Efficient mobile robot path planning by Voronoi-based heuristic

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Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution

Shepherding involves herding a swarm of agents (\emph{sheep}) by another a control agent (\emph{sheepdog}) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using \emph{way points} on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved

Application of a mobile robot to spatial mapping of radioactive substances in indoor environment

Nuclear medicine requires the use of radioactive substances that can contaminate critical areas (dangerous or hazardous) where the presence of a human must be reduced or avoided. The present work uses a mobile robot in real environment and 3D simulation to develop a method to realize spatial mapping of radioactive substances. The robot should visit all the waypoints arranged in a grid of connectivity that represents the environment. The work presents the methodology to perform the path planning, control and estimation of the robot location. For path planning two methods are approached, one a heuristic method based on observation of problem and another one was carried out an adaptation in the operations of the genetic algorithm. The control of the actuators was based on two methodologies, being the first to follow points and the second to follow trajectories. To locate the real mobile robot, the extended Kalman filter was used to fuse an ultra-wide band sensor with odometry, thus estimating the position and orientation of the mobile agent. The validation of the obtained results occurred using a low cost system with a laser range finder.A medicina nuclear requer o uso de substâncias radioativas que pode vir a contaminar áreas críticas, onde a presença de um ser humano deve ser reduzida ou evitada. O presente trabalho utiliza um robô móvel em ambiente real e em simulação 3D para desenvolver um método para o mapeamento espacial de substâncias radioativas. O robô deve visitar todos os waypoinst dispostos em uma grelha de conectividade que representa o ambiente. O trabalho apresenta a metodologia para realizar o planejamento de rota, controle e estimação da localização do robô. Para o planejamento de rota são abordados dois métodos, um baseado na heurística ao observar o problema e ou outro foi realizado uma adaptação nas operações do algoritmo genético. O controle dos atuadores foi baseado em duas metodologias, sendo a primeira para seguir de pontos e a segunda seguir trajetórias. Para localizar o robô móvel real foi utilizado o filtro de Kalman extendido para a fusão entre um sensor ultra-wide band e odometria, estimando assim a posição e orientação do agente móvel. A validação dos resultados obtidos ocorreu utilizando um sistema de baixo custo com um laser range finder

Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.Comment: 8 pages, 8 figure