2
Archived Theses
0
DOIs Assigned
0%
DOI Rate
Discipline
Çevresel algılama ve harıtalama ıle ınsan takıbı
This thesis is concerned with human following by a mobile robot that also needs to navigate in a socially compliant manner. The importance of this problem is due to the increasing usage of service robots in human-populated areas. This is a challenging task because the robot needs to consider proxemics while navigating. At the same time, it needs to keep the human in sight. In this thesis, this problem is studied from four aspects. First, an extensive evaluation of the previously proposed social navigation method, Social APF-RL, has been conducted to verify that the robot's navigation is socially compliant. Following, in order to better keep the human target within its field of view, a social robot head design has been realized based on a pan-tilt mechanism. Thirdly, human following in a socially compliant manner has been considered using the developed robot head, including body-head coordination while navigating. For this, a deep learning network based on reinforcement learning has been developed. Finally, recovery in case of human target loss is considered through human tracking and environmental sensing of doors using a specially trained YOLOv8 network. All the proposed methods are tested extensively in the Gazebo simulation environment.
Giyilebilir sensörler ile kas yükü tahmini
Assessment of workload intensity and muscle load measurement is important to understand the quality of the intended motion. There have been numerous attempts to quantify workload levels for different motion for different parts of the body using wearable sensors. This thesis investigates human locomotion, particularly walking and running, which are fundamental activities of daily life. Electromyography (EMG) is a technique to quantify the level of muscle activity. In this study, the aim is to lay foundation for feature engineering and to build a machine learning model to predict EMG sensor output with minimal number of sensors during walking. Two pressure insole sensors and a single EMG - IMU combined sensor have been used during the experiments. The process starts with building a machine learning model to predict EMG sensor output using only raw input signals, which provided a poor accuracy. In order to improve the model, lagged features, derivatives and window-based statistics like moving averages and standard deviations are added to feature set and accuracy is improved. Then the focus is put on hyperparameter tuning and regularization is introduced to decrease overfitting. Although these steps led to incremental improvements, the test performance eventually plateaued with overfitting. In order to overcome this issue, data is shuffled before splitting it into training and test sets. After shuffling during training and test data split, the model delivered its best results—both training and test accuracy improved, and overfitting was significantly reduced.