Hybrid Swarm Algorithm for Mobile Robot Path Planning

           The adoption of lightweight and effective swarm algorithms is required for low resource usage algorithms for mobile robot path planning crises. We present a hybrid swarm approach in this study that combines the best features of particle swarm optimization and river formation dynamics. This method looks for the shortest route while keeping the path as smooth as feasible. The best qualities of both approaches are combined and leveraged by the hybrid RFD-PSO methodology. While the RFD algorithm is well known for its smooth path discovery, it needs a lot of drops for good convergence and suffers from sinuosity problems. The generated hybrid RFD-PSO algorithm synergistically balances PSO's fast convergence with the river method's adaptive exploration and exploitation. Comparing the simulation results of the proposed method versus the Ant Colony Optimization (ACO), modified Ant Colony Optimization ACO*, PSO, RFD, A*, and Dijkstra’s, Hybrid RFD-PSO have better results in creating optimal path

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

Sistema robótico basado en el comportamiento de las hormigas para la optimización en la búsqueda de rutas más cortas

In recent decades, bio-inspired systems or biological systems have become essential elements for the understanding and explanation of the logic and complexity of life.[1] Given a problem or task, it is always possible to find a way to find a solution, but when the tasks to be performed become complex, so do the solutions. Given a problem or task, it is always possible to find a way to find a solution, but when the tasks to be performed become complex, so do the solutions become more complex, so it is more practical to provide solutions from bio-inspired systems that have already provided solutions to many existing problems in our reality through evolution, which leads to the implementation of a bio-inspired algorithm, which takes as a basis the behavior of what nature already provides and uses it in its favor to solve real-life problems more efficiently [2][3].En las últimas décadas, los sistemas bio-inspirados o sistemas biológicos se han convertido en elementos imprescindibles para la comprensión y la explicación de la lógica y la complejidad de la vida.[1]Dado un problema o tarea siempre se puede buscar la manera de dar con una solución, pero cuando las tareas a realizar se vuelven complejas, así mismo se hacen más complejas las soluciones, por lo cual es más práctico dar solución desde los sistemas bio-inspirados que ya han dado solución a muchos problemas existentes en nuestra realidad por medio de la evolución, lo cual lleva a la implementación de un algoritmo bio-inspirado, que toma como base el comportamiento de lo que ya otorga la naturaleza y lo usa en su favor para así resolver de manera más eficiente problemas de la vida real [2][3]

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Development of Hand-cleaning Service-oriented Autonomous Navigation Robot

[[abstract]]This paper proposes the development of an autonomous navigation robot with hand-cleaning service in indoor environments. To navigate in unknown environments and provide service, the robot is with several intelligent behaviors including wall-following, obstacle avoidance, autonomous navigation, and human detection. A laser-sensor-based approach is used in the wall-following and obstacle avoidance behavior controllers. A preliminary map-matching algorithm is applied in the localization strategy of autonomous navigation in which the robot can acquire the current location and then move toward to the target position. In this study a hand-cleaning mechanism is embedded into the robot and the service will activate while a human is recognized within the designated range. The overall robotic system is carried out using a two-wheeled driving mobile robot with LabVIEW as an integration tool. The experimental results demonstrate the practicable application of the proposed approach.[[conferencetype]]國際[[conferencedate]]20121014~20121017[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Seoul, Kore

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Impact of initialization of a modified particle swarm optimization on cooperative source searching

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization