Design study of an earthquake rescue robot

This thesis describes the design of a brush robot for earthquake rescue and for traversing pipes with varied cross sectional shape. Earthquake rescue is a very dangerous, difficult and challenging task, in which emergency services rescue people who are trapped in man-made structures, such as collapsed buildings after an earthquake. The building collapse may have been caused by natural or man-made events. This technology is also applicable to tunnel collapse and land slips. The focus of this work is finding the location of victims and provision of primary life support and communications. To illustrate the concept of the robot, the thesis first discusses the current development of rescue robots and pipe robots. Then the thesis focuses on the description of a brush based pipe robot, developed by the University of Durham, which would be used as the basis of an earthquake rescue robot. The concept of the robot was illustrated and compared with other current rescue robots and pipe robots. After outlining the advantages of this robot concept, a robot body shape change theory was proposed and theoretical simulations were used to verily the practicality of the robot shape change theory. The thesis also illustrates the design of the working principle and design of a robot sensor, which was subsequently used in the robot shape change experiments. The robot body shape change experiments and the experimental results are described and discussed. The experimental results illustrate the robot concept and support the robot body shape change theory. Chapter 6 focuses on the brush unit traction investigation, bristle theory and mathematical model. Furthermore, the bristle theory and mathematical model were used to explain the variation of traction force in the traction experiments

Deep reinforcement learning for drone navigation using sensor data

Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios

An Advanced Home ElderCare Service

With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

Socially Believable Robots

Long-term companionship, emotional attachment and realistic interaction with robots have always been the ultimate sign of technological advancement projected by sci-fi literature and entertainment industry. With the advent of artificial intelligence, we have indeed stepped into an era of socially believable robots or humanoids. Affective computing has enabled the deployment of emotional or social robots to a certain level in social settings like informatics, customer services and health care. Nevertheless, social believability of a robot is communicated through its physical embodiment and natural expressiveness. With each passing year, innovations in chemical and mechanical engineering have facilitated life-like embodiments of robotics; however, still much work is required for developing a “social intelligence” in a robot in order to maintain the illusion of dealing with a real human being. This chapter is a collection of research studies on the modeling of complex autonomous systems. It will further shed light on how different social settings require different levels of social intelligence and what are the implications of integrating a socially and emotionally believable machine in a society driven by behaviors and actions

Knowledge representation for culturally competent personal robots: requirements, design principles, implementation, and assessment

Culture, intended as the set of beliefs, values, ideas, language, norms and customs which compose a person’s life, is an essential element to know by any robot for personal assistance. Culture, intended as that person’s background, can be an invaluable source of information to drive and speed up the process of discovering and adapting to the person’s habits, preferences and needs. This article discusses the requirements posed by cultural competence on the knowledge management system of a robot. We propose a framework for cultural knowledge representation that relies on (i) a three layer ontology for storing concepts of relevance, culture specific information and statistics, person-specific information and preferences; (ii) an algorithm for the acquisition of person-specific knowledge, which uses culture specific knowledge to drive the search; (iii) a Bayesian Network for speeding up the adaptation to the person by propagating the effects of acquiring one specific information onto interconnected concepts. We have conducted a preliminary evaluation of the framework involving 159 Italian and German volunteers and considering 122 among habits, attitudes and social norms

Knowledge representation for culturally competent personal robots: requirements, design principles, implementation, and assessment

Culture, intended as the set of beliefs, values, ideas, language, norms and customs which compose a person’s life, is an essential element to know by any robot for personal assistance. Culture, intended as that person’s background, can be an invaluable source of information to drive and speed up the process of discovering and adapting to the person’s habits, preferences and needs. This article discusses the requirements posed by cultural competence on the knowledge management system of a robot. We propose a framework for cultural knowledge representation that relies on (i) a three layer ontology for storing concepts of relevance, culture specific information and statistics, person-specific information and preferences; (ii) an algorithm for the acquisition of person-specific knowledge, which uses culture specific knowledge to drive the search; (iii) a Bayesian Network for speeding up the adaptation to the person by propagating the effects of acquiring one specific information onto interconnected concepts. We have conducted a preliminary evaluation of the framework involving 159 Italian and German volunteers and considering 122 among habits, attitudes and social norms

Intercepting a Target with Sensor Swarms

The article of record as published may be located at http://dx.doi.org/10.1109/HICSS.2013.281This paper introduces a new coordination method to intercept a mobile target in urban areas with a team of sensor platforms. The task is to intercept the target before it leaves the area. The approach combines algorithmic concepts from ant colony and particle swarm optimization in order to bias the search and to spread the team in the search area. The algorithms introduced are tested in simulation experiments on grids. The success probabilities measured are relatively high for most parameter combinations, and the target is intercepted in roughly half the simulation time on average. Furthermore, the experiments reveal robust behavior with regard to the parameter setting