Real-time motion planning of legged robots: A model predictive control approach

Farshidian, Farbod; Jelavić, Edo; Satapathy, A.P.; Giftthaler, Markus; Buchli, Jonas

doi:10.1109/humanoids.2017.8246930

Cited by 124 publications

(96 citation statements)

References 23 publications

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“…[2]). Other approaches rely on decoupling the motion planning from the motion control [3,4,5]. Along this line, an optimization-based motion planner could rely on low dimensional models to compute Center of Mass (CoM) trajectories and footholds while a locomotion controller tracks these trajectories.…”

Section: Introductionmentioning

confidence: 99%

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

Fahmi

Mastalli

Focchi

et al. 2019

IEEE Robot. Autom. Lett.

103

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We present experimental results using a passive whole-body control approach for quadruped robots that achieves dynamic locomotion while compliantly balancing the robot's trunk. We formulate the motion tracking as a Quadratic Program (QP) that takes into account the full robot rigid body dynamics, the actuation limits, the joint limits and the contact interaction. We analyze the controller's robustness against inaccurate friction coefficient estimates and unstable footholds, as well as its capability to redistribute the load as a consequence of enforcing actuation limits. Additionally, we present practical implementation details gained from the experience with the real platform. Extensive experimental trials on the 90 kg Hydraulically actuated Quadruped (HyQ) robot validate the capabilities of this controller under various terrain conditions and gaits. The proposed approach is superior for accurate execution of highly dynamic motions with respect to the current state of the art.

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Section: Introductionmentioning

confidence: 99%

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

Fahmi

Mastalli

Focchi

et al. 2019

IEEE Robot. Autom. Lett.

103

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“…This method computes a time-varying, state-affine control policy based on a quadratic approximation of the optimal value function in an iterative process. The SLQ approach uses a Lagrangian method to enforce the state-input equality constraints in (5). The pure state constraints in (6) are handled by adding a quadratic penalty term to the cost function.…”

Section: Methods a Problem Definitionmentioning

confidence: 99%

“…Model Predictive Control (MPC) has gained broad interest in the robotics community as a tool for motion control of complex and dynamic systems. The ability to deal with nonlinearities and constraints has popularized the technique for many robotic applications, such as quadrotor control [1], autonomous racing [2], and legged locomotion [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Feedback MPC for Torque-Controlled Legged Robots

Grandia

Farshidian

Ranftl

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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131

103

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The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.

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“…The nonlinear optimal control problem defined in equations (4), (5) and (6) has been solved using the Sequential Linear Quadratic (SLQ) algorithm in a model predictive control fashion; details on the employed solver and the MPC scheme can be found in [23], [24].…”

Section: B Slq-mpc Controlmentioning

confidence: 99%

Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Minniti

Farshidian

Grandia

et al. 2019

IEEE Robot. Autom. Lett.

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Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a wholebody optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.

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Real-time motion planning of legged robots: A model predictive control approach

Cited by 124 publications

References 23 publications

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

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

Feedback MPC for Torque-Controlled Legged Robots

Whole-Body MPC for a Dynamically Stable Mobile Manipulator

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