Most of conventional controllers are prone to consume more computational time for controlling a wheeled humanoid robot. A nonlinear trajectory control of a wheeled humanoid robot using a model-based controller with less computational load and energy consumption is presented in this article. The upper body of the humanoid robot consists of two 6 degrees of freedom redundant arms, a three degrees of freedom torso and a two degrees of freedom neck. The nonholonomic mobile platform consists of two actuated wheels and two caster wheels. Nonlinearity of the robot dynamic model and coupling between various branches of the upper body are taken into account. The dynamic model derived using Newton–Euler approach along with decoupled Natural Orthogonal Complement matrix approach is used to derive dynamic equations of the upper body and the wheeled platform. Zero moment point based stability approach is used for verifying stable motion of the wheeled humanoid robot with minimum energy and time consumption to complete a task. The proposed computed torque controller is compared with other similar controllers developed for the same robot model to prove advantages of the proposed torque controller. Simulation results are experimentally validated with the real robot.

Prisma Hand II is an under-actuated prosthetic hand developed at the University of Naples, Federico II to study in-hand manipulations during grasping activities. 3 motors equipped on the robotic hand drive 19 joints using elastic tendons. The operations of the hand are achieved by combining tactile hand sensing with under-actuation capabilities. The hand has the potential to be employed in both industrial and prosthetic applications due to its dexterous motion capabilities. However, currently there are no commercially available tactile sensors with compatible dimensions suitable for the prosthetic hand. Hence, in this work, we develop a novel tactile sensor designed based on an opto-electronic technology for the Prisma Hand II. The optimised dimensions of the proposed sensor made it possible to be integrated with the fingertips of the prosthetic hand. The output voltage obtained from the novel tactile sensor is used to determine optimum grasping forces and torques during in-hand manipulation tasks employing Neural Networks (NNs). The grasping force values obtained using a Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) are compared based on Mean Square Error (MSE) values to find out a better training network for the tasks. The tactile sensing capabilities of the proposed novel sensing method are presented and compared in simulation studies and experimental validations using various hand manipulation tasks. The developed tactile sensor is found to be showcasing a better performance compared to previous version of the sensor used in the hand.

Advanced control algorithms are essential for enhancing the functionality of prosthetic hands, enabling them to operate in diverse conditions. This paper presents a Model Reference Adaptive Controller (MRAC) developed for a tendon-driven soft continuum wrist, integrated into the ’PRISMA HAND II’ prosthetic hand. The primary objective of our research is to design an adaptive controller that facilitates wrist movements eliminating external disturbances while minimizing computational requirements. To achieve this, kinematic and dynamic models of the wrist are developed based on the Piece-wise Constant Curvature (PCC) hypothesis. The controller consists of a reference model generated using the PCC model, and state errors are evaluated by comparing the responses of the reference model to those of the wrist model. These errors are reduced using the MRAC approach to make the wrist’s behavior closely align with that of the reference model. Stability of the closed-loop system is ensured using the Lyapunov direct method, along with the ’New Theorem of Stability’, a replacement for Barbalat’s lemma, ensuring that the error between the reference model and the actual system converges to zero and that the adaptive gains stabilize to fixed values. The adaptive performance of the controller is evaluated through experimental validations, where the motions of the prosthetic hand attached to the wrist are treated as unknown disturbances, and the mechanical stiffness of the wrist is considered as an uncertain parameter, resulting from the degradation of the internal springs.

This review explores the current state of eversion robotics in the context of colonoscopy, given the need for less invasive, more patient-friendly screening technologies. Conventional colonoscopy often leads to discomfort and patient reluctance, contributing to delayed diagnoses and high colorectal cancer mortality rates. Eversion robots, also known as vine robots or soft growing robots are soft, pressure-driven devices that extend by everting from the tip whilst offering a promising option by enabling frictionless advancement and potentially pain-free procedures. We examine the key challenges and opportunities in adapting eversion robots for clinical endoscopic use, focusing on material selection, actuation, steering, and payload delivery. From the literature, thermoplastic polyurethane emerges as the most viable material for the robot’s sleeve due to its airtightness, biocompatibility, suitability for heat or ultrasonic welding, and availability in highly flexible thin layers. Tip-steering mechanisms are identified as the most effective strategies for navigation, allowing high flexibility without increasing the wall thickness of the robot, as required in alternative approaches using distributed actuation mechanisms. The review also evaluates strategies for integrating functional tools at the tip of the robot, concluding that cap-free designs provide superior adaptability to the varying colon diameter, preserve compressibility, and keep tip friction to a minimum, unlike cap-based payload delivery methods. By consolidating current research and identifying pathways for innovation, this review supports the development of eversion soft robots as a next-generation solution for minimally invasive colorectal diagnostics and therapy.

The development of advanced control strategies for prosthetic hands is essential for improving performance and user experience. Soft prosthetic wrists pose substantial control challenges due to their compliant structures and nonlinear dynamics. This work presents a learning-based impedance control strategy for a tendon-driven soft continuum wrist, integrated with the PRISMA HAND II prosthesis, aimed at achieving stable and adaptive joint-space control. The proposed method combines physics-based modeling using Euler-Bernoulli beam theory and the Euler-Lagrange approach with a neural network trained to estimate unmodeled nonlinearities. Simulations achieved a Root Mean Square Error (RMSE) of 3.04×10−4 rad and a settling time of 3.1 s under nominal conditions. Experimental trials recorded an average RMSE of 2.7×10−2 rad and confirmed the controller’s ability to recover target trajectories under unknown external forces. The method supports compliant interaction, robust motion tracking, and trajectory recovery, positioning it as a viable solution for personalized prosthetic rehabilitation. Compared to traditional controllers like Sliding Mode Controller (SMC), Model Reference Adaptive Controller (MRAC), and Model Predictive Controller (MPC), the proposed method achieved superior accuracy and stability. This hybrid approach successfully balances analytical precision with data-driven adaptability, offering a promising pathway towards intelligent control in next-generation soft prosthetic systems.