Robotic systems require high accuracy in manipulating objects. Positioning errors are influenced by geometric tolerances and various sources. This paper introduces a new technique based on the interior-point algorithm optimisation method to allocate tolerances to the geometric parameters of a robot. This method consists of three steps. First, a method for modelling the kinematic problem as well as the geometric errors must be used. The Denavit–Hartenberg rule is the most suitable method for this modelling case. Then, a mathematical model for tolerance allocation is developed and used as a nonlinear multivariable optimisation problem. Finally, the “interior-point” algorithm is used to solve this optimisation problem. The accuracy and efficiency of the proposed method, in determining the tolerance allocations for a Delta parallel robot, is illustrated via calculation and simulation results. The values of the dimensional tolerances found are optimal. As a result, these values always keep the accuracy less than or equal to the imposed value.

Muscle mass significantly influences skeletal muscle behaviour, potentially explaining why traditional massless Hill-type models struggle to predict the forces generated by larger muscles during dynamic, submaximal contractions. However, the applicability of mass-enhanced Hill-type models in human locomotion remains unexplored. Here, we compared the predicted force from a 1D mass-enhanced Hill-type muscle model with a traditional 1D massless Hill-type muscle model across a range of experimentally measured human movements. Kinematic and electromyographic data were collected from twenty participants performing locomotor tasks and supplemented with existing cycling data. Muscle size was geometrically scaled by factors from 0.1 to 10, which causes lengths to be scaled proportionally, cross-sectional area and peak isometric force F ₀ with the square, and mass with the cube of the factor. Muscle tissue mass (inertia) and cadence increased the differences between mass-enhanced and massless predictions of force and power. At high cadence and the largest scale, the normalized root mean square difference between force traces reached 7% of F ₀ , (averaged across muscles). However, differences between models were minimal (<1%) at human-sized scale 1. Real muscle additionally deforms in 3D, we still do not know the extent to which this extra dimensionality affects muscle forces for these human movements.

Ankle injuries may lead to severe complications, including chronic instability and reduced mobility. Rehabilitation therefore plays a critical role in restoring normal ankle function and preventing long-term impairment. However, conventional manual physiotherapy presents several limitations, such as limited availability of therapists, variability in treatment, and reduced accessibility for home-based rehabilitation. Consequently, significant research efforts have been devoted to the development of robotic and mechatronic devices for ankle rehabilitation. In this work, we propose the design of a simple and cost-effective parallel mechanism dedicated to ankle rehabilitation. The proposed device is based on a closed-chain kinematic architecture capable of reproducing the three principal rotational degrees of freedom of the ankle joint. This configuration enables controlled and repeatable rehabilitation exercises while ensuring mechanical stability and compactness. Furthermore, the device is designed to allow patients to perform rehabilitation exercises autonomously, without the continuous assistance of a physiotherapist, both in clinical environments and in home-based rehabilitation settings.