Jumping Motion Generation of a Humanoid Robot Utilizing Human-Like Joint Elasticity

Author(s):  
T. Otani ◽  
K. Hashimoto ◽  
H. Ueta ◽  
M. Sakaguchi ◽  
Y. Kawakami ◽  
...  
2017 ◽  
Vol 14 (01) ◽  
pp. 1650022 ◽  
Author(s):  
Tianwei Zhang ◽  
Stéphane Caron ◽  
Yoshihiko Nakamura

Stair climbing is still a challenging task for humanoid robots, especially in unknown environments. In this paper, we address this problem from perception to execution. Our first contribution is a real-time plane-segment estimation method using Lidar data without prior models of the staircase. We then integrate this solution with humanoid motion planning. Our second contribution is a stair-climbing motion generator where estimated plane segments are used to compute footholds and stability polygons. We evaluate our method on various staircases. We also demonstrate the feasibility of the generated trajectories in a real-life experiment with the humanoid robot HRP-4.


Author(s):  
Genci Capi ◽  
Yasuo Nasu ◽  
Mitsuhiro Yamano ◽  
Kazuhisa Mitobe

2012 ◽  
Vol 591-593 ◽  
pp. 1386-1390
Author(s):  
Bo Tu ◽  
Dan Pu Zhao ◽  
Xian Qing Tai

Walking motion generation and validation have been a significant issue for biped humanoid robot. To generate more natural walking motions, and confirm the validity rapidly, this paper presents work on walking motion planning and validity verifying. Based on spline interpolation method, the walking motions in both sagittal and lateral planes are generated. Dynamic model is constructed with the toolbox of SimMechanics for Matlab, and the interactive forces between robot’s sole and ground are constrained in order to depict the state of balance. Dynamic model is driven by the walking motions which have been generated. The simulation and analysis demonstrate the validity of the motion which has been designed.


2009 ◽  
Vol 06 (01) ◽  
pp. 71-91 ◽  
Author(s):  
MARIO ARBULU ◽  
CARLOS BALAGUER

This paper presents the 3D foot and center of gravity motion planning for the humanoid robot called the "local axis gait" (LAG) algorithm. It permits walking on different kinds of surfaces, such as planes, ramps or stairs. Furthermore, continuous change of the step length and orientation in real time will be possible, due to the real-time linear dynamics model of the walking pattern of the humanoid. The robot model is based on the cart table formulation for planning the center of gravity (COG) and zero moment point (ZMP) motion. The proposed algorithm takes into account physical robot constraints such as joint angles, angular velocity and torques. Torques are computed by the Lagrange method under screws and Lie groups. The LAG is divided into several stages: computation of the footprints; the decision of the ZMP limits around the footprints; the dynamic humanoid COG motion generation based on the cart table model; and joining the footprints of the swing foot by splines. In this way it is possible to generate each step online, using the desired footprints as input. In order to compute the joint torque limits, the Lagrangian method is used under the Lie groups and screw theory. The paper presents and discusses some successful results on the LAG in the full-size humanoid robot Rh-1 developed in the Roboticslab of University Carlos III of Madrid.


2011 ◽  
Vol 23 (2) ◽  
pp. 239-248 ◽  
Author(s):  
Shunichi Nozawa ◽  
◽  
Ryohei Ueda ◽  
Yohei Kakiuchi ◽  
Kei Okada ◽  
...  

The novel method we propose involves a humanoid robot manipulating objects of varying size and weight. How an object is manipulated is generally determined by size and weight. The motion generation system we developed 1) utilizes manipulation strategies defined by which contact points on the robot are to be used, 2) selects the adequate manipulation strategy based on the object, and 3) generates a full-body posture sequence for the humanoid robot with controlled reaction forces and full-body balance using the manipulation strategy as an initial condition. Our system enables the robot to manipulate an object of weight thanks to multiple strategies. Our method’s effectiveness is confirmed in experiments in which a humanoid robot manipulates six different types of objects.


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