Design of Biped Locomotive System for Fire Fighting &amp; Human Detection Robot

Baki, Harish; Puviarasi, R.

doi:10.5958/0976-5506.2017.00495.8

Cited by 1 publication

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“…Research and tests of the decision layer show that the multi-sensor fusion detection system designed by this study is effective and highly accurate. Harish et al (2017) analyzed the fire sensor of the firefighting robot and the pump motor; the fire sensor and the Arduino board were used in the programming of the firefighting agency [21]. The design of the fire sensor above is similar to that in the literature and has achieved consistent results.…”

Section: Plos Onementioning

confidence: 95%

Multi-sensor information fusion detection system for fire robot through back propagation neural network

Zhang

2020

PLoS ONE

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Objective To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated. Method According to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system. Results Through the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study. Conclusion The research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest

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Section: Plos Onementioning

confidence: 95%

Multi-sensor information fusion detection system for fire robot through back propagation neural network

Zhang

2020

PLoS ONE

View full text Add to dashboard Cite

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Design of Biped Locomotive System for Fire Fighting & Human Detection Robot

Cited by 1 publication

References 0 publications

Multi-sensor information fusion detection system for fire robot through back propagation neural network

Multi-sensor information fusion detection system for fire robot through back propagation neural network

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