Task motion planning for anthropomorphic arms based on human arm movement primitives

2020 ◽  
Vol 47 (5) ◽  
pp. 669-681
Author(s):  
Shiqiu Gong ◽  
Jing Zhao ◽  
Ziqiang Zhang ◽  
Biyun Xie
Keyword(s):  
Motion Planning ◽  
Arm Movement ◽  
Humanoid Robots ◽  
Planning Method ◽  
Service Robots ◽  
Joint Angles ◽  
Content Type ◽  
Structure And Motion ◽  
Human Arm ◽  
Arm Motion

Purpose This paper aims to introduce the human arm movement primitive (HAMP) to express and plan the motions of anthropomorphic arms. The task planning method is established for the minimum task cost and a novel human-like motion planning method based on the HAMPs is proposed to help humans better understand and plan the motions of anthropomorphic arms. Design/methodology/approach The HAMPs are extracted based on the structure and motion expression of the human arm. A method to slice the complex tasks into simple subtasks and sort subtasks is proposed. Then, a novel human-like motion planning method is built through the selection, sequencing and quantification of HAMPs. Finally, the HAMPs are mapped to the traditional joint angles of a robot by an analytical inverse kinematics method to control the anthropomorphic arms. Findings For the exploration of the motion laws of the human arm, the human arm motion capture experiments on 12 subjects are performed. The results show that the motion laws of human arm are reflected in the selection, sequencing and quantification of HAMPs. These motion laws can facilitate the human-like motion planning of anthropomorphic arms. Originality/value This study presents the HAMPs and a method for selecting, sequencing and quantifying them in human-like style, which leads to a new motion planning method for the anthropomorphic arms. A similar methodology is suitable for robots with anthropomorphic arms such as service robots, upper extremity exoskeleton robots and humanoid robots.

Are Complex Control Signals Required for Human Arm Movement?

1998 ◽  
Vol 79 (3) ◽  
pp. 1409-1424 ◽  
Author(s):  
Paul L. Gribble ◽  
David J. Ostry ◽  
Vittorio Sanguineti ◽  
Rafael Laboissière
Keyword(s):  
Joint Stiffness ◽  
Arm Movement ◽  
Movement Speed ◽  
Muscle Properties ◽  
Complex Control ◽  
Equilibrium Trajectory ◽  
Control Signals ◽  
Human Arm ◽  
Constant Rate ◽  
Arm Motion

Gribble, Paul L., David J. Ostry, Vittorio Sanguineti, and Rafael Laboissière. Are complex control signals required for human arm movement? J. Neurophysiol. 79: 1409–1424, 1998. It has been proposed that the control signals underlying voluntary human arm movement have a “complex” nonmonotonic time-varying form, and a number of empirical findings have been offered in support of this idea. In this paper, we address three such findings using a model of two-joint arm motion based on the λ version of the equilibrium-point hypothesis. The model includes six one- and two-joint muscles, reflexes, modeled control signals, muscle properties, and limb dynamics. First, we address the claim that “complex” equilibrium trajectories are required to account for nonmonotonic joint impedance patterns observed during multijoint movement. Using constant-rate shifts in the neurally specified equilibrium of the limb and constant cocontraction commands, we obtain patterns of predicted joint stiffness during simulated multijoint movements that match the nonmonotonic patterns reported empirically. We then use the algorithm proposed by Gomi and Kawato to compute a hypothetical equilibrium trajectory from simulated stiffness, viscosity, and limb kinematics. Like that reported by Gomi and Kawato, the resulting trajectory was nonmonotonic, first leading then lagging the position of the limb. Second, we address the claim that high levels of stiffness are required to generate rapid single-joint movements when simple equilibrium shifts are used. We compare empirical measurements of stiffness during rapid single-joint movements with the predicted stiffness of movements generated using constant-rate equilibrium shifts and constant cocontraction commands. Single-joint movements are simulated at a number of speeds, and the procedure used by Bennett to estimate stiffness is followed. We show that when the magnitude of the cocontraction command is scaled in proportion to movement speed, simulated joint stiffness varies with movement speed in a manner comparable with that reported by Bennett. Third, we address the related claim that nonmonotonic equilibrium shifts are required to generate rapid single-joint movements. Using constant-rate equilibrium shifts and constant cocontraction commands, rapid single-joint movements are simulated in the presence of external torques. We use the procedure reported by Latash and Gottlieb to compute hypothetical equilibrium trajectories from simulated torque and angle measurements during movement. As in Latash and Gottlieb, a nonmonotonic function is obtained even though the control signals used in the simulations are constant-rate changes in the equilibrium position of the limb. Differences between the “simple” equilibrium trajectory proposed in the present paper and those that are derived from the procedures used by Gomi and Kawato and Latash and Gottlieb arise from their use of simplified models of force generation.

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.

