Structural optimization of a new type of lever-assisted gear reducer based on a genetic algorithm

Abstract

Gear reducers are critical for speed and torque transmissions between motors and manipulators. With the development of robotic research, many new requirements, such as low speed and heavy load, have been proposed for the design of gear reducers used in the joints. To meet these challenges, here, we present the design of a new gear reducer based on a spherical motion sub-lever drive mechanism. Our lever-based gear reducer can transmit the speed and torque from the input shaft to the output shaft through a fixed-axis gear train transmission, lever transmission, and internal translational gear transmission. Compared with traditional gear reducers, our lever-based reducer has stronger load capacities and is suitable for low-speed and heavy-load scenarios. The design parameters of the lever drive mechanism were optimized via finite element analysis and a genetic algorithm, and the assembly of the lever drive mechanism was further simplified. We found the dimensions of the lever are critical for improving the overall performance of this reducer. In addition, the transmission ability of this reducer was demonstrated by a physical prototype. This reducer will find many applications in robotic joints, cranes, and mine hoists.