Motion planning for humanoid robot dynamically stepping over consecutive large obstacles

2016 ◽  
Vol 43 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Fayong Guo ◽  
Tao Mei ◽  
Minzhou Luo ◽  
Marco Ceccarelli ◽  
Ziyi Zhao ◽  
...  
Keyword(s):  
Motion Planning ◽  
Trajectory Planning ◽  
Humanoid Robot ◽  
Extreme Case ◽  
Humanoid Robots ◽  
Decision Criterion ◽  
Body Motion ◽  
Feasibility Analysis ◽  
Upper Body ◽  
Content Type

Purpose – Humanoid robots should have the ability of walking in complex environment and overcoming large obstacles in rescue mission. Previous research mainly discusses the problem of humanoid robots stepping over or on/off one obstacle statically or dynamically. As an extreme case, this paper aims to demonstrate how the robots can step over two large obstacles continuously. Design/methodology/approach – The robot model uses linear inverted pendulum (LIP) model. The motion planning procedure includes feasibility analysis with constraints, footprints planning, legs trajectory planning with collision-free constraint, foot trajectory adapter and upper body motion planning. Findings – The motion planning with the motion constraints is a key problem, which can be considered as global optimization issue with collision-free constraint, kinematic limits and balance constraint. With the given obstacles, the robot first needs to determine whether it can achieve stepping over, if feasible, and then the robot gets the motion trajectory for the legs, waist and upper body using consecutive obstacles stepping over planning algorithm which is presented in this paper. Originality/value – The consecutive stepping over problem is proposed in this paper. First, the paper defines two consecutive stepping over conditions, sparse stepping over (SSO) and tight stepping over (TSO). Then, a novel feasibility analysis method with condition (SSO/TSO) decision criterion is proposed for consecutive obstacles stepping over. The feasibility analysis method’s output is walking parameters with obstacles’ information. Furthermore, a modified legs trajectory planning method with center of mass trajectory compensation using upper body motion is proposed. Finally, simulations and experiments for SSO and TSO are carried out by using the XT-I humanoid robot platform with the aim to verify the validity and feasibility of the novel methods proposed in this paper.

The Pransky interview: Dr Jun Ho Oh, Professor and Director of Humanoid Robot Research Center, KAIST; Cofounder, Rainbow Robotics Co.

2017 ◽  
Vol 44 (6) ◽  
pp. 695-699
Author(s):  
Joanne Pransky
Keyword(s):  
Mechanical Engineering ◽  
Humanoid Robot ◽  
Industrial Robot ◽  
Science And Technology ◽  
Vice President ◽  
Humanoid Robots ◽  
Real Time Control ◽  
Commercial Development ◽  
Content Type ◽  
The University

Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned-entrepreneur regarding the commercialization and challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Jun Ho Oh, Professor of Mechanical Engineering at the Korea Advanced Institute of Science and Technology (KAIST) and Director of KAIST’s Hubolab. Determined to build a humanoid robot in the early 2000s to compete with Japan’s humanoids, Dr Oh and KAIST created the KHR1. This research led to seven more advanced versions of a biped humanoid robot and the founding of the Robot for Artificial Intelligence and Boundless Walking (Rainbow) Co., a professional technological mechatronics company. In this interview, Dr Oh shares the history and success of Korea’s humanoid robot research. Findings Dr Oh received his BSc in 1977 and MSc in Mechanical Engineering in 1979 from Yonsei University. Oh worked as a Researcher for the Korea Atomic Energy Research Institute before receiving his PhD from the University of California (UC) Berkeley in mechanical engineering in 1985. After his PhD, Oh remained at UC Berkeley to do Postdoctoral research. Since 1985, Oh has been a Professor of Mechanical Engineering at KAIST. He was a Visiting Professor from 1996 to 1997 at the University of Texas Austin. Oh served as the Vice President of KAIST from 2013-2014. In addition to teaching, Oh applied his expertise in robotics, mechatronics, automatic and real-time control to the commercial development of a series of humanoid robots. Originality/value Highly self-motivated and always determined, Dr Oh’s initial dream of building the first Korean humanoid bipedal robot has led him to become one of the world leaders of humanoid robots. He has contributed widely to the field over the nearly past two decades with the development of five versions of the HUBO robot. Oh led Team KAIST to win the 2015 DARPA Robotics Challenge (DRC) and a grand prize of US$2m with its humanoid robot DRC-HUBO+, beating 23 teams from six countries. Oh serves as a robotics policy consultant for the Korean Ministry of Commerce Industry and Energy. He was awarded the 2016 Changjo Medal for Science and Technology, the 2016 Ho-Am Prize for engineering, and the 2010 KAIST Distinguished Professor award. He is a member of the Korea Academy of Science and Technology.

scholarly journals Efficient Planning of Humanoid Motions by Modifying Constraints

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
ChangHyun Sung ◽  
Takahiro Kagawa ◽  
Yoji Uno
Keyword(s):  
Motion Planning ◽  
Sufficient Conditions ◽  
Computation Time ◽  
Humanoid Robots ◽  
Body Motion ◽  
Whole Body ◽  
Planning Problem ◽  
Point Representation ◽  
Efficient Planning ◽  
Unpredictable Environment

