SMART SENSOR AND TRACKING SYSTEM FOR UNDERGROUND MINING

The thesis predominantly discusses a smart sensor and tracking system for under- ground mining, as developed by the author. The tracking system is developed by two steps, the rst of which involves nding an e cient way to measure the distance, and the second of which involves localizing the positions of each miner in real-time. For the rst step, a Received Signal Strength Indicator (RSSI) is used to measure the distance between two points by indicating the amount of energy lost during the transmission. Due to environmental and human factors, errors exist when using RSSI to measure distance. Three methods are taken to reduce the error: Gaussian distribution, statistical average and preset points. It can be observed that the average error between actual distance and measured distance is only 0.1145 meters using the proposed model. In regards to the localization, the "3-point localization method" is considered rst. With the proposed method, the result of the localization is improved by 0.6 meters, as compared to the "2-point localization method". The transmission method for the project is then discussed. After comparing sev- eral transmission protocols in the market, ZigBee was chosen for the signal trans- mission. With the Zigbee protocol, up to 65000 nodes can be connected, which are suitable for many miners using the system at the same time. The power supply for the ZigBee protocol is only 1mW for each unit, thus potentially saving a great amount of energy during the transmission. To render the tracking system more powerful, two smart sensors are installed: an MQ-2 sensor and a temperature sensor. The MQ-2 sensor is used to detect the harmful gas and smoke. In the event that the sensor's detected value is beyond the threshold, it will provide a warning for the supervisor on the ground

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area

NIOSH Mining Program: Evidence Package for 2008-2018

This document contains materials to demonstrate the relevance and impact of the Mining Program\u2019s work in the areas of disaster preparedness and response, ground control, and respirable hazards

Toward Co-Robotic Construction: Visual Site Monitoring & Hazard Detection to Ensure Worker Safety

Construction has remained the least automated and productive as well as the most hazardous industry. Moreover, it has been plagued by a significant lack of diversity in its workforce as well as aging laborers. To address these issues, co-robotic construction has emerged as a new paradigm of construction. The industry is gradually gearing up to embrace robotic solutions, and many construction robots with various degrees of autonomy are under development or in the early stage of deployment. Presenting a different horizon of construction—harmonious co-existence and co-work between workers and robots—co-robotic construction is expected to reform labor-intensive construction into the more productive, safer, and more inclusive industry. However, an in-depth understanding of the robots’ situational intelligence is still lacking, particularly conclusive logic and technologies to ensure workers’ safety nearby autonomous (or semi-) robots, which is fundamental in realizing the co-robotic construction. To fill the gap, this research established a comprehensive robotic hazard detection roadmap and developed core technologies to realize it, leveraging unmanned aerial vehicles, computer vision, and deep learning. In this dissertation, I describe how the developed technologies with a conclusive logic can pro-actively detect the robotics hazards taking various forms and scenarios in an unstructured and dynamic construction environment. The successful implementation of the robotic hazard detection roadmap in co-robotic construction allows for timely interventions such as pro-active robot control and worker feedback, which contributes to reducing robotic accidents. Eventually, this will make human-robot co-existence and collaboration safer, while also helping to build workers’ trust in robot co-workers. Finally, the ensured safety and trust between robots and workers would contribute to promoting construction enterprises to embrace robotic solutions, boosting construction reformation toward innovative co-robotic construction.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167981/1/daeho_1.pd

A sensor-based personal navigation system and its application for incorporating humans into a human-robot team

In this thesis methods for the sensor-based localisation of human beings are studied. The thesis presents the theory, test results and a realisation of the methods, which is called PeNa. PeNa is further applied to incorporate a human into a human-robot team that performs a simulated search and rescue task. Human-robot teamwork provides the vision for this thesis. Furthermore, the PeLoTe project and its search and rescue task provided the primary motivation for the research. However, the major part of this work and contribution is on sensor-based personal navigation. The approaches studied for personal navigation systems are based on sensor-based dead reckoning, laser-based dead reckoning, and map-based localisation. Sensor-based dead reckoning is based on heading estimation using a compass and gyro and step length estimation. Two alternative step length estimation methods are presented, ultrasound-based and accelerometer-based. Two laser dead reckoning methods are presented; a pose correlation method and a combined angle histogram matcher with position correlation. Furthermore, there are three variations for map-based localisation based on the well-known Monte Carlo Localisation (MCL): topological MCL, scan-based MCL, and a combined MCL method. As a result of the research it can be stated that it is possible to build a personal navigation system that can localise a human being indoors using only self-contained sensors. The results also show that this can be achieved using various combinations of sensors and methods. Furthermore, the personal navigation system that was developed is used to incorporate a human being into a human-robot team performing a search and rescue task. The initial results show that the location information provides a basis for creating situational awareness for a spatially distributed team

Horizon 2020-funded security research projects with dual-use potential: An overview (2014-2018)

The analysis carried out in this report facilitates the identification of dual-use research topics and projects funded under Horizon 2020 that have a dual-use civilian/military potential, the results of which could be applied both by security and defence stakeholders (including industry). In this way, it could support the future security and defence research programmes in their attempt of avoiding duplication of investments and promoting synergies.JRC.E.7-Knowledge for Security and Migratio

Dynamic virtual reality user interface for teleoperation of heterogeneous robot teams

This research investigates the possibility to improve current teleoperation control for heterogeneous robot teams using modern Human-Computer Interaction (HCI) techniques such as Virtual Reality. It proposes a dynamic teleoperation Virtual Reality User Interface (VRUI) framework to improve the current approach to teleoperating heterogeneous robot teams

Digital Twins in Industry

Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies