Indentation size effects in poly(methyl methacrylate) (PMMA) were studied through nanoindentation. Two factors of indentation size effects in PMMA, namely yield criterion and shear transformation-mediated plasticity, were analysed in detail. The yield criterion that considers strength differential (SD) effects and pressure sensitivity was constructed by performing the combined shear-compression experiments. The relationship between hardness and normal stress can then be obtained based on Tabot’s relation. Shear transformation-mediated plasticity was also applied to model the measured hardness as a function of the indentation depth at different strain rates. Results show that the yield criterion contains the terms of SD effects and pressure sensitivity gives the best description of the yielding of PMMA. Additionally, the volume of single shear transformation zone calculated through the presented criterion agrees well with simulation and exhibits increases with increasing strain rate. Indentation size effects in PMMA under different strain rates were discussed and an appropriate indentation depth range was suggested for calculating the hardness and modulus.

Quadruped robots have frequently appeared in various situations, including wilderness rescue, planetary exploration, and nuclear power facility maintenance. The quadruped robot with an active body joint has better environmental adaptability than one without body joints. However, it is difficult to guarantee the stability of the body joint quadruped robot when walking on rough terrain. Given the above issues, this paper proposed a gait control method for the body joint quadruped robot based on multi-constraint spatial coupling (MCSC) algorithm. The body workspace of the robot is divided into three subspaces, which are solved for different gaits, and then coupled to obtain the stable workspace of the body. A multi-layer central pattern generator model based on the Hopf oscillator is built to realize the generation and switching of walk and trot gaits. Then, combined with the MCSC area of the body, the reflex adjustment strategy on different terrains is established to adjust the body’s posture in real time and realize the robot’s stable locomotion. Finally, the robot prototype is developed to verify the effectiveness of the control method. The simulation and experiment results show that the proposed method can reduce the offset of the swing legs and the fluctuation of the body attitude angle. Furthermore, the quadruped robot is ensured to maintain stability by dynamically modifying its body posture. The relevant result can offer a helpful reference for the control of quadruped robots in complex environments.

Robotic monocular pose estimation is an important part of neural monocular pose estimation-driven methods, which includes monocular simultaneous localization and mapping (SLAM) and single-view object pose estimation (OPE) driven by neural methods. The mapping thread leeches onto robotic monocular pose estimation. Robotic monocular pose estimation consists of the localization part of monocular SLAM and the object pose solving part of single-view OPE. Depth prediction neural networks, semantics, neural implicit representations, and large language models (LLMs) are neural methods that have been important components of neural monocular pose estimation-driven methods. Complete robotic monocular pose estimation is a potential module in real robots. Possible future research directions and applications are discussed.