Excitation and stabilization of passive dynamics in locomotion using hierarchical operational space control

Hutter, Marco; Gehring, Christian; Bloesch, Michael; Hoepflinger, Mark A.; Fankhauser, Péter; Siegwart, Roland

doi:10.1109/icra.2014.6907288

Cited by 6 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The H-QP solves this problem by dividing the action into a hierarchical set of tasks. The decomposition of the problem into sub-problems simplifies the constraints by decoupling them, while regarding the co-domain of the higher task as the domain of the current optimization [15], [16]. NLP uses a general formulation to tackle non-linear optimizations [18].…”

Section: Related Workmentioning

confidence: 99%

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control

Wen¹,

Rouxel²,

Mistry³

et al. 2022

Preprint

View full text Add to dashboard Cite

This work developed collaborative bimanual manipulation for reliable and safe human-robot collaboration, which allows remote and local human operators to work interactively for bimanual tasks. We proposed an optimal motion adaptation to retarget arbitrary commands from multiple human operators into feasible control references. The collaborative manipulation framework has three main modules: (1) contact force modulation for compliant physical interactions with objects via admittance control; (2) task-space sequential equilibrium and inverse kinematics optimization, which adapts interactive commands from multiple operators to feasible motions by satisfying the task constraints and physical limits of the robots; and (3) an interaction controller adopted from the fractal impedance control, which is robust to time delay and stable to superimpose multiple control efforts for generating desired joint torques and controlling the dual-arm robots. Extensive experiments demonstrated the capability of the collaborative bimanual framework, including (1) dual-arm teleoperation that adapts arbitrary infeasible commands that violate joint torque limits into continuous operations within safe boundaries, compared to failures without the proposed optimization; (2) robust maneuver of a stack of objects via physical interactions in presence of model inaccuracy;(3) collaborative multi-operator part assembly, and teleoperated industrial connector insertion, which validate the guaranteed stability of reliable human-robot co-manipulation.

show abstract

Section: Related Workmentioning

confidence: 99%

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control

Wen¹,

Rouxel²,

Mistry³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Using the Operational Space Formulation [4], one may prioritize tasks to be completed and ensure that lower priority tasks are only executed in the null space of higher priority task control. In [5] as well as [6] this is used to control a humanoid robot, while in [7] it is used as part of a locomotion scheme for a quadruped. In [8] a similar control hierarchy is achieved through quadratic programming which also allows for control of a humanoid.…”

Section: A Optimal Control Of High Dof Systemsmentioning

confidence: 99%

Parameterized and GPU-Parallelized Real-Time Model Predictive Control for High Degree of Freedom Robots

Hyatt,

Williams,

Killpack

2020

Preprint

View full text Add to dashboard Cite

This work presents and evaluates a novel input parameterization method which improves the tractability of model predictive control (MPC) for high degree of freedom (DoF) robots. Experimental results demonstrate that by parameterizing the input trajectory more than three quarters of the optimization variables used in traditional MPC can be eliminated with practically no effect on system performance. This parameterization also leads to trajectories which are more conservative, producing less overshoot in underdamped systems with modeling error. In this paper we present two MPC solution methods that make use of this parameterization. The first uses a convex solver, and the second makes use of parallel computing on a graphics processing unit (GPU). We show that both approaches drastically reduce solve times for large DoF, long horizon MPC problems allowing solutions at real-time rates. Through simulation and hardware experiments, we show that the parameterized convex solver MPC has faster solve times than traditional MPC for high DoF cases while still achieving similar performance. For the GPU-based MPC solution method, we use an evolutionary algorithm and that we call Evolutionary MPC (EMPC). EMPC is shown to have even faster solve times for high DoF systems. Solve times for EMPC are shown to decrease even further through the use of a more powerful GPU. This suggests that parallelized MPC methods will become even more advantageous with the improvement and prevalence of GPU technology.

show abstract

Optimized Foothold Planning and Posture Searching for Energy-Efficient Quadruped Locomotion over Challenging Terrains

Sun

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Excitation and stabilization of passive dynamics in locomotion using hierarchical operational space control

Cited by 6 publications

References 30 publications

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control

Parameterized and GPU-Parallelized Real-Time Model Predictive Control for High Degree of Freedom Robots

Optimized Foothold Planning and Posture Searching for Energy-Efficient Quadruped Locomotion over Challenging Terrains

Contact Info

Product

Resources

About