Internet of Things 36-rotor Multicopter for Ionizing Radiation Surveying

This paper presents an Internet of things 36-rotor unmanned aerial vehicle suitable for radiological surveying of buildings and facilities. The design of the 36-rotor multicopter platform is disclosed. The aircraft is used as a testbed for a lightweight gamma/beta/neutron ionizing radiation sensor closely coupled with the autopilot of the multirotor aircraft. A prototype of the drone and sensor was developed and initial tests were conducted. Test results are presented with data from measuring different radiation sources. The proposed novel design is compared to existing work and advantages to the latter were established

A Robust Localization System for Inspection Robots in Sewer Networks †

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2

Localizing Multiple Radiation Sources Actively with a Particle Filter

The article discusses the localization of radiation sources whose number and other relevant parameters are not known in advance. The data collection is ensured by an autonomous mobile robot that performs a survey in a defined region of interest populated with static obstacles. The measurement trajectory is information-driven rather than pre-planned. The localization exploits a regularized particle filter estimating the sources' parameters continuously. The dynamic robot control switches between two modes, one attempting to minimize the Shannon entropy and the other aiming to reduce the variance of expected measurements in unexplored parts of the target area; both of the modes maintain safe clearance from the obstacles. The performance of the algorithms was tested in a simulation study based on real-world data acquired previously from three radiation sources exhibiting various activities. Our approach reduces the time necessary to explore the region and to find the sources by approximately 40 %; at present, however, the method is unable to reliably localize sources that have a relatively low intensity. In this context, additional research has been planned to increase the credibility and robustness of the procedure and to improve the robotic platform autonomy.Comment: 9 pages, 2 tables, 3 figures; submitted to IEEE RA-

Robot-assisted smart firefighting and interdisciplinary perspectives

Urbanization and changes in modern infrastructure have introduced new challenges to current firefighting practices. The current manual operations and training including fire investigation, hazardous chemicals detection, fire and rescue are insufficient to protect the firefighter's safety and life. The firefighting and rescue functions of the existing equipment and apparatus and their dexterity are limited, particularly in the harsh firefighting environments. It is well-established that data and informatics are key factors for efficient and smart firefighting operation. This paper provides a review on the robot-assisted firefighting systems with interdisciplinary perspectives to identify the needs, requirements, challenges as well as future trends to facilitate smart and efficient operations. The needs and challenges of robot-assisted firefighting systems are firstly investigated and identified. Subsequently, prevailing firefighting robotic platforms in literature as well as in practices are elaborately scrutinized and discussed, followed by investigation of localization and navigation support methods. Finally, conclusions and future trends outlook are provided

SYSTEM INTEGRATION OF C-ARM ROBOTIC PROTOTYPE USING MOTION CAPTURE GUIDANCE FOR ACCURATE REPOSITIONING

One of the important surgical tools in spinal surgery is the C-Arm X-ray System. The C-Arm is a large “C” shaped and manually maneuvered arm that provides surgeons and X-ray technicians the ability to take quick quality X-rays during surgery. Because of its five degrees of freedom, the C-Arm can be manually maneuvered around the patient to provide many angles and perspectives, ensuring surgical success. This system works fine for most surgical procedures but falls short when the C-Arm must be moved out of the way for complicated surgical procedures. The aim of this thesis is to develop an accurate repositioning method with the use of motion capture technology. This will be a novel approach to creating a repositioning integrated system. To develop a motion capture repositioning integrated system, a set of research tasks needed to be completed. A virtual prototype and a virtual platform were developed that quantified the dynamics of the C-Arm maneuvering. Next, a complete kinematic model of the C-Arm was developed. Third, a fully automatic robotic C-Arm prototype was designed and manufactured to serve as a replacement for the actual C-Arm. Finally, the robotic prototype, the virtual platform, and the kinematic model were all systematically integrated using Vicon motion capture system to perform the automatic repositioning of the C-Arm. Testing of the newly developed repositioning system was completed with successful results

Utilizing radiation for smart robotic applications using visible, thermal, and polarization images.

The domain of this research is the use of computer vision methodologies in utilizing radiation for smart robotic applications for driving assistance. Radiation can be emitted by an object, reflected or transmitted. Understanding the nature and the properties of the radiation forming an image is essential in interpreting the information in that image which can then be used by a machine e.g. a smart vehicle to make a decision and perform an action. Throughout this work, different types of images are used to help a robotic vehicle make a decision and perform a certain action. This work presents three smart robotic applications; the first one deals with polarization images, the second one deals with thermal images and the third one deals with visible images. Each type of these images is formed by light (radiation) but in a way different from other types where the information embedded in an image depends on the way it was formed and how the light was generated. For polarization imaging, a direct method utilizing shading and polarization for unambiguous shape recovery without the need for nonlinear optimization routines is proposed. The proposed method utilizes simultaneously polarization and shading to find the surface normals, thus eliminating the reconstruction ambiguity. This can be useful to help a smart vehicle gain knowledge about the terrain surface geometry. Regarding thermal imaging, an automatic method for constructing an annotated thermal imaging pedestrian dataset is proposed. This is done by transferring detections from registered visible images simultaneously captured at day-time where pedestrian detection is well developed in visible images. Histogram of Oriented Gradients (HOG) features are extracted from the constructed dataset and then fed to a discriminatively trained deformable part based classifier that can be used to detect pedestrians at night. The resulting classifier was tested for night driving assistance and succeeded in detecting pedestrians even in the situations where visible imaging pedestrian detectors failed because of low light or glare of oncoming traffic. For visible images, a new feature based on HOG is proposed to be used for pedestrian detection. The proposed feature was augmented to two state of the art pedestrian detectors; the discriminatively trained Deformable Part based models (DPM) and the Integral Channel Features (ICF) using fast feature pyramids. The proposed approach is based on computing the image mixed partial derivatives to be used to redefine the gradients of some pixels and to reweigh the vote at all pixels with respect to the original HOG. The approach was tested on the PASCAL2007, INRIA and Caltech datasets and showed to have an outstanding performance