Landing Force Controller for a Humanoid Robot: Time-Domain Passivity Approach

Self Cite

-In order to accomplish complex navigational commands, humanoid robot should be able to modify its walking period, step length and direction independently. In this paper, a novel walking pattern generation algorithm is proposed to satisfy these requirements. Modification of the walking pattern can be considered as a transition between two periodic walking patterns, which follows each navigational command. By assuming the robot as a linear inverted pendulum, the equations of motion between ZMP(Zero Moment Point) and CM(Center of Mass) state is easily derived and analyzed. After navigational command is translated into the desired CM state, corresponding CM motion is generated to achieve the desired state by using simple ZMP functions. Moreover, when the command is not feasible, feasible command is alternated by using binary search algorithm. Subsequently, corresponding CM motion is generated. The effectiveness of the proposed algorithm is verified by computer simulation.

Section: Introductionmentioning

confidence: 99%

Modifiable Walking Pattern Generation Handling Infeasible Navigational Commands for Humanoid Robots

Lee¹,

Kim²

2014

Self Cite

“…Approaches have been proposed for compensating for the disturbance using heuristic damping control [8,9], the dynamics between a robot and its environment [10,11], and landing force control [12,13]. In this study, the torques and ground reaction force were measured using force-sensing resistors (FSRs) on each foot, and the foot of the robot was modeled using three virtual spring-damper (VSD) models for the purpose of disturbance compensation.…”

Section: Introductionmentioning

confidence: 99%

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

Hong¹,

Lee²

2015

-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.

“…Note that the proposed algorithm is focused on feedforward pattern generation. For stable robot walking, the feedback controllers introduced in [19][20][21][22] need to be adopted. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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

Hong¹,

Lee²,

Lee³

2016

-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.