Final Year Project Lego Robot Guided by Wi-Fi (QYA2) Presented by: Li Chun Kit (Ash) So Hung Wai (Rex) 1

Overview 1.Introduction 2.Video Demo 3.System Functions - Localization - Self-Guiding - Obstacles Detection - Auto Data Collection 4.Conclusion 5.Q&A 2

Introduction 3 Goals Wi-Fi Indoor localization Self-Guiding Lego robot as the media to move and collect data automatically Figure 1. The client-server architecture.

Video Demo 4

Localization 5 Offline PhraseOnline Phrase Data collected for establishing the training database Observed data is compared with the training database Estimated Location Machine Learning Algorithm Figure 2. Records in training database. Figure 3. Observed data received during online phrase.

Localization : K-Nearest Neighbor (KNN) 6 a a a c c b b a K=10 K=4 Classification by computing similarity between unknown object and known objects. Euclidean Distance b a c c Records in grid a, band c Figure 4. For k=4, the user trace is classified to be grid c record; while it is classified to be grid a when k=10. b Unknown Objects Observed Data O( o 1, o 2, o 3, ……o k ) Known Objects Training Data T(t 1, t 2, t 3, ……t m ) c c c c c c Estimated Location The grid cell having the highest occurrence in the k coverage

7 K-Nearest Neighbor (KNN) Euclidean Distance is calculated for each records in training database In Practice Figure 5. Computing Euclidean Distance

Localization: Bayesian Probability 8 Bayesian approach is based on signal strength distribution of access points on each grid cell. mitigates the random errors adopts probability measurements Figure 6. A histogram showing the RSSI distribution of an access point at a grid cell computes across 106 grid cells

In Practice Mac Address RSSI probability …… 00:17:DF:AA:9B:A …… 00:23:EB:0B:4F:F …… 00:23:EB:0B:51: …… 9 Grid Cell 82 RSSI Profiles Mac Address RSSI probability …… 00:23:EB:0B:4F:F …… 00:23:EB:3A:12: …… 00:17:DF:AA:9E:C …… Grid Cell 83 RSSI Profiles Bayesian Probability

Algorithm Accuracy 10

Appendix 11 KNN Demonstration

Appendix 12 Bayesian Formula

Appendix 13

Intuitively 14 Figure 2. Records in training database. Bayesian Probability