Humanoid Arm Motion Planning Using Stereo Vision and RRT Search

2003 ◽  
Vol 15 (2) ◽  
pp. 200-207 ◽  
Author(s):  
Satoshi Kagami ◽  
◽  
James J. Kuffner ◽  
Koichi Nishiwaki ◽  
Kei Okada ◽  
...  
Keyword(s):  
Motion Planning ◽  
Stereo Vision ◽  
Humanoid Robots ◽  
Random Trees ◽  
Planning System ◽  
Robot Arm ◽  
Consistency Checking ◽  
Disparity Map ◽  
Arm Motion ◽  
High Level

This paper describes an experimental stereo vision based motion planning system for humanoid robots. The goal is to automatically generate arm trajectories that avoid obstacles in unknown environments from high-level task commands. Our system consists of three components: 1) environment sensing using stereo vision with disparity map generation and online consistency checking, 2) probabilistic mesh modeling in order to accumulate continuous vision input, and 3) motion planning for the robot arm using RRTs (Rapidly exploring Random Trees). We demonstrate results from experiments using an implementation designed for the humanoid robot H7.

2A2-D22 A Global Motion Planning Method for Humanoid Robots

2010 ◽  
Vol 2010 (0) ◽  
pp. _2A2-D22_1-_2A2-D22_3 ◽  
Author(s):  
Hiroyuki TSUKAGOSHI ◽  
Eiichi YOSHIDA ◽  
Kazuhito YOKOI
Keyword(s):  
Motion Planning ◽  
Humanoid Robots ◽  
Planning Method ◽  
Global Motion

The Pransky interview: Dr James Kuffner, CEO at Toyota Research Institute Advanced Development, Coinventor of the rapidly, exploring random tree algorithm

2019 ◽  
Vol 47 (1) ◽  
pp. 7-11
Author(s):  
Joanne Pransky
Keyword(s):  
Motion Planning ◽  
Speech Synthesis ◽  
Industrial Robot ◽  
Humanoid Robots ◽  
Random Tree ◽  
Content Type ◽  
Cloud Robotics ◽  
Engineering Team ◽  
Advanced Development ◽  
Research Institute

Purpose The following article 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 his pioneering efforts of bringing technological inventions to market. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr James Kuffner, CEO at Toyota Research Institute Advanced Development (TRI-AD). Kuffner is a proven entrepreneur and inventor in robot and motion planning and cloud robotics. In this interview, Kuffner shares his personal and professional journey from conceptualization to commercial realization. Findings Dr Kuffner received BS, MS and PhD degrees from the Stanford University’s Department of Computer Science Robotics Laboratory. He was a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo where he worked on software and planning algorithms for humanoid robots. He joined the faculty at Carnegie Mellon University’s Robotics Institute in 2002 where he served until March 2018. Kuffner was a Research Scientist and Engineering Director at Google from 2009 to 2016. In January 2016, he joined TRI where he was appointed the Chief Technology Officer and Area Lead, Cloud Intelligence and is presently an Executive Advisor. He has been CEO of TRI-AD since April of 2018. Originality/value Dr Kuffner is perhaps best known as the co-inventor of the rapidly exploring random tree (RRT) algorithm, which has become a key standard benchmark for robot motion planning. He is also known for introducing the term “Cloud Robotics” in 2010 to describe how network-connected robots could take advantage of distributed computation and data stored in the cloud. Kuffner was part of the initial engineering team that built Google’s self-driving car. He was appointed Head of Google’s Robotics Division in 2014, which he co-founded with Andy Rubin to help realize the original Cloud Robotics concept. Kuffner also co-founded Motion Factory, where he was the Senior Software Engineer and a member of the engineering team to develop C++ based authoring tools for high-level graphic animation and interactive multimedia content. Motion Factory was acquired by SoftImage in 2000. In May 2007, Kuffner founded, and became the Director of Robot Autonomy where he coordinated research and software consulting for industrial and consumer robotics applications. In 2008, he assisted in the iOS development of Jibbigo, the first on-phone, real-time speech recognition, translation and speech synthesis application for the iPhone. Jibbigo was acquired by Facebook in 2013. Kuffner is one of the most highly cited authors in the field of robotics and motion planning, with over 15,000 citations. He has published over 125 technical papers and was issued more than 50 patents related to robotics and computer vision technology.

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.

Energy-optimal motion planning of a biped pole-climbing robot with kinodynamic constraints

2018 ◽  
Vol 45 (3) ◽  
pp. 343-353 ◽  
Author(s):  
Xuefeng Zhou ◽  
Li Jiang ◽  
Yisheng Guan ◽  
Haifei Zhu ◽  
Dan Huang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Outdoor Environment ◽  
Planning Problem ◽  
Climbing Robot ◽  
Optimal Motion ◽  
Climbing Robots ◽  
Content Type ◽  
Optimal Motion Planning

Purpose Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment. Design/methodology/approach First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively. Findings Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper. Research limitations/implications Only one step climbing motion planning is considered in this method. Practical implications With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint. Originality/value The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

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