Multi-contact walking pattern generation based on model preview control of 3D COM accelerations

Caron, Stéphane; Kheddar, Abderrahmane

doi:10.1109/humanoids.2016.7803329

Cited by 63 publications

(66 citation statements)

References 28 publications

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“…Approaches to synthesize dynamically feasible multicontact motions have also been extensively studied [21][22][23][24][25]. However, it is not trivial to include planning of contact poses in these approaches because contacts planning in general involves discrete or non-convex constraints for the contact poses.…”

Section: Related Workmentioning

confidence: 99%

Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction

Lin

Ponton

Righetti

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Humanoid robots dynamically navigate an environment by interacting with it via contact wrenches exerted at intermittent contact poses. Therefore, it is important to consider dynamics when planning a contact sequence. Traditional contact planning approaches assume a quasi-static balance criterion to reduce the computational challenges of selecting a contact sequence over a rough terrain. This however limits the applicability of the approach when dynamic motions are required, such as when walking down a steep slope or crossing a wide gap. Recent methods overcome this limitation with the help of efficient mixed integer convex programming solvers capable of synthesizing dynamic contact sequences. Nevertheless, its exponential-time complexity limits its applicability to short time horizon contact sequences within small environments. In this paper, we go beyond current approaches by learning a prediction of the dynamic evolution of the robot centroidal momenta, which can then be used for quickly generating dynamically robust contact sequences for robots with arms and legs using a search-based contact planner. We demonstrate the efficiency and quality of the results of the proposed approach in a set of dynamically challenging scenarios.

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Section: Related Workmentioning

confidence: 99%

Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction

Lin

Ponton

Righetti

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…This inner approximation allows us to perform MPC for the LIP model by solving a QP that already accounts for contact constraints in the whole-body model, as shown in Figure 2. In contrast to existing work [23], this new method does not require an admissible region of the CoM to be specified apriori.…”

Section: Introductionmentioning

confidence: 99%

Formal Connections between Template and Anchor Models via Approximate Simulation

Kurtz

Silva

Wensing

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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Reduced-order template models like the Linear Inverted Pendulum (LIP) and Spring-Loaded Inverted Pendulum (SLIP) are widely used tools for controlling high-dimensional humanoid robots. However, connections between templates and whole-body models have lacked formal underpinnings, preventing formal guarantees when it comes to integrated controller design. We take a small step towards addressing this gap by considering the notion of approximate simulation. Derived from simulation relations for discrete transition systems in formal methods, approximate similarity means that the outputs of two systems can remain -close. In this paper, we consider the case of controlling a balancer via planning with the LIP model. We show that the balancer approximately simulates the LIP and derive linear constraints that are sufficient conditions for maintaining ground contact. This allows for rapid planning and replanning with the template model by solving a quadratic program that enforces contact constraints in the full model. We demonstrate the efficacy of this planning and control paradigm in a simulated push recovery scenario for a planar 4-link balancer.

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“…Typical MPC adopt an hierarchical approach, see e.g. in [1], [2], [3], [4]: they compute trajectories to be best tracked by the underlying local whole-body controller -see e.g. in [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Model-predictive control in multi-contact based on stability polyhedrons

Audren

Kheddar

2017

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)

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Abstract-We propose a new linear model-predictive control scheme in multi-contact based on the center of mass reduced model. In order to linearize the dynamics of the CoM, we exploit the notion of stability polyhedrons associated to given contact stances, inside which the existence of contact forces is guaranteed. To compute stability polyhedrons, we have first to specify a convex polytope inside which the center of mass's acceleration lies. We then generate a minimum jerk trajectory inside these successive stability polyhedrons that also yields contact transitions timings, and integrate it with our quadratic programming whole-body controller as part of the multi-objective tasks.

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Multi-contact walking pattern generation based on model preview control of 3D COM accelerations

Cited by 63 publications

References 28 publications

Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction

Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction

Formal Connections between Template and Anchor Models via Approximate Simulation

Model-predictive control in multi-contact based on stability polyhedrons

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