Motion control and navigation of multiple mobile robots for obstacle avoidance and target seeking: A rule-based neuro-fuzzy technique

Pradhan, Saroj Kumar; Parhi, Dayal R.; Panda, Anup Kumar

doi:10.1243/09596518jsce631

Cited by 14 publications

(11 citation statements)

References 24 publications

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“…In the papers [67][68][69][70][71] neuro fuzzy technique for various engineering applications have been discussed. Singh et al, [72][73][74][75][76] have used neural network and fuzzy logic for robots navigation in various environmental conditions. Pandey et al 77,78 have discussed about fuzzy logic and ANFIS method for control of robots subjected to various terrains.…”

Section: Analysis Of Various Ai Techniques Used For Navigationmentioning

confidence: 99%

Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi¹

2018

IRATJ

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In this paper, a review analysis has been carried out on navigational methodologies of robots with the help of various artificial intelligence techniques such as Fuzzy Logic, Neural Network, Genetic Algorithm, Particle Swarm Optimisation (PSO) and other Artificial Intelligence techniques. During the survey analysis a systematic review has been done and shown how various artificial intelligence techniques can be successfully used for control and navigation of various types of robots subjected to various environmental conditions.

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Section: Analysis Of Various Ai Techniques Used For Navigationmentioning

confidence: 99%

Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi¹

2018

IRATJ

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“…Neuro-Fuzzy Controllers Using The Inverse Learning Technique A feedback control system working in a discrete time domain that can be described by the following equations, (6) and (8).…”

Section: Manipulatormentioning

confidence: 99%

“…Hence, at present Adaptive Neuro-Fuzzy Inference System (ANFIS) models have become one of the major areas of interest as it combines the beneficial features of both neural networks and fuzzy systems and reduces some of the individual disadvantages. [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

An adaptive Neuro-Fuzzy control approach for motion control of a robot arm

Lakshmi

Nabi

2012

2012 International Conference on Informatics, Electronics &Amp; Vision (ICIEV)

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This paper proposes an adaptive Neuro-Fuzzy control approach for controlling the link variables of a 4 degreeof-freedom Selective Compliant Assembly Robot Arm (SCARA) type robot arm / manipulator. In the real world environment, the mathematical models of many robots are often not accurate, due to the presence of continuous disturbances that effect their dynamic equations, in addition to errors in parameter knowledge. Consequently, method that rely less on precise mathematical models are often preferred. One such Adaptive Machine Learning Technique is proposed to be applied here, for motion control of the robot arm. The controller uses an inverse learning Adaptive Neuro-Fuzzy Inference System (ANFIS) model only to train itself from certain given robot trajectories. Ideally, these trajectories should be obtained by directly measuring the robot arm responses for given inputs to capture the actual dynamics in the presence of all uncertainties. However, for algorithm validation, trajectories generated through simulations based on mathematical models assumed to be reasonably accurate, can also be used for the training purpose. This approach is used for design and implementation of an ANFIS controller which is shown to act work satisfactorily. Further possible developments of this method are also outlined.

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“…As a result, the design of a robust and effective controller for nonlinear indeterminate systems remains a current issue of interest in the literature. 18,19 For this reason, different types of control technique have been suggested for various WMR systems in the literature such as H infinity, 20 neural network (NN) control, 21,22 adaptive control, 23,24 sliding mode control (SMC), 25 fuzzy control [26][27][28][29] and fractional-order control. 30 Among the above-mentioned control techniques, SMC can be considered a reliable control technique for external disturbances, uncertainties and un-modelled dynamics to the non-holonomic system's control problem.…”

Section: Introductionmentioning

confidence: 99%

Implementation a fractional-order adaptive model-based PID-type sliding mode speed control for wheeled mobile robot

Ayten

Çiplak

Dumlu

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents the speed and direction angle control of a wheeled mobile robot based on a fractional-order adaptive model-based PID-type sliding mode control technique. Taking into account the individual benefits of the fractional calculus and the adaptive model-based PID-type sliding mode control method, the fractional order and the adaptive model-based PID-type sliding mode control technique are combined and proposed as an effective controller for the first time in the literature for real-time control of the wheeled mobile robot under the external payload. In this proposed method, several critical issues are considered; first, a kinematic and dynamic model of the wheeled mobile robot is analysed considering the system’s uncertainties. Second, fractional-order calculus and the model-based PID-type sliding mode control is composed to realize the chattering-free control, accurate trajectory tracking response, finite time convergence and robustness for the wheeled mobile robot. Finally, an adaptive process is also employed to meet and overcome the unknown dynamics and uncertain parameters of the system, regardless of the previous information of the uncertainties. The experimental outcomes demonstrate that the proposed controller (fractional-order adaptive model-based PID-type sliding mode controller) delivers an accurate trajectory tracking performance, faster finite-time convergence as well as having a smaller speed error under the external payload when the adaptive model-based PID-type sliding mode control is compared.

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Motion control and navigation of multiple mobile robots for obstacle avoidance and target seeking: A rule-based neuro-fuzzy technique

Cited by 14 publications

References 24 publications

Advancement in navigational path planning of robots using various artificial and computing techniques

Advancement in navigational path planning of robots using various artificial and computing techniques

An adaptive Neuro-Fuzzy control approach for motion control of a robot arm

Implementation a fractional-order adaptive model-based PID-type sliding mode speed control for wheeled mobile robot

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