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This study presents a theoretical method for planning and controlling agilebipedal locomotion based on robustly tracking a set of non-periodic keyframestates. Based on centroidal momentum dynamics, we formulate a hybridphase-space planning and control method which includes the following keycomponents: (i) a step transition solver that enables dynamically trackingnon-periodic keyframe states over various types of terrains, (ii) a robusthybrid automaton to effectively formulate planning and control algorithms,(iii) a steering direction model to control the robot's heading, (iv) aphase-space metric to measure distance to the planned locomotion manifolds, and(v) a hybrid control method based on the previous distance metric to producerobust dynamic locomotion under external disturbances. Compared to otherlocomotion methodologies, we have a large focus on non-periodic gait generationand robustness metrics to deal with disturbances. Such focus enables theproposed control method to robustly track non-periodic keyframe states overvarious challenging terrains and under external disturbances as illustratedthrough several simulations.

Robust Optimal Planning and Control of Non-Periodic Bipedal Locomotion with A Centroidal Momentum Model