Furkan Cakmak

Implementation of frontier-based exploration algorithm for an autonomous robot

Exploration is defined as the selection of target points that yield the biggest contribution to a specific gain function at an initially unknown environment. Exploration for autonomous mobile robots is closely related to mapping, navigation, localization and obstacle avoidance. In this study an autonomous frontier-based exploration strategy is implemented. Frontiers are defined as the border points that are calculated throughout the mapping and navigation stage between known and unknown areas. Frontier-based exploration implementation is compatible with the Robot Operating System (ROS). Also in this study, real robot platform is utilized for testing and the effect of different frontier target assignment approaches are comparatively analyzed by means of total path length and thereby total exploration time.

Design and implementation of an outdoor line follower robot for RoboCup search and rescue league

RoboCup is the most prestigious robotics contest in the world, with increasing popularity among robot communities and new contests. In this study, design and implementation of a line follower robot for outdoor categories which have been recently added to RoboCup Search and Rescue League is emphasized. In this category, a 100m long colored rope is placed in a field whose floor is made of sand, concrete and laminate and the competing robots are ranked according to the maximum trajectory distance that they cover in 20 minutes. The track is made of right and acute angle parts for the robot which is expected to follow the line including also 15° ramps. The robot which is implemented on ROS (The Robot Operating System) follows the line with the camera sensor above it.

Hazmat charts detection on search and rescue league of RoboCup competitions

RoboCup competitions are among the most prestigious robotics organizations in the world. Contestants compete in different categories with the robots they develop step by step to achieve the objectives of the organization for 2050. One of these categories is designed for search and rescue robots. Robots get points from the following criteria: identifying the victims autonomously in disaster situations, detecting hazmat chars, reading QR code and recognizing the various objects which is identified during the competition and marking their the locations on a map. In this study, some performance tests of various feature extraction methods have been performed for the detection of hazmat signs in competition environments of RoboCup search and rescue league. The tests were conducted in a laboratory which designed as RoboCup search and rescue league competition environment. After some comparison tests, the most appropriate method for real-time operation has been decided considering acceptable accuracy rates.

Autonomous mobile robot exploration in negative obstacle environment

Restricted 3D simultaneous localization and mapping (SLAM) can be achived in an environment that has negative and positive obstacles, using a 2D SLAM and a 3D mapping together. Exploration can utilize 2D navigation, which uses a 2D map constructed with respect to 3D map obstacles both negative and positive, and free areas. Robocup, Rescue League, Exploration 4 competition is designed to measure real platform autonomous mobile robot exploration capabilities in an negative obstacle scene. In this paper aforementioned exploration approach is implemented in a simulation environment created in Gazebo similar to Exploration 4 arena, using Robot Operating System (ROS).

A fast 3D exploration algorithm for autonomous aerial robots

In the scope of the study, it was aimed to discover a closed multi-storey environment with autonomous air robots and produce a three dimensional map. In order to reduce the complexity of the three-dimensional exploration algorithm, we have developed Target Elimination Method that can calculate the result by narrowing the problem space. The Target Elimination Method ensures that the exploration algorithm is faster to explore the environment as it reduces the calculation time.

An architecture for multi-robot localization and mapping in the Gazebo/Robot Operating System simulation environment:

Robots are an important part of urban search and rescue tasks. World wide attention has been given to developing capable physical platforms that would be beneficial for rescue teams. It is evident that use of multi-robots increases the effectiveness of these systems. The Robot Operating System (ROS) is becoming a standard platform for the robotics research community for both physical robots and simulation environments. Gazebo, with connectivity to the ROS, is a three-dimensional simulation environment that is also becoming a standard. Several simultaneous localization and mapping algorithms are implemented in the ROS; however, there is no multi-robot mapping implementation. In this work, two multi-robot mapping algorithm implementations are presented, namely multi-robot gMapping and multi-robot Hector Mapping. The multi-robot implementations are tested in the Gazebo simulation environment. Also, in order to achieve a more realistic simulation, every incremental robot movement is modeled with rotational and translational noise.

Android application for simultaneously control of multiple land robots which have different drive strategy

The remote controls which are used to control the robots are very expensive. In addition, one remote control is required for each robot. The goal of this work is to control multi robots at the same time by publish a single ROS message using one Android device. The user can drive robots with help of a variety of sensors and camera data which are displayed on the Android device screen, even if the user is visually disconnected from all robots. The biggest difference of this application from similar applications is that two different robots can be driven at the same time. With this feature, many multi robot tasks can be performed. Also robots have different driving strategies. This feature allows drive robots on different surfaces. Another powerful aspect of the work is the usage of ROS. Thanks to ROS, it is ensured that processes such as data communication protocols, process scheduling, etc. are implemented easily and efficiently. Furthermore, this work developed for Android. This feature facilitates the spread of the application.

Comparison of 3-dimensional SLAM systems: RTAB-Map vs. Kintinuous

The fact that 6-DoF (Degree of Freedom) mobile robots become widespread, makes 2 dimensional Simultaneous Localization and Mapping (SLAM) systems insufficient in order to provide autonomous abilities like exploration and navigation. Thus, 3 dimensional SLAM has become a research subject, and various systems have been developed for the last decade. This paper compares performances of two state-of-the-art 3D SLAM systems: RTAB-Map (Real-Time Appearance-Based Mapping) and Kintinuous. Although both system uses RGB-D sensor for input data and similar aspects in gathering information from the input, their different approaches in implementation details affects performance results. Both systems were tested on several datasets. Experimental results show that RTAB-Map has more success on handling 3-Dimensional SLAM problems so that trajectory of RGB-D sensor can be estimated with remarkably less error than Kintinuous. The reason can stem from the fact that Kintinuous is more dependent to depth data which is less reliable than RGB data while RTAB-Map uses RGB data more effectively.

Thermal based exploration for search and rescue robots

Detection of thermal targets for search and rescue robots is very important to be able to save more lives. Because the living human body is at a certain temperature, each thermal target point implies possible victim. Robots produced for search and rescue are expected to be able to perceive and steer toward the thermal targets. The focus of this work, which is also a criterion of RoboCup competitions, is the development of an exploration method for the determination of thermal targets. An algorithm has been developed which relies on giving travel priority to the thermal information emitting targets in the environment. So that the victims can be detected more effectively. Additional methodsg, such as human detection from image processing, detecting carbon dioxide gas, motion detection, etc., can be used to identify the victim, in consideration of the fact that every thermal target in the environment may not be human bein This study only involves detecting thermal targets and directing the mobile robots to them. Successful results are ensured by making the method more stable thanks to tests in both the real environment and the simulation environment. Gazebo is used as the simulation environment, and a differential drive mobile robot with a thermal camera is used for real environment experiments. Since there is no thermal camera in Gazebo simulation environment, a system was designed to represent thermal targets. This system is based on obtaining representative thermal images by applying various filters to normal camera images.

Deformable part model and deep learning comparison on victim detection

Object detection problem is a considerable research field that is being developed through continuous research. Simulated victims (dolls) detection performances of 2 different methods are given in the scope of this work. While deformable part model method is performing high accuracy and speed to detect object, with growing and remarkable popularity, deep learning method is noteworthy with higher performance results. This work mentions about the advantages and disadvantages of both methods and gives experimental results on a simulated environment.