Single-track-two-wheeled (STTW) robots show great potential in unmanned delivery applications due to their high maneuverability in crowded environments. However, in practice, strong disturbances from impacts, sharp turns, and load variations can present significant challenges for the implementation of accurate and stable robot control. In this work, a novel control framework based on model reduction of the STTW robot is proposed to solve the issue. A precise reduced model that eliminates redundant constraint equations is derived for the robot with general convex tire shapes, and an identification device is developed to experimentally obtain the accurate model parameters. Meanwhile, an online estimation method is proposed to obtain the uncertain load parameters embedded in the reduced model through abnormal dynamic behaviors of the robot. Using the identified reduced model, an adaptive impedance control law of the STTW robot is developed, and the control performance is validated on a prototype. The experimental results show that the robot has high control stability when it experiences strong impacts and sharp turns. The prototype can experimentally implement challenging tasks of automatic start-up and small-radius turning. It also successfully performs accurate motion control of the STTW robot with uncertain and unmeasurable load variations.

A two-wheeled vehicle has a compact structure and high mobility in crowded and complex environments, which has been widely used in urban logistics. The bicycle and self-balancing vehicle are the two main modes of the two-wheeled vehicle, and their combination allows for good balance-control stability at both high and low speeds. Four control inputs by two steerable driving wheels are required to implement transformations between the two modes due to the difference in their configuration spaces. However, the control inputs are redundant for planar motions, which results in an over-constraint of the vehicle. In this work, a two-wheeled robot with an additional structural degree of freedom (DOF) is designed to balance inputs and DOFs to avoid over-constraint. A transition mode based on oblique vehicle motions is used to bridge the transformation of the bicycle and self-balancing vehicle modes. A general kinematic model is developed for the two-wheeled robot’s planar motions, and the three modes’ kinematics are special cases with particular servo constraints. Structural DOF control laws are developed and experimentally validated on a prototype robot. Smooth transformations of the multimodal motions are also validated by using the prototype.

In recent years, a number of computational approaches have been proposed to effectively integrate multiple heterogeneous biological networks, and have shown impressive performance for inferring gene function. However, the previous methods do not fully represent the critical neighborhood relationship between genes during the feature learning process. Furthermore, it is difficult to accurately estimate the contributions of different views for multi-view integration. In this paper, we propose MGEGFP, a multi-view graph embedding method based on adaptive estimation with Graph Convolutional Network (GCN), to learn high-quality gene representations among multiple interaction networks for function prediction. First, we design a dual-channel GCN encoder to disentangle the view-specific information and the consensus pattern across diverse networks. By the aid of disentangled representations, we develop a multi-gate module to adaptively estimate the contributions of different views during each reconstruction process and make full use of the multiplexity advantages, where a diversity preservation constraint is designed to prevent the over-fitting problem. To validate the effectiveness of our model, we conduct experiments on networks from the STRING database for both yeast and human datasets, and compare the performance with seven state-of-the-art methods in five evaluation metrics. Moreover, the ablation study manifests the important contribution of the designed dual-channel encoder, multi-gate module and the diversity preservation constraint in MGEGFP. The experimental results confirm the superiority of our proposed method and suggest that MGEGFP can be a useful tool for gene function prediction.