Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional ground locomotion. The Bennett linkage belongs to a family of overconstrained linkages with three-dimensional spatial motion and unparalleled joint axes. We present the first work regarding the design, modeling, and optimization of the Bennett leg module, enabling lateral locomotion, like the crabs, that was not capable with robotic legs designed with common planar mechanisms. We further explored the concept of overconstrained robots, which is a class of advanced robots based on the design reconfiguration of the Bennett leg modules, serving as a potential direction for future research.

Image generation based on the generative adversarial network (GAN) has been widely used in the field of computer vision.It helps generate images similar to the given data by learning their distribution.However, in many tasks, training on small datasets of scenes may lead to mode collapse, such that the generated images are often blurred and almost the same.To solve this problem, we propose a generative adversarial network with transfer learning for mode collapse called TL-GAN.Owing to the size of the training dataset, we introduce transfer learning (VGG pre-training network) to extract more useful features from the underlying pixels and add them to the discriminator, which can be used to calculate the distance between samples, and to provide the discriminator with a new training target.The discriminator thus learns the best features that can distinguish between real data and generated data using the proposed model.This also enhances the learning capability of the generator, which learn further about the distribution of real data.Meanwhile, generator can produce new images more realistic.The results of experiments show that the TL-GAN can guarantee the diversity of samples.A qualitative comparison with several prevalent methods confirmed its effectiveness.