PEMETAAN POSISI DAN SISTEM NAVIGASI MOBILE ROBOT DALAM RUANG MENGGUNAKAN SENSOR PERPINDAHAN JENIS OPTICAL LASER

Pesatnya perkembangan teknologi robot pada saat ini memungkinkan seseorang untuk melakukan perkembangan teknologi ini. Saat ini perkembangan navigasi untuk mobile robot sangat berkembang pesat, antara lain adalah teknologi line tracer dan odometry dengan menggunakan rotary encoder. Semakin pesatnya teknologi membuat negara-negara berkembang khususnya Indonesia kalah bersaing dengan negara-negara maju lainnya dalam teknologi robot dan ajang-ajang perlombaan tingkat nasional. Pada proyek akhir ini merancang sebuah sistem untuk membentuk sebuah sistem koordinat secara cepat dan pemetaan posisi robot dalam ruang. Titik pusat koordinat berada pada titik awal sebelum robot bergerak. Setiap kali robot bergerak perubahan nilai perpindahan terhadap sumbu x dan sumbu y akan diakumulasikan dengan data sebelumnya. Pengukuran akurasi heading pada robot dilakukan dengan menempatkan dua titik sensor yang berbeda-beda pada badan robot yang dimaksudkan untuk mendapatkan posisi ideal untuk menekan kesalahan pembacaan heading. Pemilihan yang tepat dalam penggunaan sensor akan berpengaruh terhadap hasil yang dicapai. Sensor jenis optical laser merupakan salah satu pilihan untuk mendapatkan hasil yang presisi dari perhitungan yang digunakan. Pada sensor laser dapat digunakan sebagai pengganti dari rotary encoder di mana memiliki sensitivitas yang baik(persen error=7,4%) terhadap alas vinyl, karpet hijau, karpet abu-abu, dan multiplek hitam. Serta pada pencapaian target tidak terpaut jauh(selisih persen error=1.4%) dengan penggunaan rotary encoder pada umumnya

Implementation of Integration VaaMSN and SEMAR for Wide Coverage Air Quality Monitoring

The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G WiFi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm

Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

As a flexible and cost-efficient internet access network, the IEEE 802.11 wireless local-area network (WLAN) has been broadly deployed around the world. Previously, to improve the IEEE 802.11n WLAN performance, we proposed the four-step minimax access-point (AP) setup optimisation approach: 1) link throughputs between the AP and hosts in the network field are measured manually; 2) the throughput estimation model is tuned using the measurement results; 3) the bottleneck host suffering the least throughput is estimated using this model; 4) the AP setup is optimised to maximise the throughput of the bottleneck host. Unfortunately, this approach has drawbacks: 1) a lot of manual throughput measurements are necessary to tune the model; 2) the shift of the AP location is not considered; 3) IEEE 802.11ac devices at 5 GHz are not evaluated, although they can offer faster transmissions. In this paper, we present the three enhancements: 1) the number of measurement points is reduced while keeping the model accuracy; 2) the coordinate of the AP setup is newly adopted as the optimisation parameter; 3) the AP device with IEEE 802.11ac at 5 GHz is considered with slight modifications. The effectiveness is confirmed by extensive experiments in three network fields

Implementation of Integration VaaMSN and SEMAR for Wide Coverage Air Quality Monitoring

The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G WiFi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm