Probabilistic fuzzy controller based robotics path decision theory

Patle, B.K.; Parhi, Dayal R.; Jagadeesh, A.; Kashyap, Sunil Kumar

doi:10.1108/wje-04-2016-024

Cited by 12 publications

(3 citation statements)

References 26 publications

(24 reference statements)

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“…Occupancy grid mapping is proposed in late 1980’s and it is still implemented because of feature such as simplicity, easy to implemented and low computation is required (Elfes, 1989a, 1989b; Singh and Nagla, 2019e). Sonar occupancy grid mapping is generated by various techniques such as Grid Map, EKF, ICP, feature based mapping using constant depth of sonar, grid updating model to reduce the effect of specular reflection, sensor fusion in grid mapping and dedicated filter (Lim and Cho, 1994; Leonard and Durrant-Whyte, 2012; Nagla et al , 2012, 2015; Patle et al , 2016; Singh and Nagla, 2019b, 2019d). However, the reliability of the generated occupancy grid mapping is low because of spatial discretization, cross talk, wave interference, imprecision in determining the exact value of the probability in the particular cell, heavy computational load, lack of metric information, etc.…”

Section: Related Workmentioning

confidence: 99%

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

Singh

Katyal

Kumar

et al. 2020

WJE

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Purpose Sonar sensor-based mobile robot mapping is an efficient and low cost technique for the application such as localization, autonomous navigation, SLAM and path planning. In multi-robots system, numbers of sonar sensors are used and the sound waves from sonar are interacting with the sound wave of other sonar causes wave interference. Because of wave interference, the generated sonar grid maps get distorted which resulted in decreasing the reliability of mobile robot’s navigation in the generated grid maps. This research study focus in removing the effect of wave interfaces in the sonar mapping to achieve robust navigation of mobile robot. Design/methodology/approach The wrong perception (occupancy grid map) of the environment due to cross talk/wave interference is eliminated by randomized the triggering time of sonar by varying the delay/sleep time of each sonar sensor. A software-based approach randomized triggering technique (RTT) is design in laboratory virtual instrument engineering workbench (LabVIEW) that randomized the triggering time of the sonar sensor to eliminate the effect of wave interference/cross talk when multiple sonar are placed in face-forward directions. Findings To check the reliability of the RTT technique, various real-world experiments are perform and it is experimentally obtained that 64.8% improvement in terms of probabilities in the generated occupancy grid map has been attained when compared with the conventional approaches. Originality/value This proposed RTT technique maybe implementing for SLAM, reliable autonomous navigation, optimal path planning, efficient robotics vision, consistent multi-robotic system, etc.

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Section: Related Workmentioning

confidence: 99%

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

Singh

Katyal

Kumar

et al. 2020

WJE

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show abstract

“…There are various applicable controller methods that can be divided in to different branches such as model-base or none modelbase [11][12][13][14][15][16][17][18][19][20][21]; (Figure 1 & 2), adaptive [22][23][24][25][26][27][28], and intelligence [29][30][31][32][33][34][35][36] methods. Tuning the parameters of a controller method which contains a high number of tunable parameters, is very crucial and can be very cumbersome.…”

Section: Introductionmentioning

confidence: 99%

Control of a SCARA Robot Using Feedback Linearization and Kalman Filter Observer

Nazari

2019

EME

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In this paper an efficient controller is presented for controlling SCARA robot in present of undesirable noises in variable states. To do so, feedback linearization is considered as the main controller and kalman filter estimator is utilized to estimate the state variables from the noisy output signals. In order to evaluate the performance of the purposed method, a simulation test is performed to apply this controller on the SCARA robot. The simulation is done for both noisy and not noisy signals. The results show that the kalman filter observer has accomplished a good state estimation and feedback lineation controller has tracked the desired signals perfectly as well.

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“…Yan et al (2014) propose using fractional-order PI controller, which increases an adjustable parameter (the order of integral), to stabilize EV battery-incorporated MG system. Due to the nonlinear control characteristic of the object, the fuzzy controllers can provide more flexible properties than classical ones (Demenko et al , 2015; Patle et al , 2016). Also, fuzzy controllers are reasonable and useful alternatives for conventional controllers.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based PID controller design for LFC in a microgrid

Esmaeili

Shayeghi

Nejad

et al. 2017

COMPEL

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Purpose This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid. Design/methodology/approach To evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out using MATLAB/SIMULINK package. Findings Simulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions. Originality/value In this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.

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Probabilistic fuzzy controller based robotics path decision theory

Cited by 12 publications

References 26 publications

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

Control of a SCARA Robot Using Feedback Linearization and Kalman Filter Observer

Reinforcement learning based PID controller design for LFC in a microgrid

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