A Push Recovery Strategy for a Passively Compliant Humanoid Robot using Decentralized LQR Controllers

被引:0
|
作者
Spyrakos-Papastavridis, Emmanouil [1 ]
Medrano-Cerda, Gustavo A. [1 ]
Tsagarakis, Nikos G. [1 ]
Dai, Jian S.
Caldwell, Darwin G. [1 ]
机构
[1] Fdn Ist Italiano Tecnol IIT, Dept Adv Robot, I-16163 Genoa, Italy
关键词
Humanoid; passive compliance; optimal control; ANGULAR-MOMENTUM; FORCE CONTROL;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a control scheme that is directed towards the performance of push recovery on the compliant humanoid robot, COMAN. The novelty offered by this work is related to the use of a decentralized controller based on an initial Limited Quadratic Regulator (LQR) design on a humanoid robot in addition to the regulation of the actual joint positions instead of the motor positions. Moreover, the ankle-knee strategy is examined through the use of a compliant double inverted pendulum model. A key feature of the propounded approach lies in the controller's ability to regulate the system's inherently compliant dynamics through considering not only the motor-related variables but also those of the link-side, appearing after the passive compliant element. Consequently, this leads to a control method that is capable of stabilizing the robot by means of increasing the damping on the link, which is essential given the system's oscillatory behaviour once it has been perturbed.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Wall Cutting Strategy for Circular Hole Using Humanoid Robot
    Park, Beomyeong
    Cho, Hyunbum
    Choi, Wonje
    Park, Jaeheung
    [J]. 2015 12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI), 2015, : 564 - 569
  • [32] Learning Push Recovery Behaviors for Humanoid Walking Using Deep Reinforcement Learning
    Dicksiano C. Melo
    Marcos R. O. A. Maximo
    Adilson Marques da Cunha
    [J]. Journal of Intelligent & Robotic Systems, 2022, 106
  • [33] Learning Push Recovery Behaviors for Humanoid Walking Using Deep Reinforcement Learning
    Melo, Dicksiano C.
    Maximo, Marcos R. O. A.
    da Cunha, Adilson Marques
    [J]. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2022, 106 (01)
  • [34] Partition-Aware Stability Control for Humanoid Robot Push Recovery With Whole-Body Capturability
    Song, Hyunjong
    Peng, William Z.
    Kim, Joo H.
    [J]. JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME, 2024, 16 (01):
  • [35] On Global Optimization of Walking Gaits for the Compliant Humanoid Robot, COMAN Using Reinforcement Learning
    Dallali, Houman
    Kormushev, Petar
    Li, Zhibin
    Caldwell, Darwin
    [J]. CYBERNETICS AND INFORMATION TECHNOLOGIES, 2012, 12 (03) : 39 - 52
  • [36] BALANCE RECOVERY OF A HUMANOID ROBOT USING COGNITIVE SENSORIMOTOR LOOPS (CSLs)
    Kubisch, Matthias
    Benckendorff, Christian
    Hild, Manfred
    [J]. FIELD ROBOTICS, 2012, : 142 - 148
  • [37] BALANCING STRATEGY USING THE PRINCIPLE OF ENERGY CONSERVATION FOR A HOPPING HUMANOID ROBOT
    Cho, Baek-Kyu
    Kim, Jung-Hoon
    Oh, Jun-Ho
    [J]. INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS, 2013, 10 (03)
  • [38] Mobile Robot Navigation with Swarm Intelligence using a Decentralized Strategy
    Guzel, Mehmet Serdar
    Bostanci, Erkan
    Mishra, Alok
    [J]. 2022 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII 2022), 2022, : 838 - 843
  • [39] Nonlinear Two-Wheeled Self-Balancing Robot Control using LQR and LQG Controllers
    Dabbagh, Jamil
    Altas, Ismail H.
    [J]. 2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019), 2019, : 855 - 859
  • [40] Humanoid Robot Locomotion System With Balancing Feedback Using Leg and Arm Strategy and Stepping Strategy
    Luqman, Muhammad
    Adiprawita, Widyawardana
    Mutijarsa, Kusprasapta
    [J]. 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS 2015, 2015, : 650 - 655