Fast Foothold Planning for Quadruped Robot Locomotion Using Topological Perception

Recent advancements in legged robot locomotion systems have led to significant improvements in their agility and overall performance. However, for these systems to effectively handle sudden obstacles, especially in foothold areas, real-time integration of external information is crucial. In this paper, we introduce a rapid integration approach between perception and action in legged robot locomotion to address such unexpected external obstacles. We utilize depth sensors to capture external data in the form of pointclouds. We have developed a fast surface perception model based on topological information, employing dynamic sparsity through the growing neural gas algorithm. This model addresses several key challenges in 3D point cloud extraction: 1) Extracting object features from a smaller portion of data. 2) Controlling granularity in unsupervised learning using a topological structure. 3) Controlling granularity with only 3D positional point cloud information. To evaluate the effectiveness of our proposed method, we conducted a series of progressive experiments. Initially, we compared the performance of our topological perception model with similar approaches. Subsequently, we carried out qualitative and quantitative experiments using a real, low-cost quadruped robot