Computationally-Robust and Efficient Prioritized Whole-Body Controller with Contact Constraints

Abstract

In this paper, we devise methods for the multiobjective control of humanoid robots, a.k.a. prioritized wholebody controllers, that achieve efficiency and robustness in the algorithmic computations. We use a form of whole-body controllers that is very general via incorporating centroidal momentum dynamics, operational task priorities, contact reaction forces, and internal force constraints. First, we achieve efficiency by solving a quadratic program that only involves the floating base dynamics and the reaction forces. Second, we achieve computational robustness by relaxing task accelerations such that they comply with friction cone constraints. Finally, we incorporate methods for smooth contact transitions to enhance the control of dynamic locomotion behaviors. The proposed methods are demonstrated both in simulation and in real experiments using a passive-ankle bipedal robot.

Passive Whole-Body Control for Quadruped Robots: Experimental Validation Over Challenging Terrain

“…Additionally, recalling (29) proves that the controller is indeed passive; thus, the overall system is passive. 10 The time derivative of an arbitrary storage functionV (∆x(t)) that is a function of ∆x(t) could be written asV = d dt ∆x T (t) · ∂ ∂∆x(t) V that is written for short asV = ∆v T ∇V . 11 From the definition of v and F d , we get v 12 Note thatq j −q j,d = −∆q j .…”

“…Many recent contributions in locomotion control have been proposed in the literature that were successfully tested on bipeds and quadrupeds (e.g. [6,7,8,9,5,10]). Some of them are based on quasi-static assumptions or lower dimensional models [11,12,13].…”

Thermal Recovery of Multi-Limbed Robots With Electric Actuators

“…31 is equivalent to a joint configuration task. Thus, this can be inserted in a QP-based whole-body controller with task relaxation, such as [22], which ensures unilateral constraint satisfiability. This QP can then be a subroutine to compute the configuration update (Eq.…”

A Whole-Body Control Framework Based on the Operational Space Formulation Under Inequality Constraints via Task-Oriented Optimization

“…Recently, the method in [30] is further extended with consideration of dynamic consistency [31], however, it is not straightforward to apply to the underactuated robot with the floating-base, e.g., humanoids. Interestingly, the authors in [32] suggest computing acceleration in the configuration space based on the desired acceleration commands instead of using operational-space dynamics calculation. While this method is computationally efficient and it can provide feasible solutions satisfying task priorities, the operational space dynamics is disregarded.…”