AbstractIn this paper, we propose an effective planning method for whole-body motions of humanoid robots under various conditions for achieving the task. In motion planning, various constraints such as range of motion have to be considered. Specifically, it is important to maintain balance in whole-body motion. In order to be useful in an unpredictable environment, rapid planning is an essential problem. In this research, via-point representation is used for assigning sufficient conditions to deal with various constraints in the movement. The position, posture and velocity of the robot are constrained as a state of a via-point. In our algorithm, the feasible motions are planned by modifying via-points. Furthermore, we formulate the motion planning problem as a simple iterative method with a Linear Programming (LP) problem for efficiency of the motion planning. We have applied the method to generate the kicking motion of a HOAP-3 humanoid robot. We confirmed that the robot can successfully score a goal with various courses corresponding to changing conditions of the location of an obstacle. The computation time was less than two seconds. These results indicate that the proposed algorithm can achieve efficient motion planning.

Supervoxel Plane Segmentation and Multi-Contact Motion Generation for Humanoid Stair Climbing

2017 ◽  
Vol 14 (01) ◽  
pp. 1650022 ◽  
Author(s):  
Tianwei Zhang ◽  
Stéphane Caron ◽  
Yoshihiko Nakamura
Keyword(s):  
Motion Planning ◽  
Humanoid Robot ◽  
Real Life ◽  
Estimation Method ◽  
Humanoid Robots ◽  
Stair Climbing ◽  
Lidar Data ◽  
Motion Generation ◽  
Prior Models ◽  
Motion Generator

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.

Dynamics and Simulation of General Human and Humanoid Motion in Sports

2011 ◽  
pp. 36-62 ◽  
Author(s):  
Veljko Potkonjak ◽  
Miomir Vukobratovic ◽  
Kalman Babkovic ◽  
Branislav Borovac
Keyword(s):  
Mathematical Models ◽  
Motion Planning ◽  
Mathematical Analysis ◽  
Humanoid Robot ◽  
Dynamic Control ◽  
Humanoid Robots ◽  
Assistive Devices ◽  
Kinematics And Dynamics ◽  
Biologically Inspired ◽  
Biologically Inspired Robot

This chapter relates biomechanics to robotics. The mathematical models are derived to cover the kinematics and dynamics of virtually any motion of a human or a humanoid robot. Benefits for humanoid robots are seen in fully dynamic control and a general simulator for the purpose of system designing and motion planning. Biomechanics in sports and medicine can use these as a tool for mathematical analysis of motion and disorders. Better results in sports and improved diagnostics are foreseen. This work is a step towards the biologically-inspired robot control needed for a diversity of tasks expected in humanoids, and robotic assistive devices helping people to overcome disabilities or augment their physical potentials. This text deals mainly with examples coming from sports in order to justify this aspect of research.

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.

First human–robot archery competition: a new humanoid robot challenge

2020 ◽  
Vol 35 ◽  
Author(s):  
Kuo-Yang Tu ◽  
Hong-Yu Lin ◽  
You-Ru Li ◽  
Che-Ping Hung ◽  
Jacky Baltes
Keyword(s):  
Motion Planning ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Special Design ◽  
Design And Development ◽  
Trade Off ◽  
Design And Implementation ◽  
Structure And Motion ◽  
Further Development

Abstract A humanoid robot developed to play multievent athletes like human has paved a way for interesting and popular robotics research. One of the great dreams is to develop a humanoid robot being able to challenge human athletes. Therefore, the challenge of humanoid robots to play archery against human is organized at Taichung, Taiwan, in HuroCup, FIRA 2018, on August 7th. The difficulties of developing humanoid robot are not just on playing archery. The humanoid robots for HuroCup must make use of the same hardware for the 10 events. In this paper, the design and implementation of the humanoid robot for archery are proposed under the trade off with other nine events. Therefore, the humanoid robot must have some special design and development on software. More specially, the humanoid robot must use professional bow to challenge human for archery competition. Therefore, in this paper, special shooting posture under constrained arm structure and motion planning of both arms for more torque to play professional bow are proposed. In addition, the further development of humanoid robot to improve archery shooting is summarized.

scholarly journals Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach

2013 ◽  
Vol 32 (9-10) ◽  
pp. 1089-1103 ◽  
Author(s):  
Sébastien Dalibard ◽  
Antonio El Khoury ◽  
Florent Lamiraux ◽  
Alireza Nakhaei ◽  
Michel Taïx ◽  
...  
Keyword(s):  
Motion Planning ◽  
Integrated Approach ◽  
Humanoid Robots ◽  
Body Motion ◽  
Whole Body ◽  
Dynamic Walking

Optimal stable gait for nonlinear uncertain humanoid robot using central force optimization algorithm

2019 ◽  
Vol 36 (2) ◽  
pp. 599-621 ◽  
Author(s):  
Tran Thien Huan ◽  
Ho Pham Huy Anh
Keyword(s):  
Differential Evolution ◽  
Humanoid Robot ◽  
Differential Evolution Algorithm ◽  
Humanoid Robots ◽  
Central Force ◽  
Content Type ◽  
Force Optimization ◽  
Gait Parameters ◽  
Stable Gait ◽  
Central Force Optimization

Purpose The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique. Design/methodology/approach Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution). Findings The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude. Originality/value This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

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