There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

A Survey of Path Planning Algorithms for Mobile Robots

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called modified aging ant colony optimization (AACO). The AACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

https://www.mdpi.com/1424-8220/20/7/1880/pdf

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments.

A 30 MJ (8.4 kWh) Superconducting Magnetic Energy Storage (SMES) unit with a 10 MW converter has been installed and commissioned at the Bonneville Power Administration (BPA) substation in Tacoma, Washington. This is the first large-scale application in the US of superconductivity in an electric utility system. The unit, which is capable of absorbing and releasing up to 10 MJ of energy at a frequency of 0.35 Hz, was designed to damp the dominant power swing mode of the Pacific AC Intertie. This paper describes the electrical characteristics of the magnetic energy storage unit, its modes of operation, results of device tests, means for controlling real and reactive power, and some initial power system response tests. A short summary of the operating history of the unit over the first eleven months is also presented.

Commissioning tests of the Bonneville Power Administration 30 MJ superconducting magnetic energy storage unit

Mobile robots work in different kinds of environment, and it is necessary for them to move and maneuver in places with objects and obstacles. In order to navigate the robot in a collision free path, path planning algorithms have been presented. The main goal of path planning is to determine the optimal possible path between the initial point and the defined goal position in the minimal possible time. In this work, a path planning method by utilizing genetic algorithm (GA) is presented. The optimized path in terms of length and cost is generated by GA optimization. The proposed method is a global path planning method with hexagonal grid map modelling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software. The simulation results are presented and analyzed.

Global Path Planning for Autonomous Mobile Robot Using Genetic Algorithm

In this paper, we propose a new area-based stereo matching method by improving the classical Census transform It is a difficult task to match corresponding points in two images taken by stereo cameras, mostly under variant illumination and non-ideal conditions The classic Census nonparametric transform did some improvements in the accuracy of disparity map in these conditions but it also has some disadvantages The results were not robust under different illumination, and because of the complexity the performance was not suitable for real-time robotic systems In order to solve these problems, this paper presents an improved Census transform using Maximum intensity differences of the pixel placed in the center of a defined window and the pixels in the neighborhood to reduce complexity and obtain better performance and needs a smaller window size to obtain best accuracy compared to the Census transform Experimental results show that the proposed method, achieves better efficiency in term of speed and robustness against illumination changes

A new fast and robust stereo matching algorithm for robotic systems

In this paper we present a new obstacle detection method, based on stereo vision, without combination with any other kind of sensors. The proposed method uses a differential image transform algorithm to gain robustness against illumination changes. This method increases the speed of program execution while keeping the performance of stereo vision algorithm in term of accuracy in the same level with the previous algorithms. Moreover, we implement this method into a stereo vision based robot while adding some new features to widen the depth detection range. With the help of the proposed method, the robot detects obstacles between 25cm to 400cm from robot cameras. The result shows the robot has the ability to work in a wide variety of lighting conditions, while the stereo vision part of the robot does the depth detection computation with the speed of 30FPS.

Stereo vision based robots: Fast and robust obstacle detection method

Software must be well developed and maintainable to adapt to the constantly changing requirement of the competitive world. In this article, we distinct different software maintainability prediction models and techniques which can help us to predict the maintainability of software, and can lead us to minimum the effort required to fix the faults in the software and the software will be more maintainable. We have gathered our data from different studies focused on the accuracy of the prediction models as criteria. The results of our study showed that there is a little evidence on the accuracy results of the software maintainability prediction models.

Investigation of software maintainability prediction models

Mobile robots work in unfamiliar and unconstructed environments with no previous knowledge. In order to prevent any collisions between the robot and the other objects, dynamic path planning algorithms are presented. Researchers have been presenting new algorithms to overcome the dynamic path planning dilemma, continuously. Most of the time, the prepared algorithm cannot be implemented to the robot directly, since potential problems in the algorithm may lead to endanger the robot or cause other safety difficulties. Hence, it is preferred to test and examine the robot’s behaviour in a simulated environment, before the empirical test. In this paper, we propose a simulation of dynamic path planning algorithm. As a result of this work, D* algorithm is implemented with four different two-dimensional map modeling methods of Square Tiles, Hexagons, Enhanced Hexagons, and Octiles. Then the simulation results are compared based on their speed, number of searched cells, path cost and traveled distance, to point out the most effective map modeling methods.

Simulation of Dynamic Path Planning for Real-Time Vision-Base Robots

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