Natural Walking Reference Generation Based on Double-Link LIPM Gait Planning Algorithm

Li, Tzuu-Hseng S.; Ho, Yen-Hung; Kuo, Ping‐Huan; Ye, Yanting; Wu, Li-Fan

doi:10.1109/access.2017.2669209

Cited by 18 publications

(11 citation statements)

References 33 publications

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“…However, the legs usually account for 40% of the robot's body weight. Therefore, VFLIPM extends from the double-link LIPM [44], [45] and considers the mass distribution of the legs.…”

Section: A Lipmmentioning

confidence: 99%

Fuzzy Dynamic Gait Pattern Generation for Real-Time Push Recovery Control of a Teen-Sized Humanoid Robot

2020

IEEE Access

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A fuzzy dynamic gait pattern generator, which allows a teen-sized humanoid robot to generate, in real-time, a suitable gait pattern when it is hit by an unexpected force, is proposed in this paper. Conventional gait pattern generators usually utilize the ideal Zero Moment Point (ZMP) to plan the trajectory of the Center of Mass (CoM), along with a cycloid to generate steps. However, pre-planned gait patterns cannot deal with unexpected situations, especially instances when the robot experiences an unknown force. Therefore, we propose a dynamic gait pattern generator that leverages the Virtual Force Linear Inverted Pendulum Model (VFLIPM) to adjust the trajectory of the CoM, and which detects balance status by estimating the trajectory of the ZMP using eight high-precision load cell pressure sensors mounted onto the robot's soles. We integrate an accelerometer and the pressure sensors through a fuzzy controller to instantly respond to external forces and generate a suitable gait pattern. When the robot is pushed suddenly, it first adopts a pre-planned gait pattern to replace the current gait. At the same time, the fuzzy controller calculates the recovery gait, with appropriate strides and lean angles to absorb the impact. The proposed method is implemented on the teen-sized humanoid robot, David Junior II, for the Push Recovery event at RoboCup. David Junior II endured a hit with potential energy during walking, which is 1.3 times more robust than when standing. INDEX TERMS Center of pressure (CoP), dynamic gait balance, humanoid robots, linear inverted pendulum model (LIPM), push recovery, real-time biped gait generation, sensor fusion, zero moment point (ZMP).

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“…However, the legs usually account for 40% of the robot's body weight. Therefore, VFLIPM extends from the double-link LIPM [44], [45] and considers the mass distribution of the legs.…”

Section: A Lipmmentioning

confidence: 99%

Fuzzy Dynamic Gait Pattern Generation for Real-Time Push Recovery Control of a Teen-Sized Humanoid Robot

2020

IEEE Access

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“…The mass of the support leg is assumed to be massless, which is far from the actual situation. Therefore, the mass of the support leg is considered in the model of double-link LIPM (DLIPM) [26] to eliminate the conflicting assumption about the LIPM. There are four assumptions in DLIPM, but the weight of the support leg is assumed to be one mass.…”

Section: Dynamic Model Of Humanoid Robotmentioning

confidence: 99%

“…where The ZMP trajectory of the walking trajectory generator is generated based on the moving ZMP reference, which is the natural ZMP trajectory [26], [31], so that the ZMP of the humanoid robot during biped walking can be similar to the human ZMP. The natural ZMP trajectory in the sagittal plane and the lateral plane is shown in Fig.…”

Section: A Zmp Trajectorymentioning

confidence: 99%

“…The rotational inertia, which is the natural human movement, is enabled to control the angular momentum of the humanoid robot. In addition, a double-link linear inverted pendulum model (DLIPM) was proposed and applied to the walking robots [26], [27]. Compared with the LIPM, the DLIPM equation of motion with the mass of the support leg is built to design the CoM trajectory for the humanoid robot.…”

Section: Introductionmentioning

confidence: 99%

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Natural Walking Trajectory Generator for Humanoid Robot Based on Three-Mass LIPFM

2020

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“…Biped robot walking based on the zero moment point (ZMP) has been widely applied and achieved remarkable achievements [1][2][3][4][5]. Given dynamic degree of biped walking, according to the relation between projection of center of mass (CoM), ZMP, and supporting convex polygons, walking can be divided into static walking, quasi-dynamic walking, and dynamic walking.…”

Section: Introductionmentioning

confidence: 99%

Stable walking of biped robot based on center of mass trajectory control

Jing

Zheng

2020

Open Physics

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The effect of center of mass (CoM) trajectory on stable walking of biped robot was studied in this study. The method of predictive control by controlling CoM trajectory to generate stable walking patterns was discussed, which simulated the principle of adjusting center of gravity in advance during human walking to adapt to the change of road conditions. As for uncertainty of robot modeling and the influence of environment, adding variable zero moment point for variable predictive control system can achieve self-adaptive adjustment of CoM trajectory to generate stable walking patterns. The simulation experiment results indicated that walking stability of biped robot was sensitive to the change of CoM trajectory. As long as CoM trajectory was adjusted well, stable walking of biped robot can be controlled even in the presence of disturbances. It is proved that the method of predictive control by controlling CoM motion to generate stable walking pattern is consistent with reality.

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Natural Walking Reference Generation Based on Double-Link LIPM Gait Planning Algorithm

Cited by 18 publications

References 33 publications

Fuzzy Dynamic Gait Pattern Generation for Real-Time Push Recovery Control of a Teen-Sized Humanoid Robot

Fuzzy Dynamic Gait Pattern Generation for Real-Time Push Recovery Control of a Teen-Sized Humanoid Robot

Natural Walking Trajectory Generator for Humanoid Robot Based on Three-Mass LIPFM

Stable walking of biped robot based on center of mass trajectory control

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