A study of mobile robot motion planning

This thesis studies motion planning for mobile robots in various environments. The basic tools for the research are the configuration space and the visibility graph. A new approach is developed which generates a smoothed minimum time path. The difference between this and the Minimum Time Path at Visibility Node (MTPVN) is that there is more clearance between the robot and the obstacles, and so it is safer. The accessibility graph plays an important role in motion planning for a massless mobile robot in dynamic environments. It can generate a minimum time motion in 0(n2»log(n)) computation time, where n is the number of vertices of all the polygonal obstacles. If the robot is not considered to be massless (that is, it requires time to accelerate), the space time approach becomes a 3D problem which requires exponential time and memory. A new approach is presented here based on the improved accessibility polygon and improved accessibility graph, which generates a minimum time motion for a mobile robot with mass in O((n+k)2»log(n+k)) time, where n is the number of vertices of the obstacles and k is the number of obstacles. Since k is much less than n, so the computation time for this approach is almost the same as the accessibility graph approach. The accessibility graph approach is extended to solve motion planning for robots in three dimensional environments. The three dimensional accessibility graph is constructed based on the concept of the accessibility polyhedron. Based on the properties of minimum time motion, an approach is proposed to search the three dimensional accessibility graph to generate the minimum time motion. Motion planning in binary image representation environment is also studied. Fuzzy logic based digital image processing has been studied. The concept of Fuzzy Principal Index Of Area Coverage (PIOAC) is proposed to recognise and match objects in consecutive images. Experiments show that PIOAC is useful in recognising objects. The visibility graph of a binary image representation environment is very inefficient, so the approach usually used to plan the motion for such an environment is the quadtree approach. In this research, polygonizing an obstacle is proposed. The approaches developed for various environments can be used to solve the motion planning problem without any modification. A simulation system is designed to simulate the approaches

Microgrids: Planning, Protection and Control

This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation

Efficient Topology Management and Geographic Routing in High-Capacity Continental-Scale Airborne Networks

Large-scale high-capacity communication networks among mobile airborne platforms are quickly becoming a reality. Today, both Google and Facebook are seeking to form networks among high-flying balloons and drones in an effort to provide Internet connections from the stratosphere to users on the ground. This dissertation proposes an alternative, namely using the cargo and passenger aircraft already in the skies as the principal components of such a network. My work presents the design of a network architecture to overcome the challenges of managing the topology of and routing data within these continental-scale highly-dynamic networks. The architecture relies on directional communication links, such as free-space optical communication links (FSO), to achieve high data rates over long distances. However, these state-of-the-art communication systems present new networking challenges. One such challenge is that of managing the physical topology of the network. Such a topology must be explicitly managed, ensuring that each directional data link is pointed at and connected with an appropriate neighbor (which is also pointing back) to yield an acceptable global topology. To overcome this challenge, a distributed topology management framework and associated topology generation algorithms were designed, implemented, and tested via simulation. The framework is capable of managing the topology of thousands of nodes in a continental-scale airborne network and has no communication overhead except that required to exchange position information among nearby nodes. A second component of the work concerns routing data at high data rates through a constantly changing network topology. To address this issue Topology Aware Geographic Routing (TAG), a position-based routing protocol was developed that strategically uses local topology information to make better local forwarding decisions, decreasing the number of hops required to deliver a packet, when compared with other geographic routing protocols. In addition, unlike other similar protocols, TAG is able to reliably deliver packets even when the topology changes while the packet is in flight. These protocols are tested and validated in a series of simulations where nodes trace the trajectories recorded from thousands of actual flights. These simulations indicate that the topology management framework and TAG are able to perform well in large-scale high-density conditions, over long durations, and are able to support tens of thousands of 1 Mbps flows.Doctor of Philosoph

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Enabling Millimeter Wave Communications for Use Cases of 5G and Beyond Networks

The wide bandwidth requirements of the fifth generation (5G) and beyond networks are driving the move to millimeter wave (mmWave) bands where it can provide a huge increase in the available bandwidth. Increasing the bandwidth is an effective way to improve the channel capacity with limited power. Moreover, the short wavelengths of such bands enable massive number of antennas to be integrated together in small areas. With such massive number of antennas, narrow beamwidth beams can be obtained which in turn can improve the security. Furthermore, the massive number of antennas can help in mitigating the severe path-loss at mmWave frequencies, and realize high data rate communication at reasonable distances. Nevertheless, one of the main bottlenecks of mmWave communications is the signal blockage. This is due to weak diffraction ability and severe penetration losses by many common building materials such as brick, and mortar as well as the losses due to human bodies. Thus, user mobility and/or small movements of obstacles and reflectors cause rapid channel gain variations which leads to unreliable communication links. The harsh propagation environment at such high frequencies makes it hard to provide a reliable service, hence, maintaining connectivity is one key design challenge in mmWave networks. Relays represent a promising approach to improve mmWave connectivity where they can redirect the signal to avoid the obstacles existing in the propagation environment. However, routing in mmWave networks is known to be a very challenging problem due to the inherent propagation characteristics of mmWave frequencies. Furthermore, inflexible routing technique may worsen network performance and increase scheduling overhead. As such, designing an appropriate transmission routing technique for each service is a crucial issue in mmWave networks. Indeed, multiple factors must be taken into account in the routing process, such as guaranteeing the robustness of network connectivity and providing high data rates. In this thesis, we propose an analytical framework to investigate the network reliability of mmWave relaying systems for multi-hop transmissions. We also propose a flexible routing technique for mmWave networks, namely the $n^{\rm th}$ best routing technique. The performance of the proposed routing technique is investigated using tools from stochastic geometry. The obtained results provide useful insights on adjusting the signal noise ratio (SNR) threshold for decode and forward (DF) relay according to the order of the best relay, blockage and relay densities in order to improve spectral efficiency. We also propose a novel mathematical framework to investigate the performance of two appropriate routing techniques for mmWave networks, namely minimum hop count (MHC) and nearest LoS relay to the destination with MHC (NLR-MHC) to support wide range of use cases for 5G and beyond networks. Analytical models are provided to evaluate the performance of the proposed techniques using tools from stochastic geometry. In doing so, we model the distribution of hop count using phase-type distribution, and then we use this distribution to derive analytical results for the coverage probability and spectral efficiency. Capitalizing on the derived results, we introduce a comprehensive study of the effects of different system parameters on the performance of multi-hop mmWave systems. These findings provide important insights for designing multi-hop mmWave networks with better performance. Furthermore, we adapt the proposed relay selection technique for IoT devices in mmWave relaying systems to prolong the IoT device’s battery life. The obtained results reveal the trade-off between the network connectivity and the energy consumption of IoT devices. Lastly, we have exploited the enormous bandwidth available in the mmWave band to support reliable fronthaul links for cell-free (CF) massive multiple-input multiple-output (MIMO). We provide a comprehensive investigation of different system parameters on the uplink (UL) performance of mmWave fronthaul-based CF mMIMO systems. Results reveal that increasing the access point (AP) density beyond a certain limit would not achieve further improvement in the UL data rates. Also, the higher number of antennas per AP may even cause UL data rates degradation

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues