Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Chhotray, Animesh; Parhi, Dayal R.

doi:10.1017/s0263574718001558

Cited by 16 publications

(12 citation statements)

References 39 publications

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“…Hence, the adaptive autoadjustable controller is dependent on the pole's placement control technique and the hierarchical identification strategy. [29] developed and implemented a neural network integrated modfied DAYANI technique for path and navigational control of a TWSBR in a cluttered environment. The authors instigated a five-layered back-propagation neural network to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modfied DAYANI method.…”

Section: ) Mathematical Derivationmentioning

confidence: 99%

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

et al. 2019

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This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller.

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Section: ) Mathematical Derivationmentioning

confidence: 99%

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

et al. 2019

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“…In neural network [107][108][109][110][111] inputs are given to the neurons in input layers and output is obtained from neuron in output layer. Neural networks [112][113][114][115][116] have been used efficiently for robot navigation control of robot. Using Bat algorithm [117] researchers have tried to solve robot path planning problem.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of Automated Transport and Path Planning for Robots in Cluttered Environments using Novel Hybrid Average Genetic-Neural Control Technique

2021

CERJ

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In the current investigation on automated transport and navigational path planning of robots, a new Hybrid Average Genetic-Neural (HAGN) technique has been developed. The HAGN technique uses genetic algorithm and multi layered neural technique as important parts for its development. The robots are equipped with several sensors to map the surrounding environments and to recognize the obstacles and targets around. During the navigation robots take into account front, left and right obstacle distances obtained from sensors to negotiate with obstacles and reach targets with the help of HAGN technique. To prove authenticity of the proposed method several simulation and experimental exercises have been carried out. Comparisons between simulation and experimental results are presented in pictorial and tabular forms. The deviation between simulation and experimental results are found to be within 2.8%. Other engineering applications can also be addressed using HAGN AI technique.

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“…Papers [73][74][75][76][77] have discussed about neural networks for navigational control of mobile robots in highly cluttered environments. Neural networks [78][79][80][81][82] can be efficiently used for solving various engineering problems along with problems related to robots' control. Potential energy attraction has been used by scientists and engineers to model artificial intelligence potential field method for solving various engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation

R¹,

Chidananda

2020

CTCSA

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In this paper application of average fuzzy inference technique has been analysed for navigation control of robotic agent. Also, the reviews of other AI techniques for control of robots are carried out. The robotic agent uses sensors to map the surroundings and take the decision with the help of Fuzzy AI technique to avoid obstacles. In this paper a novel averaging method has been deployed to optimize the results obtained from various fuzzy membership functions. Using the Average Fuzzy Inference technique robot navigates from start position to goal position avoiding obstacles while reaching the target. The simulation results agree with experimental results. The methodology can be used for various applications by the scientific communities to address various engineering problems.

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Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Cited by 16 publications

References 39 publications

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

Analysis and Design of Automated Transport and Path Planning for Robots in Cluttered Environments using Novel Hybrid Average Genetic-Neural Control Technique

Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation

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