SLAM and Exploration using Differential Evolution and Fast Marching

Garrido, Santiago; Moreno, Lourdes; Blanco, Dolores

doi:10.5772/17376

Cited by 1 publication

(1 citation statement)

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“…This approach employed GAs to generate a global path based on prior information of the environment. In 2015, the work by [6] proposed a method that finds a sequence of actions using a fitness function that evaluates the actions executed in the current generation. In [7], an autonomous exploration strategy was developed by combining the simulation localization and mapping (SLAM) and Voronoi methods for mapping the environment and differential evolution (DE) algorithm for localization.…”

Section: Introductionmentioning

confidence: 99%

Experimental study of path planning problem using EMCOA for a holonomic mobile robot

Alireza¹,

Duchaine²,

Tony³

2021

J. of Syst. Eng. Electron.

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In this paper, a comparative study of the path planning problem using evolutionary algorithms, in comparison with classical methods such as the algorithm, is presented for a holonomic mobile robot. The configured navigation system, which consists of the integration of sensors sources, map formatting, global and local path planners, and the base controller, aims to enable the robot to follow the shortest smooth path delicately. Grid-based mapping is used for scoring paths efficiently, allowing the determination of collision-free trajectories from the initial to the target position. This work considers the evolutionary algorithms, the mutated cuckoo optimization algorithm (MCOA) and the genetic algorithm (GA), as a global planner to find the shortest safe path among others. A non-uniform motion coefficient is introduced for MCOA in order to increase the performance of this algorithm. A series of experiments are accomplished and analyzed to confirm the performance of the global planner implemented on a holonomic mobile robot. The results of the experiments show the capacity of the planner framework with respect to the path planning problem under various obstacle layouts.

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Section: Introductionmentioning

confidence: 99%