Exploiting the Natural Dynamics of Series Elastic Robots by Actuator-Centered Sequential Linear Programming

Schlossman, Rachel; Thomas, Gray C.; Campbell, Orion; Sentis, Luis

doi:10.1109/robio.2018.8665239

Cited by 3 publications

(3 citation statements)

References 16 publications

(25 reference statements)

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“…The optimization algorithm is in those cases able to exploit the properties of the specific actuator and adding spring-damper elements to the joints is even known to result in motions that resemble those found in nature [7]. For series elastic actuators, methods have been proposed to incorporate bandwidth, torque, and joint limits in a computationally efficient way [8][9] [10]. However, very often we do not have exact details of actuators and modeling them would lead to high engineering effort.…”

Section: A Related Workmentioning

confidence: 99%

Frequency-Aware Model Predictive Control

Grandia

Farshidian

Dosovitskiy

et al. 2019

IEEE Robot. Autom. Lett.

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Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic tasks, the presence of unmodeled dynamics renders the motion infeasible on the real system. Model errors can be a result of model simplifications, but also naturally arise when deploying the robot in unstructured and nondeterministic environments. Predominantly, compliant contacts and actuator dynamics lead to bandwidth limitations. While classical control methods provide tools to synthesize controllers that are robust to a class of model errors, such a notion is missing in modern trajectory optimization, which is solved in the time domain. We propose frequency-shaped cost functions to achieve robust solutions in the context of optimal control for legged robots. Through simulation and hardware experiments we show that motion plans can be made compatible with bandwidth limits set by actuators and contact dynamics. The smoothness of the model predictive solutions can be continuously tuned without compromising the feasibility of the problem. Experiments with the quadrupedal robot ANYmal, which is driven by highlycompliant series elastic actuators, showed significantly improved tracking performance of the planned motion, torque, and force trajectories and enabled the machine to walk robustly on terrain with unmodeled compliance.

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Section: A Related Workmentioning

confidence: 99%

Frequency-Aware Model Predictive Control

Grandia

Farshidian

Dosovitskiy

et al. 2019

IEEE Robot. Autom. Lett.

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“…semidefinite programming, and quantum computing [16], [17], [14], [18], [19], [20], [21]. It is bilinear and associative:…”

Section: Appendix B Implementation Of Uc Inversementioning

confidence: 99%

A Generalized Matrix Inverse with Applications to Robotic Systems

Zhang¹,

Uhlmann²

2018

Preprint

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It is well-understood that the robustness of mechanical and robotic control systems depends critically on minimizing sensitivity to arbitrary application-specific details whenever possible. For example, if a system is defined and performs well in one particular Euclidean coordinate frame then it should be expected to perform identically if that coordinate frame is arbitrarily rotated or scaled. Similarly, the performance of the system should not be affected if its key parameters are all consistently defined in metric units or in imperial units. In this paper we show that a recently introduced generalized matrix inverse permits performance consistency to be rigorously guaranteed in control systems that require solutions to underdetermined and/or overdetermined systems of equations.

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“…Miyadaira et al [ 31 ] proposed two approaches of analyzing the movement of a robot with an articulated leg (without a toe) having three degrees of freedom. Schlossman et al [ 32 ] formulated the trajectory optimization problem for a series of elastic robots in a novel way adopting sequential linear programming. Zhang et al [ 33 ] presented the design of a jumping robot inspired by the jumping locomotion of locusts.…”

Section: Introductionmentioning

confidence: 99%

Vertical Jumping for Legged Robot Based on Quadratic Programming

Tian

Gao

Shi

et al. 2021

Sensors

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The highly dynamic legged jumping motion is a challenging research topic because of the lack of established control schemes that handle over-constrained control objectives well in the stance phase, which are coupled and affect each other, and control robot’s posture in the flight phase, in which the robot is underactuated owing to the foot leaving the ground. This paper introduces an approach of realizing the cyclic vertical jumping motion of a planar simplified legged robot that formulates the jump problem within a quadratic-programming (QP)-based framework. Unlike prior works, which have added different weights in front of control tasks to express the relative hierarchy of tasks, in our framework, the hierarchical quadratic programming (HQP) control strategy is used to guarantee the strict prioritization of the center of mass (CoM) in the stance phase while split dynamic equations are incorporated into the unified quadratic-programming framework to restrict the robot’s posture to be near a desired constant value in the flight phase. The controller is tested in two simulation environments with and without the flight phase controller, the results validate the flight phase controller, with the HQP controller having a maximum error of the CoM in the x direction and y direction of 0.47 and 0.82 cm and thus enabling the strict prioritization of the CoM.

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Exploiting the Natural Dynamics of Series Elastic Robots by Actuator-Centered Sequential Linear Programming

Cited by 3 publications

References 16 publications

Frequency-Aware Model Predictive Control

Frequency-Aware Model Predictive Control

A Generalized Matrix Inverse with Applications to Robotic Systems

Vertical Jumping for Legged Robot Based on Quadratic Programming

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