3-D Command State-Based Modifiable Bipedal Walking on Uneven Terrain

Self Cite

-Previous walking pattern generation methods could generate walking patterns that allow only straight walking on flat and uneven terrain. They were unable to generate modifiable walking patterns whereby the sagittal and lateral step lengths and walking direction can be changed at every footstep. This paper proposes a novel walking pattern generation method to realize modifiable walking of humanoid robots on unknown uneven terrain. The proposed method employs a walking pattern generator based on the 3-D linear inverted pendulum model (LIPM), which enables a humanoid robot to vary its walking patterns at every footstep. A control strategy for walking on unknown uneven terrain is proposed. Virtual spring-damper (VSD) models are used to compensate for the disturbances that occur between the robot and the terrain when the robot walks on uneven terrain with unknown height. In addition, methods for generating the foot and vertical center of mass (COM) of the 3-D LIPM trajectories are developed to realize stable walking on unknown uneven terrain. The proposed method is implemented on a small-sized humanoid robot platform, DARwIn-OP and its effectiveness is demonstrated experimentally.

Section: Generation Of Modifiable Walking Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control Strategy for Modifiable Bipedal Walking on Unknown Uneven Terrain

Lee¹,

Chwa²,

Hong³

2016

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“…To implement a walking pattern in real time, reduced dynamic models such as the single linear inverted pendulum are commonly utilized. Because the inverted pendulum model effectively reflects the dominant dynamics of the walking motion, many successful results have been reported [1][2][3][4]. Other approaches reduce the complexity of the equation of motion by assuming that the zero moment point (ZMP) trajectory has a specific form [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Simulation of Modifiable Walking Pattern Generation to Handle Infeasible Navigational Commands for Humanoid Robots

Hong¹,

Lee²,

Lee³

2016

Self Cite

-The modifiable walking pattern generation (MWPG) algorithm can handle dynamic walking commands by changing the walking period, step length, and direction independently. When an infeasible command is given, the algorithm changes the command to a feasible one. After the feasibility of the navigational command is checked, it is translated into the desired center of mass (CM) state. To achieve the desired CM state, a reference CM trajectory is generated using predefined zero moment point (ZMP) functions. Based on the proposed algorithm, various complex walking patterns were generated, including backward and sideways walking. The effectiveness of the patterns was verified in dynamic simulations using the Webots simulator.

“…Because of the innate difficulty of solving the differential algebraic equations (DAEs) that represent aspects of dynamic motion, including the contact problem, simplified versions of the equations have been devised for practical use. One such simplification, the linear inverted pendulum model, has attracted the attention of many researchers because of its ease of implementation [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Modifiable Walking Pattern Generation for Handling Infeasible Navigational Commands

Hong¹,

Lee²

2015

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-To accommodate various navigational commands, a humanoid should be able to change its walking motion in real time. Using the modifiable walking pattern generation (MWPG) algorithm, a humanoid can handle dynamic walking commands by changing its walking period, step length, and direction independently. If the humanoid is given a command to perform an infeasible movement, the algorithm substitutes the infeasible command with a feasible one using binary search. The feasible navigational command is subsequently translated into the desired center-of-mass (CM) state. Every sample time CM reference is generated using a zero-moment-point (ZMP) variation scheme. Based on this algorithm, various complex walking patterns can be generated, including backward and sideways walking, without detailed consideration of the feasibility of the navigational commands. In a previous study, the effectiveness of the MWPG algorithm was verified by dynamic simulation. This paper presents experimental results obtained using the small-sized humanoid robot platform DARwIn-OP.