Generalized Contact Constraints of Hybrid Trajectory optimization for Different Terrains and Analysis of Sensitivity to Randomized Initial Guesses

Abstract

To generate a dynamic bipedal walking with foot rolling motion for bipedal robot, hybrid trajectory optimization is capable of planning level walking with great energetic efficiency. However, the direct implementation of this optimization requires different sets of variables to express different active contact constraints, which can be complicated to implement. To simplify the optimization formulation, we propose the generalized contact constraints where the same set of variables are used through all the walking phases. By changing the variable and constraint bounds, different contact constraints for different contact conditions can be generally expressed. The proposed modifications are applied on the bipedal robot AMBER 3, where the optimization results on different terrains are compared and discussed. On the other hand, it is known that a randomized initial guess can be used to solve this optimization, yet its effect on the gaits on different terrains is unclear. As a result, we analyzed the sensitivity of the optimization to a set of randomized initial guesses. The level and downslope walking gaits are also validated via the experiments on AMBER 3.