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Quadruped locomotion is currently a vibrant research area, which has reacheda level of maturity and performance that enables some of the most advancedreal-world applications with autonomous quadruped robots both in academia andindustry. Blind robust quadruped locomotion has been pushed forward in controland technology aspects within recent decades. However, in the complicatedenvironment, the capability including terrain perception and path planning isstill required. Visual perception is an indispensable ability in leggedlocomotion for such a demand. This study explores a vision-based navigationmethod for a small-scale quadruped robot Pegasus-Mini, aiming to propose amethod that enables efficient and reliable navigation for the small-scalequadruped locomotion. The vision-based navigation method proposed in this studyis applicable in such a small-scale quadruped robot platform in which thecomputation resources and space are limited. The semantic segmentation based ona CNN model is adopted for the real-time path segmentation in the outdoorenvironment. The desired traverse trajectory is generated through real-timeupdating the middle line, which is calculated from the edge position of thesegmented path in the images. To enhance the stability of the path planningdirectly based on the semantic segmentation method, a trajectory compensationmethod is supplemented considering the temporal information to revise theuntrustworthy planned path. Experiments of semantic segmentation and navigationin a garden scene are demonstrated to verify the effectiveness of the proposedmethod.

Vision-based Navigation for a Small-scale Quadruped Robot Pegasus-Mini