Realtime Safety Control for Bipedal Robots to Avoid Multiple Obstacles via CLF-CBF Constraints

This research introduces a novel planning system for the Cassie-series bipedal robot, enabling it to effectively navigate around multiple non-overlapping obstacles. The system utilizes a single, continuously differentiable control barrier function (CBF) to achieve reactive planning. To detect individual obstacles, a height map is generated from a LiDAR point cloud, and an elliptical outer approximation is computed, which is then transformed into a CBF. The system employs the QP-CLF-CBF formalism, originally developed by Ames et al., to ensure the generation of safe trajectories. The effectiveness of the proposed system is demonstrated through both simulation and experimental evaluations conducted on Cassie, showcasing safe planning capabilities in environments with multiple obstacles.