Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656749
Title: Design and control of a humanoid robot
Author: Wee, Teck Chew
ISNI:       0000 0004 5349 3507
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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Abstract:
Design and control of the bipedal humanoid robot locomotion are challenging areas of research. Accurate models of the kinematics and dynamics of the robot are essential to achieve bipedal locomotion. Bipedal walking can be achieved either with flat-foot or toe-foot walking. Flat-foot walking is more stable but slower, whereas toe-foot walking produces more natural and faster motion. Furthermore in toe-foot walking it is possible to perform stretch knee walking. The mechanical structure of the robot is designed with compact modular parts so that the robot kinematics can be modelled as a multi-point-mass system, and its dynamics are modelled applying the inverted pendulum model and the zero-moment-point concept. The optimality in the gait trajectory is achieved exploiting augmented model predictive control methods taking into consideration the trade-off between walking speed and stability. The robustness and stability of the walking gaits and posture in the presence of internal or external disturbances are enhanced by adopting angular compensation with joint control techniques. The thesis develops a flat-foot optimal walking gait generation method. The effectiveness of the control technique and the passive-toe design is validated by simulation tests with the robot walking on slope, stepping over an obstacle and climbing a stair. The walking gaits are implemented on a mid-size (1.6 meter, 58 kg) bipedal robot. Experiments demonstrate the effectiveness of the new proposed Augmented Model Predictive Control (AMPC) method has improved and produced a smoother gaits tracking trajectory in comparison with existing LQR and preview methods; and at the same time the proposed algorithms are able to reduce noise interference.
Supervisor: Astolfi, Alessandro; Xie, Ming Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.656749  DOI: Not available
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