Centroidal-momentum-based trajectory generation for legged locomotion

Abstract This paper presents a trajectory optimization framework for planning dynamic legged locomotion based on a robot’s centroidal momentum (CM), which is the aggregation of all the links’ momenta at the robot’s Center of Mass (CoM). This new framework is built around CM dynamic model driven by Ground Reaction Forces (GRFs) parameterized with Bezier polynomials. Due to the simple form of CM dynamics, the closed-form solution of the robot’s CM can be obtained by directly integrating the Bezier polynomials of GRFs. The CM can be also calculated from the robot’s generalized coordinates and velocities using Centroidal Momentum Matrices (CMM). For dynamically feasible motions, these CM values should match, thereby providing equality constraints for the proposed trajectory optimization framework. Direct collocation methods are utilized to obtain feasible GRFs and joint trajectories simultaneously under kinematic and dynamic constraint. With the closed-form solutions of CM due to the parameterization of GRFs in the formulation, numerical error induced by collocation methods in the solution of trajectory optimization can be reduced, which is crucial for reliable tracking control when applied to real robotic systems. Using the proposed framework, jumping trajectories of legged robots are obtained in the simulation. Experimental validation of the algorithm is performed on a planar robot testbed, proving the effectiveness of the proposed method in generating dynamic motions of the legged robots.

[1]  Sangbae Kim,et al.  High-speed bounding with the MIT Cheetah 2: Control design and experiments , 2017, Int. J. Robotics Res..

[2]  David E. Orin,et al.  Centroidal dynamics of a humanoid robot , 2013, Auton. Robots.

[3]  Marco Hutter,et al.  Gait and Trajectory Optimization for Legged Systems Through Phase-Based End-Effector Parameterization , 2018, IEEE Robotics and Automation Letters.

[4]  Yuval Tassa,et al.  Synthesis and stabilization of complex behaviors through online trajectory optimization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Darwin G. Caldwell,et al.  Design of the Hydraulically Actuated, Torque-Controlled Quadruped Robot HyQ2Max , 2017, IEEE/ASME Transactions on Mechatronics.

[6]  Michel Taïx,et al.  CROC: Convex Resolution of Centroidal Dynamics Trajectories to Provide a Feasibility Criterion for the Multi Contact Planning Problem , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  Hae-Won Park,et al.  Single Leg Dynamic Motion Planning with Mixed-Integer Convex Optimization , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2005, SIAM Rev..

[9]  Katja D. Mombaur,et al.  Using optimization to create self-stable human-like running , 2009, Robotica.

[10]  Rida T. Farouki,et al.  Algorithms for polynomials in Bernstein form , 1988, Comput. Aided Geom. Des..

[11]  Kazuhito Yokoi,et al.  The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[12]  Anil V. Rao,et al.  Algorithm 902: GPOPS, A MATLAB software for solving multiple-phase optimal control problems using the gauss pseudospectral method , 2010, TOMS.

[13]  Russ Tedrake,et al.  Efficient Bipedal Robots Based on Passive-Dynamic Walkers , 2005, Science.

[14]  Russ Tedrake,et al.  Whole-body motion planning with centroidal dynamics and full kinematics , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[15]  Mihai Anitescu,et al.  A fixed time-step approach for multibody dynamics with contact and friction , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[16]  Russ Tedrake,et al.  A direct method for trajectory optimization of rigid bodies through contact , 2014, Int. J. Robotics Res..

[17]  C. David Remy,et al.  Optimal gaits and motions for legged robots , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Sangbae Kim,et al.  Online Planning for Autonomous Running Jumps Over Obstacles in High-Speed Quadrupeds , 2015, Robotics: Science and Systems.

[19]  David E. Orin,et al.  Centroidal Momentum Matrix of a humanoid robot: Structure and properties , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Yannick Aoustin,et al.  Numerical and experimental study of the virtual quadrupedal walking robot-semiquad , 2006 .

[21]  Peter Fankhauser,et al.  ANYmal - a highly mobile and dynamic quadrupedal robot , 2016, IROS 2016.

[22]  James P. Ostrowski Computing reduced equations for robotic systems with constraints and symmetries , 1999, IEEE Trans. Robotics Autom..

[23]  Hae-Won Park,et al.  Design and experimental implementation of a quasi-direct-drive leg for optimized jumping , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[24]  Kevin Blankespoor,et al.  BigDog, the Rough-Terrain Quadruped Robot , 2008 .

[25]  Sung-Hee Lee,et al.  Reaction Mass Pendulum (RMP): An explicit model for centroidal angular momentum of humanoid robots , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[26]  Jonas Buchli,et al.  Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds , 2016, IEEE Robotics and Automation Letters.

[27]  Alexander Herzog,et al.  Trajectory generation for multi-contact momentum control , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[28]  David E. Orin,et al.  Development of high-span running long jumps for humanoids , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).