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In this paper, chiral metamaterials (CMM) were optimized from conceptual design to fine design with the effective elastic constants unchanged under finite strain. First, through calculation and comparison of examples, the unit cell method was selected to compute the effective elastic properties of the periodic chiral metamaterials under finite strain. Secondly, the conceptual design of chiral metamaterials with prescribed Poisson's ratios under finite strain was realized through density-based and feature-driven topology optimization. Then, the method of moving asymptotes (MMA) was used to solve the optimization problems. Based on the optimal configuration, chiral metamaterials with prescribed Poisson's ratios and Young's moduli under finite strain were carefully designed through shape optimization. Genetic algorithm was used to solve the optimization problem. Finally, the optimal models were fabricated by 3D printing. The optimal design was validated by tensile test results, i.e., the designed chiral metamaterials can maintain effective elastic properties under large deformation, and the invariance of the effective elastic properties depends on the nonlinearity of the flexible chiral metamaterials. [ABSTRACT FROM AUTHOR]

This add-on discussion addresses a shortcoming of the paper in the title – the authors missed an important reference by Clausen et al. (2015). Differences between the current work and the said reference are clarified in this comment. In addition, some mistakes in the commented paper were corrected. [ABSTRACT FROM AUTHOR]

Terrain segmentation is crucial for field robots’ navigation, path planning, and map building in unstructured environments. However, it is still a challenge for most existing vision-based methods to segment terrains with satisfactory accuracy and real-time performance. Deep learning-based approaches have been proven to be quite competitive for image segmentation. However, most previous segmentation networks have deep networks and explosive growth of the number of parameters, which can hardly satisfy the inference speed of onboard computers. Therefore, a novel terrain segmentation method founded on CSPResnet is designed for segmenting terrains of field robots in this study. It fuses some advantages of several state-of-the-art networks and has a novel structure suitable for on-board computers. The proposed method reduces the computational cost for segmenting terrains by cross-stage partial, spatial pyramid pooling, and nearest interpolation. Finally, some comparison and ablation studies were made on the HDU-Terrain dataset. We collected this dataset from a field robot’s perspective. It is quite different from the existing benchmark dataset for unmanned driving and has more than 4000 frames of pixel-wise annotation, with many frequently encountered unstructured terrain types. Experimental results prove that the proposed terrain segmentation network named TSCSPnet balances real-time performance with high accuracy and has the potential to be applied to various field robots. [ABSTRACT FROM AUTHOR]