Motion planning and control method of dexterous manipulation utilizing a simulation system for a multi-fingered robotic hand

2001 ◽  
pp. 309-314
Author(s):  
Chang-Soon Hwang ◽  
Terunao Hirota ◽  
Ken Sasaki
Keyword(s):  
Motion Planning ◽  
Control Method ◽  
Simulation System ◽  
Robotic Hand ◽  
Dexterous Manipulation ◽  
Planning And Control ◽  
And Control

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.

Parking Motion Planning and Control of a Car-Like Robot Using a Fuzzy Neural Network

1995 ◽  
Vol 7 (1) ◽  
pp. 52-56 ◽  
Author(s):  
Motoji Yamamoto ◽  
◽  
Masaaki Kobayashi ◽  
Akira Mohri
Keyword(s):  
Neural Network ◽  
Motion Planning ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Difficult Problem ◽  
Human Control ◽  
Planning And Control ◽  
Fuzzy Neural ◽  
Neural Network System ◽  
And Control

This paper discusses a parking motion planning and control of a car-like robot. Because of non-holonomic constraints of the system, motion planning and control is regarded as a difficult problem. In this paper, constraints of steering operation and obstacle avoidance with garage and walls are also considered. As one approach to this problem, extracting human control strategy can be considered, because many drivers can easily park their cars in garages. This paper proposes a motion planning and control method using a fuzzy neural network (FNN). The fuzzy neural network system for parking motion planning learns good parking motions by human operations to generate motion strategy of parking. The fuzzy neural network is then used for parking motion planning in a restricted area surrounded by walls. Computer simulation demonstrates the effectiveness of the planning method. Furthermore, the method can be considered as a feedback control law for the parking of car-like robot. Therefore, an experiment of parking motion control using the fuzzy neural network is also tested.

Grid graph planning and control method applied to equipment maintenance in the chemical industry

1967 ◽  
Vol 3 (3) ◽  
pp. 231-234 ◽  
Author(s):  
E. K. Sashina ◽  
�. I. Shklovskii ◽  
A. B. Miller ◽  
Yu. S. Chentsov
Keyword(s):  
Chemical Industry ◽  
Control Method ◽  
Equipment Maintenance ◽  
Grid Graph ◽  
Planning And Control ◽  
And Control ◽  
Graph Planning

scholarly journals Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

2021 ◽  
Author(s):  
Fahad Iqbal Khawaja ◽  
Akira Kanazawa ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge
Keyword(s):  
Motion Planning ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Collaborative Robots ◽  
Sensing System ◽  
Planning And Control ◽  
Control Scheme ◽  
Active Research ◽  
And Control ◽  
Collaborative Robot

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

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