Humanoid robotic upper limbs including the robotic hand and robotic arm are widely studied as the important parts of a humanoid robot. A robotic upper limb with light weight and high output can perform more tasks. The drive system is one of the main factors affecting the weight and output of the robotic upper limb, and therefore, the main purpose of this study is to compare and analyze the effects of the different drive methods on the overall structure. In this paper, we first introduce the advantages and disadvantages of the main drive methods such as tendon, gear, link, fluid (hydraulic and pneumatic), belt, chain, and screw drives. The design of the drive system is an essential factor to allow the humanoid robotic upper limb to exhibit the structural features and functions of the human upper limb. Therefore, the specific applications of each drive method on the humanoid robotic limbs are illustrated and briefly analyzed. Meanwhile, we compared the differences in the weight and payload (or grasping force) of the robotic hands and robotic arms with different drive methods. The results showed that the tendon drive system is easier to achieve light weight due to its simple structure, while the gear drive system can achieve a larger torque ratio, which results in a larger output torque. Further, the weight of the actuator accounts for a larger proportion of the total weight, and a reasonable external placement of the actuator is also beneficial to achieve light weight.

As observed in the outbreaks of SARS and swine flu, as well as many other infectious diseases, the huge volume of human traffic across numerous enclosed public venues has posed immense challenges to preventing the spread of communicable diseases. There is an urgent need for effective disease surveillance management in public areas under pandemic outbreaks. The physicochemical properties associated with ionic liquids make them particularly suited for molecular communications in sensing networks where low throughput is quite adequate for pathogen detection. This paper presents a self-cognizant system for rapid diagnosis of infectious disease using a bionic sensor such that testing can be supported without collecting a fluid sample from a subject through any invasive methods. The system is implemented for testing the performance of the proposed bionic liquid sensing network.