Efficient Locomotion Planning for a Humanoid Robot with Whole-Body Collision Avoidance Guided by Footsteps and Centroidal Sway Motion

2018 ◽  
Author(s):  
Iori Kumagai ◽  
Mitsuharu Morisawa ◽  
Shin'ichiro Nakaoka ◽  
Fumio Kanehiro
Keyword(s):  
Collision Avoidance ◽  
Humanoid Robot ◽  
Whole Body ◽  
Sway Motion

On-Site Locomotion Planning for a Humanoid Robot with Stable Whole-Body Collision Avoidance Motion Guided by Footsteps and Centroidal Trajectory

2019 ◽  
Vol 17 (01) ◽  
pp. 1950035
Author(s):  
Iori Kumagai ◽  
Mitsuharu Morisawa ◽  
Shin’ichiro Nakaoka ◽  
Fumio Kanehiro
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Dynamic Simulation ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Computational Cost ◽  
Body Motion ◽  
Whole Body ◽  
Planning Framework ◽  
Narrow Space

In this paper, we propose a locomotion planning framework for a humanoid robot with stable whole-body collision avoidance motion, which enables the robot to traverse an unknown narrow space on the spot based on environmental measurements. The key idea of the proposed method is to reduce a large computational cost for the whole-body locomotion planning by utilizing global footstep planning results and its centroidal trajectory as a guide. In the global footstep planning phase, we modify the bounding box of the robot approximating the centroidal sway amplitude of the candidate footsteps. This enables the planner to obtain appropriate footsteps and transition time for next whole-body motion planning. Then, we execute sequential whole-body motion planning by prioritized inverse kinematics considering collision avoidance and maintaining its ZMP trajectory, which enables the robot to plan stable motion for each step in 223[Formula: see text]ms at worst. We evaluated the proposed framework by a humanoid robot HRP-5P in the dynamic simulation and the real world. The major contribution of our paper is solving the problem of increasing computational cost for whole-body motion planning and enabling a humanoid robot to execute adaptive on-site locomotion planning in an unknown narrow space.

scholarly journals Energy-Efficient Hip Joint Offsets in Humanoid Robot via Taguchi Method and Bio-inspired Analysis

2020 ◽  
Vol 10 (20) ◽  
pp. 7287
Author(s):  
Jihun Kim ◽  
Jaeha Yang ◽  
Seung Tae Yang ◽  
Yonghwan Oh ◽  
Giuk Lee
Keyword(s):  
Energy Efficiency ◽  
Taguchi Method ◽  
Power Consumption ◽  
Hip Joint ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Upper Body ◽  
Mean Square ◽  
Hip Joints ◽  
Sway Motion

Although previous research has improved the energy efficiency of humanoid robots to increase mobility, no study has considered the offset between hip joints to this end. Here, we optimized the offsets of hip joints in humanoid robots via the Taguchi method to maximize energy efficiency. During optimization, the offsets between hip joints were selected as control factors, and the sum of the root-mean-square power consumption from three actuated hip joints was set as the objective function. We analyzed the power consumption of a humanoid robot model implemented in physics simulation software. As the Taguchi method was originally devised for robust optimization, we selected turning, forward, backward, and sideways walking motions as noise factors. Through two optimization stages, we obtained near-optimal results for the humanoid hip joint offsets. We validated the results by comparing the root-mean-square (RMS) power consumption of the original and optimized humanoid models, finding that the RMS power consumption was reduced by more than 25% in the target motions. We explored the reason for the reduction of power consumption through bio-inspired analysis from human gait mechanics. As the distance between the left and right hip joints in the frontal plane became narrower, the amplitude of the sway motion of the upper body was reduced. We found that the reduced sway motion of the upper body of the optimized joint configuration was effective in improving energy efficiency, similar to the influence of the pathway of the body’s center of gravity (COG) on human walking efficiency.

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