Energy-efficient bio-inspired gait planning and control for biped robot based on human locomotion analysis

In this paper an experiment of human locomotion was carried out using a motion capture system to extract the human gait features. The modifiable key gait parameters affecting the dominant performance of biped robot walking were obtained from the extracted human gait features. Based on the modifiable key gait parameters and the Allowable Zero Moment Point (ZMP) Variation Region (AZR), we proposed an effective Bio-inspired Gait Planning (BGP) and control scheme for biped robot towards a given travel distance D. First, we construct an on-line Bio-inspired Gait Synthesis algorithm (BGSN) to generate a complete walking gait motion using the modifiable key gait parameters. Second, a Bio-inspired Gait Parameters Optimization algorithm (BGPO) is established to minimize the energy consumption of all actuators and guarantee biped robot walking with certain walking stability margin. Third, the necessary controllers for biped robot were introduced in briefly. Simulation and experiment results demonstrated the effectiveness of the proposed method, and the gait control system was implemented on DRC-XT humanoid robot.

[1]  Minzhou Luo,et al.  An Experimental Analysis of Overcoming Obstacle in Human Walking , 2014 .

[2]  Jun-Ho Oh,et al.  Online Walking Pattern Generation and Its Application to a Biped Humanoid Robot — KHR-3 (HUBO) , 2008, Adv. Robotics.

[3]  Kazuhito Yokoi,et al.  Biped walking stabilization based on linear inverted pendulum tracking , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Chee-Meng Chew,et al.  Achieving Energy-Efficient Bipedal Walking Trajectory through GA-Based Optimization of Key Parameters , 2009, Int. J. Humanoid Robotics.

[5]  Yaonan Wang,et al.  Energy-Efficiency-Based Gait Control System Architecture and Algorithm for Biped Robots , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Kazuhito Yokoi,et al.  Biped walking pattern generation by using preview control of zero-moment point , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[7]  Fan-Ren Chang,et al.  Anthropomorphic Design of the Human-Like Walking Robot , 2013 .

[8]  Byung Kook Kim,et al.  Energy-Efficient Gait Planning and Control for Biped Robots Utilizing the Allowable ZMP Region , 2014, IEEE Transactions on Robotics.

[9]  Kazuhito Yokoi,et al.  Planning walking patterns for a biped robot , 2001, IEEE Trans. Robotics Autom..

[10]  Florentin Wörgötter,et al.  Fast Biped Walking with a Sensor-driven Neuronal Controller and Real-time Online Learning , 2006, Int. J. Robotics Res..

[11]  Xiang Luo,et al.  Planning and Control for Passive Dynamics Based Walking of 3D Biped Robots , 2012 .

[12]  Kazuo Hirai.  The Honda humanoid robot: development and future perspective , 1999 .

[13]  Jorge Angeles,et al.  Minimization of power losses in cooperating manipulators , 1992 .

[14]  Patrick Lacouture,et al.  From human motion capture to humanoid locomotion imitation Application to the robots HRP-2 and HOAP-3 , 2011, Robotica.

[15]  Marco Ceccarelli,et al.  An application of CaTraSys, a cable-based parallel measuring system for an experimental characterization of human walking , 2009, Robotica.

[16]  Toshikazu Kawasaki,et al.  Design of prototype humanoid robotics platform for HRP , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Tzuu-Hseng S. Li,et al.  Dynamic Balance Control for Biped Robot Walking Using Sensor Fusion, Kalman Filter, and Fuzzy Logic , 2012, IEEE Transactions on Industrial Electronics.

[18]  Ambarish Goswami,et al.  Postural Stability of Biped Robots and the Foot-Rotation Indicator (FRI) Point , 1999, Int. J. Robotics Res..

[19]  Prahlad Vadakkepat,et al.  Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed , 2009, Robotica.

[20]  Luquan Ren,et al.  Biomechanics of Musculoskeletal System and Its Biomimetic Implications: A Review , 2014 .

[21]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[22]  Wei Xu,et al.  An improved ZMP trajectory design for the biped robot BHR , 2011, 2011 IEEE International Conference on Robotics and Automation.

[23]  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).

[24]  Yoshihiko Nakamura,et al.  Analytical real-time pattern generation for trajectory modification and footstep replanning of humanoid robots , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Bram Vanderborght,et al.  Objective locomotion parameters based inverted pendulum trajectory generator , 2008, Robotics Auton. Syst..

[26]  M Vukobratović,et al.  On the stability of biped locomotion. , 1970, IEEE transactions on bio-medical engineering.

[27]  Sven Behnke,et al.  Lateral capture steps for bipedal walking , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[28]  Arthur D. Kuo,et al.  Choosing Your Steps Carefully , 2007, IEEE Robotics & Automation Magazine.

[29]  Kouhei Ohnishi,et al.  Biped Walking Pattern Generation by Using Preview Control Based on Three-Mass Model , 2013, IEEE Transactions on Industrial Electronics.