This study analyzed the spatial distribution characteristics of the foot-end trajectory of a robotic leg mechanism during different gait phases. Based on this analysis, a task space partition-based dimensional parameter optimization method was proposed. To further evaluate the spatial distribution of the high-performance transmission regions after optimization, a box-counting dimension and lacunarity were introduced as supplementary characterization indices. First, according to the functional requirements of different gait phases, the task space of the mechanism is partitioned into stance, mid-swing, and swing-transition regions. A unified kinematic model and singularity criterion are then established for the planar five-bar mechanism, and mechanism performance indices for different task regions are constructed based on the Jacobian matrix to characterize the force and velocity transmission capabilities of the mechanism, as well as its singularity margin. A genetic algorithm is used to perform dimensional synthesis optimization of the mechanism parameters. Furthermore, a task space transmission performance field is introduced, and the area ratio, box-counting dimension, and lacunarity of regions with high performance are used to characterize the spatial structure of high-performance transmission regions before and after optimization. Finally, a series of theoretical calculations and physical experiments are conducted to verify that the differential characteristics of the mechanism have a significant influence on both its static and dynamic performance. The experimental results show that the optimized mechanism achieves lower normalized objective values in all task regions and outperforms the reference mechanism in load capacity, static power consumption, positioning accuracy, and trajectory consistency. The maximum static load capacity reaches 1.29 times that of the reference mechanism, while the static power consumption is reduced to approximately one half of that of the reference mechanism.
Liu et al. (Thu,) studied this question.