A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems

Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller. This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control. The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties

Using Particle Swarm Optimization for Power System Stabilizer and energy storage in the SMIB system under load shedding conditions

Generator instability, which manifests as oscillations in frequency and rotor angle, is brought on by sudden disruptions in the power supply. Power System Stabilizer (PSS) and Energy Storage are additional controllers that enhance generator stability. Energy storage types include superconducting magnetic (SMES) and capacitive (CES) storage. If the correct settings are employed, PSS, SMES, and CES coordination can boost system performance. It is necessary to use accurate and effective PSS, SMES, and CES tuning techniques. Artificial intelligence techniques can replace traditional trial-and-error tuning techniques and assist in adjusting controller parameters. According to this study, the PSS, SMES, and CES parameters can be optimized using a method based on particle swarm optimization (PSO). Based on the investigation's findings, PSO executes quick and accurate calculations in the fifth iteration with a fitness function value of 0.007813. The PSO aims to reduce the integral time absolute error (ITAE). With the addition of a load-shedding instance, the case study utilized the Single Machine Infinite Bus (SMIB) technology. The frequency response and rotor angle of the SMIB system are shown via time domain simulation. The analysis's findings demonstrate that the controller combination can offer stability, reducing overshoot oscillations and enabling quick settling times.

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

[PDF] from annalsofrscb.ro Predictive Analytics for Sentiment Classification of Social Media Data Using Deep Neural Network

A huge amount of user-generated data in the form of tweets or reviews on social media can be collected and analyzed for making informed decisions. This paper uses the novel deep learning model, namely the Elite Opposition-based Bat Algorithm for Deep Neural Network (EOBA-DNN) for performing polarity classification of the social media data. The proposed method includes three major steps, such as preprocessing, term weighting, and sentiment classification for identifying the polarity of the data. The results show that the EOBA-DNN outperforms other existing algorithms with improved accuracy for Sentiment Classification