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.

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.

Computer simulation of sensor-based robot collision avoidance in an unknown environment

Robotica ◽  
1987 ◽  
Vol 5 (4) ◽  
pp. 291-302 ◽  
Author(s):  
K. Sun ◽  
V. Lumelsky
Keyword(s):  
Computer Simulation ◽  
Motion Planning ◽  
Collision Avoidance ◽  
Autonomous Vehicle ◽  
Planning System ◽  
Robot Motion ◽  
Robot Arm ◽  
Fixed Base ◽  
The One ◽  
Robot Path

SUMMARYComputer simulation is a major tool in validation of robot motion planning systems, since, on the one hand, underlying theory of algorithms typically requires questionable assumptions and simplifications, and, on the other hand, experiments with hardware are necessarily limited by available resources and time. This is especially true when the motion planning system in question is based on sensor feedback and the generated trajectory is, therefore, unpredictable. This paper describes a simulation system ROPAS (for RObot PAth Simulation) for testing one approach — called Dynmic Path Planning (DPP) — to sensor-based robot collision avoidance in an environment with unknown obstacles. Using real time graphics animation of the motion planning system, the user can simulate the behavior of an autonomous vehicle or a robot arm manipulator with a fixed base. The overall structure of the system is described, and examples are presented.

MASSIVELY PARALLEL IDA* SEARCH

1993 ◽  
Vol 02 (02) ◽  
pp. 163-180 ◽  
Author(s):  
DIANE J. COOK ◽  
GARY LYONS
Keyword(s):  
Artificial Intelligence ◽  
Motion Planning ◽  
Heuristic Search ◽  
Massively Parallel ◽  
Search Process ◽  
Planning Problem ◽  
Robot Arm ◽  
Speed Up ◽  
Arm Motion ◽  
Computational Bottleneck

Heuristic search is a fundamental component of Artificial Intelligence applications. Because search routines are frequently also a computational bottleneck, numerous methods have been explored to increase the efficiency of search. Recently, researchers have begun investigating methods of using parallel MIMD and SIMD hardware to speed up the search process. In this paper, we present a massively-parallel SIMD approach to search named MIDA* search. The components of MIDA* include a very fast distribution algorithm which biases the search to one side of the tree, and an incrementally-deepening depthfirst search of all the processors in parallel. We show the results of applying MIDA* to instances of the Fifteen Puzzle problem and to the robot arm motion planning problem. Results reveal an efficiency of 74% and a speedup of 8553 and 492 over serial and 16-processor MIMD algorithms, respectively, when finding a solution to the Fifteen Puzzle problem that is close to optimal.

Deep learning can accelerate grasp-optimized motion planning

2020 ◽  
Vol 5 (48) ◽  
pp. eabd7710
Author(s):  
Jeffrey Ichnowski ◽  
Yahav Avigal ◽  
Vishal Satish ◽  
Ken Goldberg
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Computation Time ◽  
Robot Arm ◽  
Optimal Motion ◽  
Asymptotically Optimal ◽  
Arm Motions ◽  
Motion Time ◽  
Starting Point ◽  
Arm Motion

Robots for picking in e-commerce warehouses require rapid computing of efficient and smooth robot arm motions between varying configurations. Recent results integrate grasp analysis with arm motion planning to compute optimal smooth arm motions; however, computation times on the order of tens of seconds dominate motion times. Recent advances in deep learning allow neural networks to quickly compute these motions; however, they lack the precision required to produce kinematically and dynamically feasible motions. While infeasible, the network-computed motions approximate the optimized results. The proposed method warm starts the optimization process by using the approximate motions as a starting point from which the optimizing motion planner refines to an optimized and feasible motion with few iterations. In experiments, the proposed deep learning–based warm-started optimizing motion planner reduces compute and motion time when compared to a sampling-based asymptotically optimal motion planner and an optimizing motion planner. When applied to grasp-optimized motion planning, the results suggest that deep learning can reduce the computation time by two orders of magnitude (300×), from 29 s to 80 ms, making it practical for e-commerce warehouse picking.

Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees

2016 ◽  
Vol 30 (3-4) ◽  
pp. 245-255 ◽  
Author(s):  
Jacky Baltes ◽  
Jonathan Bagot ◽  
Soroush Sadeghnejad ◽  
John Anderson ◽  
Chen-Hsien Hsu
Keyword(s):  
Motion Planning ◽  
Humanoid Robots ◽  
Random Trees ◽  
Body Motion ◽  
Rapidly Exploring Random Trees ◽  
Full Body

Assembly Sequence Planning by Using Multiple Random Trees Based Motion Planning

2012 ◽  
Author(s):  
Carlos Morato ◽  
Krishnanand Kaipa ◽  
Satyandra K. Gupta
Keyword(s):  
Motion Planning ◽  
Assembly Sequence Planning ◽  
Random Trees ◽  
Planning System ◽  
Assembly Model ◽  
Assembly Sequence ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Assembly Sequences

In this paper, we introduce multiple random trees based motion planning to perform assembly sequence planning for complex assemblies. Initially, given an assembly model, our technique performs disassembly sequence planning. This approach dynamically reduces the size and complexity of the assembly based on a hierarchical exploration structure that keeps information about the completion of the disassembly. Next, the disassembly information is used to generate feasible assembly sequences, along with precedence constraints, to assemble each part into the current subassembly. The motion planning system chooses part order by detecting geometrical interferences and analyzing feasible part movements. Results from tests on a variety of complex assemblies validate the efficiency of our approach.

scholarly journals A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3515 ◽  
Author(s):  
Chuzhao Liu ◽  
Junyao Gao ◽  
Yuanzhen Bi ◽  
Xuanyang Shi ◽  
Dingkui Tian
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Data Acquisition ◽  
Humanoid Robot ◽  
Data Acquisition System ◽  
Humanoid Robots ◽  
Acquisition System ◽  
Robot Arm ◽  
Digital Twin ◽  
Angle Data

Humanoid robots are equipped with humanoid arms to make them more acceptable to the general public. Humanoid robots are a great challenge in robotics. The concept of digital twin technology complies with the guiding ideology of not only Industry 4.0, but also Made in China 2025. This paper proposes a scheme that combines deep reinforcement learning (DRL) with digital twin technology for controlling humanoid robot arms. For rapid and stable motion planning for humanoid robots, multitasking-oriented training using the twin synchro-control (TSC) scheme with DRL is proposed. For switching between tasks, the robot arm training must be quick and diverse. In this work, an approach for obtaining a priori knowledge as input to DRL is developed and verified using simulations. Two simple examples are developed in a simulation environment. We developed a data acquisition system to generate angle data efficiently and automatically. These data are used to improve the reward function of the deep deterministic policy gradient (DDPG) and quickly train the robot for a task. The approach is applied to a model of the humanoid robot BHR-6, a humanoid robot with multiple-motion mode and a sophisticated mechanical structure. Using the policies trained in the simulations, the humanoid robot can perform tasks that are not possible to train with existing methods. The training is fast and allows the robot to perform multiple tasks. Our approach utilizes human joint angle data collected by the data acquisition system to solve the problem of a sparse reward in DRL for two simple tasks. A comparison with simulation results for controllers trained using the vanilla DDPG show that the designed controller developed using the DDPG with the TSC scheme have great advantages in terms of learning stability and convergence speed.

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