Rancang bangun Mobile Robot Real Time Vision menggunakan Webcam dan Raspberry Pi

Jauhari, Hasnan Habib (2019) Rancang bangun Mobile Robot Real Time Vision menggunakan Webcam dan Raspberry Pi. Sarjana thesis, UIN Sunan Gunung Djati Bandung.

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Abstract

INDONESIA : Robot merupakan suatu alat yang dapat membantu pekerjaan manusia diberbagai bidang. Penelitian ini bertujuan untuk Merancang dan membangun mobile robot real time vision agar bisa mengenali wajah dan mengikuti warna serta mengetahui kinerja dari mobile robot real time vision untuk mendeteksi wajah. Robot ini pada bagian kepala terdiri dari Webcam, bagian perut terdiri dari LCD 7 inch, dan bagian dalam terdiri dari Raspberry Pi dan Motor Driver dengan IC LM293D. Software yang digunakan yaitu Python versi 2.7.9 dan open source library OpenCV versi 2.4.9.1. Dari penelitian yang dilakukan, didapatkan hasil keberhasilan dari percobaan face recognition yang dilakukan sebanyak satu orang yaitu 100%, dua orang yaitu 50%, dan tiga orang yaitu 9%. Hasil keberhasilan saat robot melakukan color tracking pada software interface yaitu warna hijau 100%, warna merah 99.34%, dan warna biru 94%. Hasil langsung dilapangan untuk color tracking warna hijau 33%, warna merah 35%, dan warna biru 19%. Hasil pengujian menunjukan bahwa hasil rata-rata time response face recognition pada intensitas cahaya redup medapatkan nilai rata-rata 12.52 detik, cahaya sedang 11.06 detik, dan cahaya terang 10.35 detik dan hasil rata-rata time response color detection pada cahaya redup 11.41 detik, cahaya sedang 11.34 detik, dan cahaya terang 11.10 detik. ENGLISH : Robot is a tool that can help human work in various fields. This study aims to design and build a real time vision mobile robot so that it can recognize faces and follow colors and determine the performance of real time vision mobile robots to detect faces. This robot on the head consists of a webcam, the stomach consists of a 7 inch LCD, and the inside consists of a Raspberry Pi and Motor Driver with IC LM293D. The software used is Python version 2.7.9 and the open source library OpenCV version 2.4.9.1. From the research conducted, the results of the success of the facial recognition experiment were carried out by one person, namely 100%, two people, namely 50%, and three people, namely 9%. The results of success when the robot performs color tracking on the software interface are 100% green, 99.34% red, and 94% blue. Direct results in the field for color tracking are 33% green, 35% red, and 19% blue. The test results show that the average face recognition time response at low light intensity gets an average value of 12.52 seconds, medium light 11.06 seconds, and bright light 10.35 seconds and the average color detection response time in low light is 11.41 seconds, medium light 11.34 seconds, and bright light 11.10 seconds.

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