ROS Based Adjustable Resolution Compact 3D Scanner

This research study aims to design and develop a compact multi-sensor 3D scanning system for a mobile robot which can detect object types. Sensor suite is equipped with a 3D Lidar with 360-degree field of view and a stationary camera mounted on top of a 2DOF turret. The sensor data is fused to generate a 3D point cloud. Mechanism is required to sweep the 3D space in pitch and roll axes. While servo controller can adjust generated point cloud resolution — and scan duration —, camera images can also be registered to generate a panoramic image of the environment followed by radial distortion corrections. System can generate variable density scans for specific application such mapping or self-driving. This study is the perception portion of our full-scale self-driving golf cart research project of Autonomous Mobile Robotics Research Group.

The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/ .

An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments

The exploration problem is a fundamental subject in autonomous mobile robotics that deals with achieving the complete coverage of a previously unknown environment. There are several scenarios where completing exploration of a zone is a main part of the mission. Due to the efficiency and robustness brought by multi-robot systems, exploration is usually done cooperatively. Wireless communication plays an important role in collaborative multi-robot strategies. Unfortunately, the assumption of stable communication and end-to-end connectivity may be easily compromised in real scenarios. In this paper, a novel auto-adaptive multi-objective strategy is followed to support the selection of tasks regarding both exploration performance and connectivity level. Compared with others, the proposed approach shows effectiveness and flexibility to tackle the multi-robot exploration problem, being capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.

Fog Effects on Time-of-Flight Imaging Investigated by Ray-Tracing Simulations

Time-of-Flight (ToF) sensors are a key technology for autonomous vehicles and autonomous mobile robotics. Quantifying the extent of perturbation induced by atmospheric phenomena on ToF imaging is critical to identify effective correction strategies. Here we present an approach that uses optical ray-tracing to simulate the ToF image, while the distance information is recovered by analyzing the optical path of each ray. Such an approach allows, for example, understanding the effects of different ray paths on the ToF image, or testing various retrieval/correction algorithms upon running a single ray-tracing simulation. By modelling several scattering scenarios, we show that ranging errors arise mostly from light backscattered to the sensor prior reaching the scene. Scattering events close to the sensors (<1 m) have the largest influence, therefore strategies capable of filtering out signals from distances shorter than the range of interest can significantly improve the accuracy of ToF sensors.

Tsukuba Challenge: Open Experiments for Autonomous Navigation of Mobile Robots in the City – Activities and Results of the First and Second Stages –

The Tsukuba Challenge is an open experiment for autonomous mobile robotics researchers who want to build small mobile robots capable of autonomously moving through real and populated pedestrian environments. The Tsukuba Challenge started in 2007 and has been run every year since then. Each year, the self-contained mobile robots of participated team are tasked with autonomously navigating more than 1 km of a given pedestrian pathway through the city. As of 2017, the final year of the second stage, a total of over 500 teams have taken a part in this challenge, by trying to develop their own robot hardware and software to complete the given task. In this paper, the basic concept and the history of Tsukuba Challenge are first explained, and then what has and has not achieved is discussed.

State of the art in cooperative robotics applied to rescue of victims

The present article, in the context of a documentary research carried out and interpreted to be taken as a baseline in research for the ROMA Autonomous Mobile Robotics Group of the Francisco José de Caldas District University, describes the state of art of the applied RC to the rescue of victims. The revision is established chronologically in the last fifteen years; in Latin America; and focused mainly in Colombia. They are used as sources: The Google Schoolar database, and articles from the electronic engineering journals indexed for the year 2017 in COLCIENCIAS. As a product thrown, a particular model of communication technology used in CR is presented in the Colombian context.

Open Experiment of Autonomous Navigation of Mobile Robots in the City: Tsukuba Challenge 2014 and the Results

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/01.jpg"" width=""300"" /> Autonomous mobile robot in RWRC 2014</div> The Tsukuba Challenge, an open experiment for autonomous mobile robotics researchers, lets mobile robots travel in a real – and populated – city environment. Following the challenge in 2013, the mobile robots must navigate autonomously to their destination while, as the task of Tsukuba Challenge 2014, looking for and finding specific persons sitting in the environment. Total 48 teams (54 robots) seeking success in this complex challenge. </span>