Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Robotic navigation and inspection of bridge bearings

This thesis focuses on the development of a robotic platform for bridge bearing inspection. The existing literature on this topic highlights an aspiration for increased automation of bridge inspection, due to an increasing amount of ageing infrastructure and costly inspection. Furthermore, bridge bearings are highlighted as being one of the most costly components of the bridge to maintain. However, although autonomous robotic inspection is often stated as an aspiration, the existing literature for robotic bridge inspection often neglects to include the requirement of autonomous navigation. To achieve autonomous inspection, some methods for mapping and localising in the bridge structure are required. This thesis compares existing methods for simultaneous localisation and mapping (SLAM) with localisation-only methods. In addition, a method for using pre-existing data to create maps for localisation is proposed. A robotic platform was developed and these methods for localisation and mapping were then compared in a laboratory environment and then in a real bridge environment. The errors in the bridge environment are greater than in the laboratory environment, but remained within a defined error bound. A combined approach is suggested as an appropriate method for combining the lower errors of a SLAM approach with the advantages of a localisation approach for defining existing goals. Longer-term testing in a real bridge environment is still required. The use of existing inspection data is then extended to the creation of a simulation environment, with the goal of creating a methodology for testing different configurations of bridges or robots in a more realistic environment than laboratory testing, or other existing simulation environments. Finally, the inspection of the structure surrounding the bridge bearing is considered, with a particular focus on the detection and segmentation of cracks in concrete. A deep learning approach is used to segment cracks from an existing dataset and compared to an existing machine learning approach, with the deep-learning approach achieving a higher performance using a pixel-based evaluation. Other evaluation methods were also compared that take the structure of the crack, and other related datasets, into account. The generalisation of the approach for crack segmentation is evaluated by comparing the results of the trained on different datasets. Finally, recommendations for improving the datasets to allow better comparisons in future work is given

Bioinspired design of a landing system with soft shock absorbers for autonomous aerial robots

© 2018 Wiley Periodicals Inc. One of the main challenges for autonomous aerial robots is to land safely on a target position on varied surface structures in real-world applications. Most of current aerial robots (especially multirotors) use only rigid landing gears, which limit the adaptability to environments and can cause damage to the sensitive cameras and other electronics onboard. This paper presents a bioinpsired landing system for autonomous aerial robots, built on the inspire–abstract–implement design paradigm and an additive manufacturing process for soft thermoplastic materials. This novel landing system consists of 3D printable Sarrus shock absorbers and soft landing pads which are integrated with an one-degree-of-freedom actuation mechanism. Both designs of the Sarrus shock absorber and the soft landing pad are analyzed via finite element analysis, and are characterized with dynamic mechanical measurements. The landing system with 3D printed soft components is characterized by completing landing tests on flat, convex, and concave steel structures and grassy field in a total of 60 times at different speeds between 1 and 2 m/s. The adaptability and shock absorption capacity of the proposed landing system is then evaluated and benchmarked against rigid legs. It reveals that the system is able to adapt to varied surface structures and reduce impact force by 540N at maximum. The bioinspired landing strategy presented in this paper opens a promising avenue in Aerial Biorobotics, where a cross-disciplinary approach in vehicle control and navigation is combined with soft technologies, enabled with adaptive morphology

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazard