Igi Ardiyanto

Pandega Abyan Zumarsyah, Igi Ardiyanto, Hanung Adi Nugroho

This study develops meta-learners for few-shot weakly-supervised segmentation (FWS) to address the challenge of optic disc (OD) and optic cup (OC) segmentation for glaucoma diagnosis with limited labeled fundus images. We significantly improve existing meta-learners by introducing Omni meta-training which balances data usage and diversifies the number of shots. We also develop their efficient versions that reduce computational costs. In addition, we develop sparsification techniques that generate more customizable and representative scribbles and other sparse labels. After evaluating multiple datasets, we find that Omni and efficient versions outperform the original versions, with the best meta-learner being Efficient Omni ProtoSeg (EO-ProtoSeg). It achieves intersection over union (IoU) scores of 88.15% for OD and 71.17% for OC on the REFUGE dataset using just one sparsely labeled image, outperforming few-shot and semi-supervised methods which require more labeled images. Its best performance reaches 86.80% for OD and 71.78%for OC on DRISHTIGS, 88.21% for OD and 73.70% for OC on REFUGE, 80.39% for OD and 52.65% for OC on REFUGE. EO-ProtoSeg is comparable to unsupervised domain adaptation methods yet much lighter with less than two million parameters and does not require any retraining.

Resha Dwika Hefni Al-Fahsi, Kevin Aldian Winanta, Fauzan Pradana et al.

NightOwl is a robotic platform designed exclusively for a wheeled service robot. The robot navigates autonomously in omnidirectional fashion movement and equipped with LIDAR to sense the surrounding area. The platform itself was built using the Robot Operating System (ROS) and written in two different programming languages (C++ and Python). NightOwl is composed of several modular programs, namely hardware controller, light detection and ranging (LIDAR), simultaneous localization and mapping (SLAM), world model, path planning, robot control, communication, and behaviour. The programs run in parallel and communicate reciprocally to share various information. This paper explains the role of modular programs in the term of input, process, and output. In addition, NightOwl provides simulation visualized in both Gazebo and RViz. The robot in its environment is visualized by Gazebo. Sensor data from LIDAR and results from SLAM will be visualized by RViz.

Fahri Ali Rahman, Igi Ardiyanto, Adha Imam Cahyadi

RoboCup Middle Size League (RoboCup MSL) provides a standardized testbed for research on mobile robot navigation, multi-robot cooperation, communication and integration via robot soccer competition in which the environment is highly dynamic and adversarial. One of important research topic in such area is kinodynamic motion planning that plan the trajectory of the robot while avoiding obstacles and obeying its dynamics. Kinodynamic motion planning for omnidirectional robot based on kinodynamic-RRT* method is presented in this work. Trajectory tracking control to execute the planned trajectory is also considered in this work. Robot motion planning in translational and rotational direction are decoupled. Then we implemented kinodynamic-RRT* with double integrator model to plan the translational trajectory. The rotational trajectory is generated using minimum-time trajectory generator satisfying velocity and acceleration constraints. The planned trajectory is then tracked using PI-Control. To address changing environment, we developed concurrent sofware module for motion planning and trajectory tracking. The resulting system were applied and tested using RoboCup simulation system based on Robot Operating System (ROS). The simulation results that the motion planning system are able to generate collision-free trajectory and the trajectory tracking system are able to follow the generated trajectory. It is also shown that in highly dynamic environment the online scheme are able to re-plan the trajectory.