Canine animals excel at running and jumping, exhibiting remarkable agility and making them popular bionic models for legged robots. In this study, we utilized motion capture equipment to record a Malinois dog jumping over hurdles and circular holes while running, obtaining motion trajectory data of the trunk, limbs, head, and tail. Subsequently, a digital model of both the Malinois and the environment was constructed, and the limb motion data, as well as the spatial relationships between the dog and the obstacles, were analyzed in both temporal and spatial dimensions. From this analysis, we summarized the behavioral strategies and kinematic patterns underlying the Malinois’ running and obstacle‐crossing process. The main findings are as follows: (1) The obstacle‐crossing process follows a specific footfall sequence and can be divided into three major phases: takeoff phase, flight phase, and landing phase. (2) During the takeoff phase, the pitch angle of the trunk at liftoff exhibits an arctangent function relationship with the height and distance of the obstacle, while the liftoff velocity is determined by the obstacle height and distance. (3) In the flight phase, the head and tail movements contribute to adjusting trunk posture. (4) During the landing phase, the forelimbs touch the ground first, and the virtual leg formed by the hip and foot generates a spring‐like effect upon contact. Finally, we applied data retargeting and optimization methods to reproduce the Malinois’ obstacle‐crossing behavior on a quadruped robot, verifying the practicality of our data and research findings. Additionally, we proposed a performance evaluation method for animals and legged robots in jumping over obstacles while running.
HUANG et al. (Thu,) studied this question.