The appearance of cars has raised materialistic civilization and living standard to an unprecedented level. Today, it is hard to imagine how we human beings can live without cars. Yet, motor vehicles can cause a great number of deaths and injuries as well as considerable economic losses, which have constituted the global burden. Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.

Road traffic injuries.

This research study explores the Global Positioning System (GPS), its history, and the process of discovery needed to create the most accurate GPS possible, as well as the contemporary applications of GPS technology. Starting with the first satellite in space, GPS has been a work in progress. Originally pursued by the military for improvements to military tactics, GPS has become integrated into the everyday lives of millions of people around the world. How GPS determines location is a dichotomy, with simplistic theory and complex application. Many factors go into GPS to provide a consistent, accurate location. The orbital planes the satellites are placed in provide 24/7 coverage globally, the L-band frequencies used were chosen specifically for the characteristics they possess, and the multiple atomic clocks installed on each satellite provide incredible accuracy down to the nanoseconds, which is quintessential in GPS accuracy. The applications in GPS are far reaching and more applications are continually being discovered. With as far as GPS technology has progressed, there are still several factors that degrade the signal and are a challenge to overcome. Many of these challenges can be corrected efficiently, however, others, such as scintillation and total electron content variability in the ionosphere, create major hurdles to overcome. Luckily, there are many programs that aid in the correction process of these hurdles. The History of GPS According to R. Saunders’ article ​A Short History of GPS Development,​ The Global Positioning System (GPS) has a long history of trial and error and refinement and improvement. It’s purpose has shifted from being a military strategic asset to commonplace among the general public with its use in traveling, farming, and even banking. The beginning of GPS, introduced with a simple idea, can be traced back to the Soviet Union in the late 1950’s. In 1957, the Soviet Union made history with successfully launching the first satellite in space. To track the satellite Sputnik, Physicists and Scientists at John Hopkins University’s Applied Physics Laboratory listened to the beeps Sputnik’s signals produced. They noticed that the beeps had a Doppler Effect or Doppler Shift as the satellite passed by. Much like the sound a siren makes as a fire truck approaches, then as it passes, the sound of the siren seems different. The change in timing between the beeps let the scientist know Sputnik’s location. This led to the idea of reversing that process, to give a location on the Earth. Using radio frequencies to determine location in a two dimensional plane had been around since WWII, but using satellites would push this technology into the three dimensional realm. The United States Navy, Army, and Air Force all began developing their own GPS satellites in the 1960’s, but this was no small task. In the early 1960’s, the Navy launched its first Transit Satellite. The failure of this satellite, however, was due to

/pdf/the-global-positioning-system-30ag469ov3.pdf

The Global Positioning System

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and b...

https://journals.sagepub.com/doi/pdf/10.1177/1729881418774673

Mobile robot path planning using an improved ant colony optimization

As a challenging optimization problem, path planning for mobile robot refers to searching an optimal or near-optimal path under different types of constrains in complex environments. In this paper, a self-adaptive learning particle swarm optimization (SLPSO) with different learning strategies is proposed to address this problem. First, we transform the path planning problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives: path length, collision risk degree and smoothness. Then, a novel self-adaptive learning mechanism is developed to adaptively select the most suitable search strategies at different stages of the optimization process, which can improve the search ability of particle swarm optimization (PSO). Moreover, in order to enhance the feasibility of the generated paths, we further apply the new bound violation handling schemes to restrict the velocity and position of each particle. Finally, experiments respectively with a simulated robot and a real robot are conducted and the results demonstrate the feasibility and effectiveness of SLPSO in solving mobile robot path planning problem.

Path planning for mobile robot using self-adaptive learning particle swarm optimization

Although there exist efficient methods to determine an optimal path in a graph, such as Dijkstra and A* algorithms, large instances of the path planning problem need more adequate and efficient techniques to obtain solutions in reasonable time We propose two new time-linear relaxed versions of Dijkstra (RD) and A* (RA*) algorithms to solve the global path planning problem in large grid environments The core idea consists in exploiting the grid-map structure to establish an accurate approximation of the optimal path, without visiting any cell more than once We conducted extensive simulations (1290 runs on 43 maps of various types) for the proposed algorithms, both in four-neighbor and eight-neighbor grid environments, and compared them against original Dijkstra and A* algorithms with different heuristics We demonstrate that our relaxed versions exhibit a substantial gain in terms of computational time (more than 3 times faster in average), and in most of tested problems an optimal solution (in at least 97 % of cases for RD and 82 % for RA*) or a very close one is reached (at most 9 % of extra length, and less than 2 % in average) Besides, the simulations also show that RA* provides a better trade-off between solution quality and execution time than previous bounded relaxations of A* that exist in the literature

Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments

Path planning is a critical combinatorial problem essential for the navigation of a mobile robot Several research initiatives, aiming at providing optimized solutions to this problem, have emerged Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are the two most widely used heuristics that have shown their effectiveness in solving such a problem This paper presents, smartPATH, a new hybrid ACO-GA algorithm to solve the global robot path planning problem The algorithm consists of a combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator for avoiding falling into a local minimum Our system model incorporates a Wireless Sensor Network (WSN) infrastructure to support the robot navigation, where sensor nodes are used as signposts that help locating the mobile robot, and guide it towards the target location We found out smartPATH outperforms classical ACO (CACO) and GA algorithms (as defined in the literature without modification) for solving the path planning problem both and Bellman-Ford shortest path method We demonstrate also that smartPATH reduces the execution time up to 649% in comparison with Bellman-Ford exact method and improves the solution quality up to 483% in comparison with CACO

smartPATH: A hybrid ACO-GA algorithm for robot path planning

Path planning is a fundamental optimization problem that is crucial for the navigation of a mobile robot. Among the vast array of optimization approaches, we focus in this paper on Ant Colony Optimization (ACO) and Genetic Algorithms (GA) for solving the global path planning problem in a static environment, considering their effectiveness in solving such a problem. Our objective is to design an efficient hybrid algorithm that takes profit of the advantages of both ACO and GA approaches for the sake of maximizing the chance to find the optimal path even under real-time constraints. In this paper, we present smartPATH, a new hybrid ACO-GA algorithm that relies on the combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator to reduce the risk of falling into a local minimum. We demonstrate through extensive simulations that smartPATH outperforms classical ACO (CACO), GA algorithms. It also outperforms the Dijkstra exact method in solving the pat...

/pdf/smartpath-an-efficient-hybrid-aco-ga-algorithm-for-solving-408d6lwv2r.pdf

SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots:

The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.

/pdf/intelligent-traffic-management-system-based-on-the-internet-3eh6252uqe.pdf

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Cooperative robots and their integration with Wireless Sensor Networks (WSNs) is an expanding area that still deserves significant research efforts. This paper presents a multi-robot surveillance application supported by a WSN. We investigate the problem of multi-robot coordination for target tracking and capturing. One key distinction of our problem model is to consider a WSN that supports the mission of the multi-robot team. We devised three mechanisms: centralized, distributed and market-based algorithms, which were extensively evaluated under the Player/Stage simulator. Simulation results show that the centralized approach is the most likely solution able to maintain an efficient system cost.

Coordination in a multi-robot surveillance application using Wireless Sensor Networks

Localization based on Received Signal Strength (RSS) is a key method for locating objects in Wireless Sensor Networks (WSNs). However, current RSS-based methods are ineffective at both deployment and operation design levels. First, they usually require a labor-intensive pre-deployment profiling operations to map the RSS to either locations or distances. Second often rely on heavy processing operations. These two design problems limit the possibility of implementing such localization techniques on resource-constrained sensor nodes, and also restrict their scalability and use in practice. In this book chapter, we discuss the challenges and limitations of RSS-based localization mechanisms and we propose, EasyLoc, an autonomous and practical RSS-based localization technique that improves on previous approaches in terms of ease of deployment and ease of implementation, while still providing a reasonable accuracy. EasyLoc is a plug-and-play and fully distributed RSS-based localization method that requires zero pre-deployment configuration. The idea consists in exploiting the available distance information between anchors to derive an online and anchor-specific RSS to distance mapping. We show that, in addition to its simplicity, EasyLoc provides, in the best case, a reasonable average distance error of \(2\) m in an indoor environment of 30 \(\mathrm{m}^2\).

Khaled Al-Shalfan

Papers

smartPATH: A hybrid ACO-GA algorithm for robot path planning

SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots:

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Coordination in a multi-robot surveillance application using Wireless Sensor Networks

EasyLoc: Plug-and-Play RSS-Based Localization in Wireless Sensor Networks