Driver package for using Leap Motion controller on the robotics development platform ROS

Järjest laienev robotite kasutusvaldkond nõuab inimeste ja robotite vahel üha tihedamat koostööd, mida kirjeldatakse koondterminiga inimese ja roboti suhtlus (ingl „Human-Robot Interaction“). Seetõttu on vajalik arendada töökindlaid meetodeid inimeste tuvastamiseks ning luua intuitiivseid kasutajaliideseid robotite kergeks juhtimiseks. Käesoleva bakalaureusetöö eesmärgiks on luua draiver, mis võimaldaks kasutada käte jälgimise seadet Leap Motion kontroller (LM-kontroller) robootika arendusplatvormil ROS (Robot Operating System). Kuigi arendusplatvormile ROS eksisteerib juba LM-kontrollerile draiver, siis selle funktsionaalsus ja dokumentatsioon on puudulikud ning alates 2014. aastast pole seda arendatud, sest koodihoidlal puudub aktiivne haldaja. Bakalaureusetöö raames valminud LM-kontrolleri draiveripaketi eesmärgiks on laiendada olemasoleva paketi võimalusi, tõsta seadme kasutajamugavust nii kasutajatele kui ka arendajatele ning koostada terviklik dokumentatsioon (paigaldusjuhendid ja lähtekoodi kommentaarid)

Distributed camera subsystem for obstacle detection

This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.Web of Science2212art. no. 458