Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems.

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks

Control of robot swarms through natural language dialogue: A case study on monitoring fires

There are numerous environmental and non-environmental disasters happening throughout the world, representing a big danger to common people, community helpers, to the fauna and flora. Developing a program capable of controlling swarms of robots, using natural language processing (NLP) and further on, a speech to text system, will enable a more mobile solution, with no need for keyboard and mouse or a mobile device for operating with the robots. Using a welldeveloped NLP system will allow the program to understand natural languagebased interactions, making this system able to be used in different contexts. In firefighting, the use of robots, more specifically drones, enables new ways to obtain reliable information that before was based on guesses or knowledge from someone who had long-time experience on field. Using a swarm of robots to monitor fire enables innumerous advantages, from the creation of a dynamic fire map, climate information inside the fire, to finding lost firefighters on field through the generated map. This work uses firefighting as a case-study, but other situations can be considered, like searching someone in the sea or searching for toxins in an open environmental area.Existem muitos desastres ambientais e não ambientais em todo o mundo, representando um grande perigo para pessoas comuns, ajudantes da comunidade e para a fauna e flora. O desenvolvimento de um programa capaz de controlar enxames de robôs, usando Processamento Computacional da Língua (PCL) e, posteriormente, um sistema de fala-para-texto, permitirá uma solução mais móvel, sem necessidade de teclado e rato ou dispositivos móveis para operar com os robôs. O uso de um sistema bem desenvolvido de PCL permitirá que o programa entenda interações baseadas em linguagem natural, tornando-o capaz de ser usado em diferentes contextos. O uso de robôs (mais especificamente drones) no combate a incêndios, permite novas maneiras de obter informações confiáveis que antes eram baseadas em suposições ou conhecimentos de pessoas com longa experiência em campo. O uso de um enxame de robôs para monitorizar o incêndio permite inúmeras vantagens, desde a criação de um mapa dinâmico do incêndio, informações climáticas dentro do mesmo, até encontrar bombeiros perdidos no campo, através do mapa gerado pelos robôs. Este trabalho usa o combate a incêndios como um estudo de caso, mas outras situações podem ser consideradas, como procurar alguém no mar ou procurar toxinas numa área ambiental aberta

Small scale implementation of a robotic urban search and rescue network

Thesis (M.S.) University of Alaska Fairbanks, 2012With the advancement of robotics technologies, it is now possible to use robots for high risk jobs that have historically been accomplished by humans. One such example is the use of robots for Urban Search and Rescue (USR): finding chemical spills, fires, or human survivors in disaster areas. With the ability to include inexpensive wireless transceivers, it is possible to network numerous robots as part of a swarm that can explore an area much more expeditiously than a single robot can. With the inclusion of wireless capabilities comes the necessity to create a protocol for the communication between robots. Also necessary is the creation of an exploration protocol that allows the network of robots to explore such a building or search area in as little time as possible yet as accurately as possible. This thesis covers the development of such a network of robots, starting with the hardware/software co-design, the individual robots' control mechanisms, and their mapping and communications protocols

Location based services in wireless ad hoc networks

In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii

The emergence of biofilms:Computational and experimental studies

The response of biofilms to any external stimuli is a cumulative response aggregated from individual bacteria residing within the biofilm. The organizational complexity of biofilm can be studied effectively by understanding bacterial interactions at cell level. The overall aim of the thesis is to explore the complex evolutionary behaviour of bacterial biofilms. This thesis is divided into three major studies based on the type of perturbation analysed in the study. The first study analyses the physics behind the development of mushroom-shaped structures from the influence of nutrient cues in biofilms. Glazier-Graner-Hogeweg model is used to simulate the cell characteristics. From the study, it is observed that chemotaxis of bacterial cells towards nutrient source is one of the major precursors for formation of mushroom-shaped structures. The objective of the second study is to analyse the impact of environmental conditions on the inter-biofilm quorum sensing (QS) signalling. Using a hybrid convection-diffusion-reaction model, the simulations predict the diffusivity of QS molecules, the spatiotemporal variations of QS signal concentrations and the competition outcome between QS and quorum quenching mutant bacterial communities. The mechanical effects associated with the fluid-biofilm interaction is addressed in the third study. A novel fluid-structure interaction model based on fluid dynamics and structural energy minimization is developed in the study. Model simulations are used to analyse the detachment and surface effects of the fluid stresses on the biofilm. In addition to the mechanistic models described, a separate study is carried out to estimate the computational efficiency of the biofilm simulation models

Towards Real-time Remote Processing of Laparoscopic Video

Laparoscopic surgery is a minimally invasive technique where surgeons insert a small video camera into the patient\u27s body to visualize internal organs and use small tools to perform these procedures. However, the benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic system is the daVinci-si robotic surgical vision system. The video streams generate approximately 360 megabytes of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Real-time processing this large stream of data on a bedside PC, single or dual node setup, may be challenging and a high-performance computing (HPC) environment is not typically available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate (fps), it is required that each 11.9 MB (1080p) video frame be processed by a server and returned within the time this frame is displayed or 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. We have implemented and compared performance of compression, segmentation and registration algorithms on Clemson\u27s Palmetto supercomputer using dual Nvidia graphics processing units (GPUs) per node and compute unified device architecture (CUDA) programming model. We developed three separate applications that run simultaneously: video acquisition, image processing, and video display. The image processing application allows several algorithms to run simultaneously on different cluster nodes and transfer images through message passing interface (MPI). Our segmentation and registration algorithms resulted in an acceleration factor of around 2 and 8 times respectively. To achieve a higher frame rate, we also resized images and reduced the overall processing time. As a result, using high-speed network to access computing clusters with GPUs to implement these algorithms in parallel will improve surgical procedures by providing real-time medical image processing and laparoscopic data

Analysis and control of distributed cooperative systems.

As part of DARPA Information Processing Technology Office (IPTO) Software for Distributed Robotics (SDR) Program, Sandia National Laboratories has developed analysis and control software for coordinating tens to thousands of autonomous cooperative robotic agents (primarily unmanned ground vehicles) performing military operations such as reconnaissance, surveillance and target acquisition; countermine and explosive ordnance disposal; force protection and physical security; and logistics support. Due to the nature of these applications, the control techniques must be distributed, and they must not rely on high bandwidth communication between agents. At the same time, a single soldier must easily direct these large-scale systems. Finally, the control techniques must be provably convergent so as not to cause undo harm to civilians. In this project, provably convergent, moderate communication bandwidth, distributed control algorithms have been developed that can be regulated by a single soldier. We have simulated in great detail the control of low numbers of vehicles (up to 20) navigating throughout a building, and we have simulated in lesser detail the control of larger numbers of vehicles (up to 1000) trying to locate several targets in a large outdoor facility. Finally, we have experimentally validated the resulting control algorithms on smaller numbers of autonomous vehicles