scholarly journals A Review of Motion Planning Algorithms for Robotic Arm Systems

2021 ◽  
pp. 56-66
Author(s):  
Shuai Liu ◽  
Pengcheng Liu
Keyword(s):  
Motion Planning ◽  
Robotic Arm ◽  
Planning Algorithms

Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

2016 ◽  
Vol 23 (4) ◽  
pp. 107-117 ◽  
Author(s):  
J.J.M. Lunenburg ◽  
S.A.M. Coenen ◽  
G.J.L. Naus ◽  
M.J.G. van de Molengraft ◽  
M. Steinbuch
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Planning Algorithms ◽  
Selection Of

Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

2019 ◽  
Vol 16 (04) ◽  
pp. 1950012 ◽  
Author(s):  
Mircea Hulea ◽  
Adrian Burlacu ◽  
Constantin-Florin Caruntu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Control Method ◽  
Inhibitory Neurons ◽  
Robotic Arm ◽  
Control Approach ◽  
Planning And Control ◽  
Robotic Joints ◽  
And Control

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

A scalable method for parallelizing sampling-based motion planning algorithms

2012 ◽  
Author(s):  
Sam Ade Jacobs ◽  
Kasra Manavi ◽  
Juan Burgos ◽  
Jory Denny ◽  
Shawna Thomas ◽  
...  
Keyword(s):  
Motion Planning ◽  
Planning Algorithms

Tool path optimization for robotic surface machining by using sampling-based motion planning algorithms

2020 ◽  
pp. 1-28
Author(s):  
Lu Lei ◽  
Jiong Zhang ◽  
Xiaoqing Tian ◽  
Jiang Han ◽  
Hao Wang
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Tool Path ◽  
Path Optimization ◽  
Machining Process ◽  
Axis Orientation ◽  
Surface Machining ◽  
Tool Path Optimization ◽  
Tool Axis ◽  
Planning Algorithms

Abstract This paper develops a tool path optimization method for robot surface machining by sampling-based motion planning algorithms. In the surface machining process, the tool-tip position needs to strictly follow the tool path curve and the posture of the tool axis should be limited in a certain range. But the industrial robot has at least six degrees of freedom (Dof) and has redundant Dofs for surface machining. Therefore, the tool motion of surface machining can be optimized using the redundant Dofs considering the tool path constraints and limits of the tool axis orientation. Due to the complexity of the problem, the sampling-based motion planning method has been chosen to find the solution, which randomly explores the configuration space of the robot and generates a discrete path of valid robot state. During the solving process, the joint space of the robot is chosen as the configuration space of the problem and the constraints for the tool-tip following requirements are in the operation space. Combined with general collision checking, the limited region of the tool axis vector is used to verify the state's validity of the configuration space. In the optimization process, the sum of path length of each joint of the robot is set as the optimization objective. The algorithm is developed based on the open motion planning library (OMPL) which contains the state-of-the-art sampling-based motion planners. Finally, two examples are used to demonstrate the effectiveness and optimality of the method.

SEQUENTIAL COLLISION-FREE OPTIMAL MOTION PLANNING ALGORITHMS IN PUNCTURED EUCLIDEAN SPACES

2020 ◽  
Vol 102 (3) ◽  
pp. 506-516
Author(s):  
CESAR A. IPANAQUE ZAPATA ◽  
JESÚS GONZÁLEZ
Keyword(s):  
Euclidean Space ◽  
Motion Planning ◽  
Euclidean Spaces ◽  
Optimal Motion ◽  
Topological Theory ◽  
Theory Of Motion ◽  
Optimal Motion Planning ◽  
Planning Algorithms

In robotics, a topological theory of motion planning was initiated by M. Farber. We present optimal motion planning algorithms which can be used in designing practical systems controlling objects moving in Euclidean space without collisions between them and avoiding obstacles. Furthermore, we present the multi-tasking version of the algorithms.

A topological implementation for motion planning of a robotic arm

1993 ◽  
Vol 8 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Yongcheng Li ◽  
Bo Zhang
Keyword(s):  
Motion Planning ◽  
Robotic Arm

Improving Motion-Planning Algorithms by Efficient Nearest-Neighbor Searching

2007 ◽  
Vol 23 (1) ◽  
pp. 151-157 ◽  
Author(s):  
Anna Yershova ◽  
Steven M. LaValle
Keyword(s):  
Motion Planning ◽  
Nearest Neighbor ◽  
Nearest Neighbor Searching ◽  
Planning Algorithms
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