systemic administration is ineffective due to the blood-brain barrier, while direct surgical access, especially for multi-site delivery, is highly invasive. The spinal subarachnoid space offers potential for microcatheter-based delivery, but existing robotic catheter technologies are unsuitable due to spinal anatomy constraints. This paper presents a miniaturised and sensorised steerable eversion-growing robot tailored to navigation of the subarachnoid space of the spine. The property of eversion reduces interaction forces with the anatomy, rendering our approach safer than microcatheters that need to be pushed. Our system is capable of real-time tip force estimation with three degrees of freedom (DoF) using fibre Bragg gratings (FBG). Additionally, it incorporates a micro-endoscope and a steerable tip, all within a tiny 2mm outer diameter. The system’s navigation, sensing, and imaging capabilities were evaluated using a realistic up-scaled phantom of the subarachnoid space covering the cervical spine, demonstrating interaction forces within the safe range of 2-5N during phantom navigation. Comparison study of instrument-tissue interactions further approved its clinical relevance, presenting a 73.78% decrease of the mean absolute forces to traditional insertion without the sheath in global measurements.

This paper presents a lumped-parameter dynamic model of a pressure driven eversion robot carrying a catheter through its hollow core. A simulation framework based on the model is developed in MATLAB and is used for understanding the underlying physics, for identifying the regions of operation, and for demonstrating that, for a range of input commands, the catheter can be used as an actuation mechanism for propelling eversion; an approach especially useful for miniaturised systems. Simulations are experimentally validated on the MAMMOBOT system, which is a miniature steerable soft growing robot for early breast cancer detection. It was demonstrated that for most regions of operation experimental results compare well with simulation exhibiting an error less than 4%. Only one region of operation demonstrated larger deviations due possibly to unmodeled dynamics, which will be investigated in future work.

Aortic valve surgery is the preferred procedure for replacing a damaged valve with an artificial one. The ValveTech robotic platform comprises a flexible articulated manipulator and surgical interface supporting the effective delivery of an artificial valve by teleoperation and endoscopic vision. This article presents our recent work on force-perceptive, safe, semiautonomous navigation of the ValveTech platform prior to valve implantation. First, we present a force observer that transfers forces from the manipulator body and tip to a haptic interface. Second, we demonstrate how hybrid forward/inverse mechanics, together with endoscopic visual servoing, lead to autonomous valve positioning. Benchtop experiments and an artificial phantom quantify the performance of the developed robot controller and navigator. Valves can be autonomously delivered with a 2.0±0.5 mm position error and a minimal misalignment of 3.4±0.9°. The hybrid force/shape observer (FSO) algorithm was able to predict distributed external forces on the articulated manipulator body with an average error of 0.09 N. FSO can also estimate loads on the tip with an average accuracy of 3.3%. The presented system can lead to better patient care, delivery outcome, and surgeon comfort during aortic valve surgery, without requiring sensorization of the robot tip, and therefore obviating miniaturization constraints.

Continuum surgical robots can navigate anatomical pathways to reach pathological locations deep inside the human body. Their flexibility, however, generally comes with reduced dexterity at their tip and limited workspace. Building on recent work on eccentric tube robots, this paper proposes a new continuum robot architecture and theoretical framework that combines the flexibility of push/pull actuated snake robots and the dexterity offered by concentric tube robotic end-effectors. We designed and present a prototype system as a proof-of-concept, and developed a tailored quasistatic mechanics-based model that describes the shape and end-effector's pose for this new type robotic architecture. The model can accommodate an arbitrary number of arms placed eccentrically with respect to the backbone's neutral axis. Our experiments show that the error between model and experiment is on average 3.56% of the manipulator's overall length. This is in agreement with state of the art models of single type continuum architecture.

This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have one dominant frequency at the vertical perturbation frequency of the mobile robot and one with distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.

Tendrils are common stable structures in nature and are used for sensing, actuation, and geometrical stiffness modulation. In this paper, for the first time we exploit the helical geometry of a shape memory alloy (SMA) tendril as a simple to fabricate highly dexterous robotic continuum tentacle that we called Active Tendril-Backbone Robot (ATBR). This is achieved via partial (120 deg) activation of single helix turns resulting in backbone directional bendings. A 141.5 mm prototype (130 mm when fully compressed) has been fabricated and a simple theoretical framework is proposed and experimentally validated for modeling of the tentacle configuration. The manipulator has five 2-DOF joints capable of reaching bending angles of up to 54.5 deg and angular speed of up to 6.8 deg/s. The dexterity of the manipulator is showcased empirically in reaching complex configurations and simple navigation through confined space of a curving path.

Robust mechanisms for slip resistance are an open challenge in legged locomotion. Animals such as goats show impressive ability to resist slippage on cliffs. It is not fully known what attributes in their body determine this ability. Studying the slip resistance dynamics of the goat may offer insight toward the biologically inspired design of robotic hooves. This article tests how the embodiment of the hoof contributes to solving the problem of slip resistance. We ran numerical simulations and experiments using a passive robotic goat hoof for different compliance levels of its three joints. We established that compliant yaw and pitch and stiff roll can increase the energy required to slide the hoof by ≈ 20% compared to the baseline (stiff hoof). Compliant roll and pitch allow the robotic hoof to adapt to the irregularities of the terrain. This produces an antilock braking system-like behavior of the robotic hoof for slip resistance. Therefore, the pastern and coffin joints have a substantial effect on the slip resistance of the robotic hoof, while the fetlock joint has the lowest contribution. These shed insights into how robotic hooves can be used to autonomously improve slip resistance.

The complexity of soft continuum manipulators with hybrid and tuneable structures poses a challenging task to achieve an inverse kinematics model which is both precise and computationally efficient for control and optimization purposes. In this paper, a new method based on the principle of virtual work and a geometry deformation approach is presented for the inverse kinematics model of the STIFF-FLOP arm which is a pneumatically actuated continuum manipulator. We propose a novel simplified and computationally efficient yet accurate analytical solution to analyse the static behaviour of a compound soft manipulator in the presence of external and body forces which is verified against experimental data, showing promising agreement with 10% mean error for planar movements. In the process, we present a new modelling approach for braided soft extensor actuators with no braid-surface relative slip constraint. For the first time, our model predicts a simple analytical solution for the cross section deformation which is essential to control soft manipulators with regional tunable stiffness structure.