Foot Placement for Footpad Robots Based on Perceptive MPC

For legged robots, achieving dynamic motion in challenging terrains requires precise foot positioning and careful planning of underactuated system dynamics. Compared to robots with point feet, those with surface feet face a more complex task in selecting landing areas, and the surface contact with the ground further complicates the dynamics due to its varying interaction forms. We propose an online perception-control framework that optimizes the robot's full-degree-of-freedom motion in real time based on perceived terrain information. To manage the complexity of surface contact, we employ a contact point model that simplifies the interaction between the foot and the ground into multiple discrete point contacts, which are treated as landing points. An elevation map is created, and plane segmentation is precomputed to select the most suitable landing plane. Inequality constraints are extracted as approximations of the selected plane region and integrated into a nonlinear model predictive controller. Simulation experiments on bipedal robots were conducted in various complex terrains, including slopes, stairs, and uneven surfaces resembling plum blossom piles. The results demonstrate that our framework effectively enhances the robot's adaptability to diverse terrains.