Robotic hands perform several amazing functions similar to the human hands, thereby offering high flexibility in terms of the tasks performed. However, developing integrated hands without additional actuation parts while maintaining important functions such as human-level dexterity and grasping force is challenging. The actuation parts make it difficult to integrate these hands into existing robotic arms, thus limiting their applicability. Based on a linkage-driven mechanism, an integrated linkage-driven dexterous anthropomorphic robotic hand called ILDA hand, which integrates all the components required for actuation and sensing and possesses high dexterity, is developed. It has the following features: 15-degree-of-freedom (20 joints), a fingertip force of 34N, compact size (maximum length: 218 mm) without additional parts, low weight of 1.1 kg, and tactile sensing capabilities. Actual manipulation tasks involving tools used in everyday life are performed with the hand mounted on a commercial robot arm.

Significant attention has been paid to robotic rehabilitation using various types of actuator and power transmission. Amongst those, cable-driven rehabilitation robots (CDRRs) are relatively newer and their control strategies have been evolving in recent years. CDRRs offer several promising features, such as low inertia, lightweight, high payload-to-weight ratio, large work-space and configurability. In this paper, we categorize and review the cable-driven rehabilitation robots in three main groups concerning their applications for upper limb, lower limb, and waist rehabilitation. For each group, target movements are identified, and promising designs of CDRRs are analyzed in terms of types of actuators, controllers and their interactions with humans. Particular attention has been given to robots with verified clinical performance in actual rehabilitation settings. A large part of this paper is dedicated to comparing the control strategies and techniques of CDRRs under five main categories of: Impedance-based, PID-based, Admittance-based, Assist-as-needed (AAN) and Adaptive controllers. We have carefully contrasted the advantages and disadvantages of those methods with the aim of assisting the design of future CDRRs.

Wheels have been commonly used for locomotion in mobile robots and transportation systems because of their simple structure and energy efficiency. However, the performance of wheels in overcoming obstacles is limited compared with their advantages in driving on normal flat ground. Here, we present a variable-stiffness wheel inspired by the surface tension of a liquid droplet. In a liquid droplet, as the cohesive force of the outermost liquid molecules increases, the net force pulling the liquid molecules inward also increases. This leads to high surface tension, resulting in the liquid droplet reverting to a circular shape from its distorted shape induced by gravitational forces. Similarly, the shape and stiffness of a wheel were controlled by changing the traction force at the outermost smart chain block. As the tension of the wire spokes connected to each chain block increased, the wheel characteristics reflected those of a general circular-rigid wheel, which has an advantage in high-speed locomotion on normal flat ground. Conversely, the modulus of the wheel decreased as the tension of the wire spoke decreased, and the wheel was easily deformed according to the shape of obstacles. This makes the wheel suitable for overcoming obstacles without requiring complex control or sensing systems. On the basis of this mechanism, a wheel was applied to a two-wheeled wheelchair system weighing 120 kilograms, and the state transition between a circular high-modulus state and a deformable low-modulus state was realized in real time when the wheelchair was driven in an outdoor environment.

A straightforward inverse kinematic algorithm for multilink flexible robots is proposed to improve the control performance. The inclusion of a dynamic constraint maximizes the performance of feedback controllers in high-speed applications. To obtain a numerically feasible solution, the singular perturbation approach is employed, which decomposes the inverse kinematics into an averaged part (slow part) and a parasitic part (fast part). The solution of the averaged part is considered the desired inverse kinematics, while the parasitic part is intentionally removed. The parameter expansion is carried out to obtain the solution sequentially. The implicit expansion method, which is a refined version of the expansion method, reduces computing time considerably. The formula in discrete time offers efficiency in computer applications. In addition, a requirement on differentiability of the desired task trajectory is derived.

Industrial robot identification is usually based on the inverse dynamic identification model (IDIM) that comes from Newton's laws and has the advantage of being linear with respect to the parameters. Building the IDIM from the measurement signals allows the use of linear regression techniques like the least-squares (LS) or the instrumental variable (IV) for instance. Nonetheless, this involves a careful preprocessing to deal with sensor noise. An alternative in system identification is to consider an output error approach where the model's parameters are iteratively tuned in order to match the simulated model's output and the measured system's output. This paper proposes an extensive comparison of three different output error approaches in the context of robot identification. One of the main outcomes of this work is to show that choosing the input torque as target identification signal instead of the output position may lead to a gain in robustness versus modeling errors and noise and in computational time. Theoretical developments are illustrated on a six degrees-of-freedom rigid robot.