Agile Maneuvers in Legged Robots: a Predictive Control Approach

Mastalli, Carlos; Merkt, Wolfgang; Xin, Guiyang; Shim, Jae-Hyun; Mistry, Michael; Havoutis, Ioannis; Vijayakumar, Sethu

doi:10.21203/rs.3.rs-1870369/v1

Cited by 19 publications

(25 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter, in fact, computes the state trajectories and GRFs that are then sent to the robot, so it requires more accuracy. Thanks to the optimal references, we can avoid the use of a full dynamics model as in [20].…”

Section: A Related Workmentioning

confidence: 99%

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Bratta¹,

Focchi²,

Rathod³

et al. 2022

Preprint

View full text Add to dashboard Cite

Model Predictive Control (MPC) approaches are widely used in robotics, since they allow to compute updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of parameters of the cost function in order to obtain good performance. When for example, a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a certain goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work we propose a novel optimization-based Reference Generator, named Governor, which exploits a Linear Inverted Pendulum model to compute reference trajectories for the Center of Mass, while taking into account the possible under-actuation of a gait (e.g. in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work [1]. We also present a formulation that can guarantee a certain response time to reach a goal, without the need to tune the weights of the cost terms. In addition, foothold locations are corrected to drive the robot towards the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

show abstract

Section: A Related Workmentioning

confidence: 99%

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Bratta¹,

Focchi²,

Rathod³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, we have incorporated a feedback WB-MPC [37], [38] into our framework as opposed to the conventional MPC-WBC architecture used in our previous work. Constraining only the end-effector trajectories, instead of the CoM motions, allows the WB-MPC to freely accommodate substantial perturbations and perception errors, and to maximise the robot's capabilities by optimising its posture.…”

Section: B Contributionmentioning

confidence: 99%

“…The legged robot generates motions through a model predictive controller that relies on the robot's full-body dynamics. The formulation is based on two previous papers [37], [38]. We solve the optimal control (OC) problems using Crocoddyl's advanced solvers [36].…”

Section: A Optimal Control Formulationmentioning

confidence: 99%

“…Both yield similar performances, demonstrating our method's generalisability. Both formulations can be written as [37], [38]:…”

Section: A Optimal Control Formulationmentioning

confidence: 99%

“…τ b includes the joint torque command, Coriolis and gravitation terms and a c is the desired acceleration which results from the rigid contact constraint (contact point velocity is null) and includes the Baumgarte stabilisation term [56]. The impulse dynamics described in more detail in [37] which allow velocity changes at impact have also been omitted from the formulation. This contact dynamics formulation comes from the application of the Gauss principle of least constraint [48], as described in detail in [57].…”

Section: A Optimal Control Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of predictive controllers for locomotion and balance recovery of quadruped robots

Corbères

Flayols

Léziart

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

As locomotion decisions must be taken by considering the future, most existing quadruped controllers are based on a model predictive controller (MPC) with a reduced model of the dynamics to generate the motion and a wholebody controller to execute it. Yet the simplifying assumptions of the MPC are often chosen ad-hoc or by intuition. In this article, we focus on a set of MPCs and analyze the effect of chosen model reductions on the behavior of the robot. Based on existing formulations, we present additional controllers to better understand the influence of model reductions on the controller capabilities. Finally, we propose a robust predictive controller capable of optimizing the foot placements, gait period, centerof-mass trajectory and ground reaction forces. The behavior of these controllers is statistically evaluated in simulation. This empirical study aims to assess the relative importance of the components of the optimal control problem (variables, costs, dynamics) to be able to take reasoned decisions instead of arbitrarily emphasizing or neglecting some of them. We also provide a qualitative study in simulation and on the real robot Solo-12.

show abstract

A feasibility-driven approach to control-limited DDP

et al. 2022

Self Cite

View full text Add to dashboard Cite

Differential dynamic programming (DDP) is a direct single shooting method for trajectory optimization. Its efficiency derives from the exploitation of temporal structure (inherent to optimal control problems) and explicit roll-out/integration of the system dynamics. However, it suffers from numerical instability and, when compared to direct multiple shooting methods, it has limited initialization options (allows initialization of controls, but not of states) and lacks proper handling of control constraints. In this work, we tackle these issues with a feasibility-driven approach that regulates the dynamic feasibility during the numerical optimization and ensures control limits. Our feasibility search emulates the numerical resolution of a direct multiple shooting problem with only dynamics constraints. We show that our approach (named Box-FDDP) has better numerical convergence than Box-DDP$$^+$$ + (a single shooting method), and that its convergence rate and runtime performance are competitive with state-of-the-art direct transcription formulations solved using the interior point and active set algorithms available in Knitro. We further show that Box-FDDP decreases the dynamic feasibility error monotonically—as in state-of-the-art nonlinear programming algorithms. We demonstrate the benefits of our approach by generating complex and athletic motions for quadruped and humanoid robots. Finally, we highlight that Box-FDDP is suitable for model predictive control in legged robots.

show abstract

Agile Maneuvers in Legged Robots: a Predictive Control Approach

Cited by 19 publications

References 61 publications

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Comparison of predictive controllers for locomotion and balance recovery of quadruped robots

A feasibility-driven approach to control-limited DDP

Contact Info

Product

Resources

About