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.

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.

Intelligent Control Method of Ground to Air Missile Based on FCMAC

2011 ◽  
Vol 128-129 ◽  
pp. 168-171
Author(s):  
Gang Li ◽  
Hao He ◽  
Gang Fang ◽  
Jian Feng Wu
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Basic Structure ◽  
Control Methods ◽  
Missile Guidance ◽  
Guidance And Control ◽  
Fuzzy Neural ◽  
And Control

Intelligent control methods of missile guidance and control system (GACS) are studied in this paper. Secondly, the component and principle of GACS is introduced. Based on the fuzzy neural network, this paper constructs a basic structure of the intelligent control method of missile. Meanwhile, a new intelligent control method of rolling channel of missile based on Fuzzy Cerebella Model Articulation Controller (FCMAC) is designed. Under complicated environmental conditions, the missile can be accurately controlled with this method. Finally, the application value is illustrated. It’s very meaningful to improve the combat capability.

Study on the Lower Limbs Rehabilitation Robotic Control System by sEMG

2012 ◽  
Vol 462 ◽  
pp. 826-832
Author(s):  
Xiao Jun Zhang ◽  
Geng Qian Liu ◽  
Jian Hua Zhang ◽  
Yong Feng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Lower Limbs ◽  
Rehabilitation Robot ◽  
Robotic Control ◽  
Rehabilitation Robots ◽  
Fuzzy Neural ◽  
Neural Network Classifiers ◽  
And Control

With help training of the lower limbs rehabilitation robot, the hemiplegia patients can be helped effectively recover. Applicable control method plays an important part in performance of lower limbs rehabilitation robot. According to the preferred method, sEMG was collected from no necrosis and healthy muscle, then, the effective action signals which are extracted from the sEMG transit to Fuzzy-Neural network classifiers to identify the movements intention of paralyzed patients, and then the lower limbs rehabilitation robots can assist paralyzed patients to achieve their intent. The simulation results indicate that the Fuzzy-Neural network classifiers can identify the movements intention well, and control method of sEMG can satisfy the demand of lower limbs rehabilitation robot.

Intelligent Prediction on the Quality of Cementing

2011 ◽  
Vol 84-85 ◽  
pp. 373-377
Author(s):  
Wei Zhang Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Influential Factor ◽  
Software Systems ◽  
Neural Network Modeling ◽  
Quality Prediction ◽  
Modeling Tools ◽  
Fuzzy Neural ◽  
Neural Network System

The present solutions of well cementing are mostly designed by designers’ experience and calculation which can not predict the engineering quality after application of the designs. Meanwhile some questions in the designs can not be solved before construction. On the basis of detailed evaluation of every influential factor according to construction and environmental conditions, this article provides cementing fuzzy neural network model by means of 2nsoftEditor neural network modeling tools, and the stable software systems with the combination of artificial neural network and fuzzy logic rules are expected to improve the credibility of cementing quality prediction. Construction practice shows that cementing quality prediction with application of fuzzy neural network system before cementing can greatly reduce the cementing costs and improve the cementing success ratio.

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