Obstacle avoidance for redundant manipulator without information of the joint angles

2013 ◽  
Author(s):  
Y. J. Yoo ◽  
K. J. Oh ◽  
Y. J. Choi ◽  
S. C. Won
Keyword(s):  
Obstacle Avoidance ◽  
Redundant Manipulator ◽  
Joint Angles

scholarly journals Sensor-based skeleton modeling and MVEEs approach of the redundant manipulator for reaction motion

2022 ◽  
Vol 14 (1) ◽  
pp. 168781402210742
Author(s):  
Lan Ye ◽  
Genliang Xiong ◽  
Hua Zhang ◽  
Cheng Zeng
Keyword(s):  
Obstacle Avoidance ◽  
Reaction Force ◽  
Redundant Manipulator ◽  
Joint Angles ◽  
Damping Term ◽  
Multiple Control ◽  
Multiple Points ◽  
Working Space ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

With the wide application of redundant manipulators, sharing a working space with humans and dealing with uncertainty seems an inevitable problem, especially in the dynamic and unstructured domain. How to deal with obstacle avoidance is of particular importance that robots and humans/environments are safe interactions to fulfill the complex cooperating tasks. This paper aimed at solving the problem of multiple points avoidance for the reaction motion based on the skeleton algorithm in unstructured and dynamic environments. A method named “sensor-based skeleton modeling and MVEEs approach of the redundant manipulator for the reaction motion” is proposed. The extraction of skeleton information from image is obtained to calculate the distances of the multiple control points and establish the repulsion in this method. Afterward, the force Jacobian related to the priority weighting factors is calculated and then a reaction force with damping term is established, which is corresponding nominal torque commands. For the redundant manipulator, the joint angles are obtained through torque iteration instead of inverse kinematics to reduce calculation cost. Finally, the method was tested by a 7-DOF manipulator in the ROS framework. The obtained results indicate that the method in this method can realize dynamic obstacle avoidance and time cost reduction.

scholarly journals Learning of Obstacle Avoidance with Redundant Manipulator by Hierarchical SOM

2011 ◽  
Vol 15 (5) ◽  
pp. 525-531
Author(s):  
Yuichi Kobayashi ◽  
◽  
Takahiro Nomura
Keyword(s):  
Reinforcement Learning ◽  
Obstacle Avoidance ◽  
Inverse Kinematics ◽  
Joint Angle ◽  
Hand Position ◽  
Redundant Manipulator ◽  
Motion Generation ◽  
Joint Angles ◽  
Vision Sensors ◽  
Configuration Information

This paper proposes a method of obstacle avoidance motion generation for a redundant manipulator with a Self-OrganizingMap (SOM) and reinforcement learning. To consider redundancy, two types of SOMs - a hand position map and a joint angle map - are combined. Multiple joint angles corresponding to the same hand position are memorized in the proposed map. Preserved redundant configuration information is used to generate motions based on tasks and situations, while resolving inverse kinematics problems with a redundant manipulator. The proposed map is applied to planning motion control using reinforcement learning in an unknown environment, where collision with obstacles is detected only directly by tactile sensing. The feasibility of the proposed framework was verified by simulation and experiments with an arm robot with force and a vision sensors.

scholarly journals Bi-criteria Acceleration Level Obstacle Avoidance of Redundant Manipulator

2020 ◽  
Vol 14 ◽  
Author(s):  
Weifeng Zhao ◽  
Xiaoxiao Li ◽  
Xin Chen ◽  
Xin Su ◽  
Guanrong Tang
Keyword(s):  
Obstacle Avoidance ◽  
Redundant Manipulator ◽  
Acceleration Level

scholarly journals Obstacle avoidance and multitarget tracking of a super redundant modular manipulator based on Bezier curve and particle swarm optimization

2020 ◽  
Author(s):  
Chen Li ◽  
Ying Ma ◽  
Yu Zhang ◽  
Jinguo Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Optimization Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Redundant Manipulator ◽  
Swarm Optimization ◽  
Composite Optimization

Abstract A super redundant serpentine manipulator has slender structure and multiple degrees of freedom and can travel through narrow space and move in complex space. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents a composite optimization method of path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this composite optimization, path planning is established on a Bezier curve, particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator, and a feasible obstacle avoidance path is obtained along with a discrete trajectory tracking using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this composite optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

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