A genetic algorithm for robust motion planning

Gallardo, Domingo; Colomina, Otto; Flórez, Francisco; Aldeguer, Ramón Rizo

doi:10.1007/3-540-64574-8_397

Cited by 23 publications

(10 citation statements)

References 3 publications

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“…Fitness is assessed via a 2-step process that uses three fitness functions: ) / 0 . 1 ( * DistToGoal t (8) If no previous chromosome has reached the goal, fitness is evaluated as follows to give higher fitness values to those that came closest to reaching the goal: where t is the number of successful moves made for this chromosome and DistToGoal is the Euclidean distance between the last valid position and the goal.…”

Section: Dual Goal Approachmentioning

confidence: 99%

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

Watkins

2013

JCP

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Previous research suggests that genetic algorithms (GAs) offer a promising solution to path planning for mobile robots. We examine six simple GAs used in prior studies, comparing them to a new node sequence approach that includes a two-step fitness function. Through a series of repeated trials using a simple 16x16 grid, a 100x100 grid, a 600x600 Mars landscape, and a complex maze-like environment, we compare the chromosome structures and fitness functions of these seven methods. The results of our empirical testing indicate that the proposed dual goal approach, which uses a fixed length chromosome structure, outperformed both monotonic and other node sequence approaches, consistently finding a feasible path in even the most challenging of these environments.

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Section: Dual Goal Approachmentioning

confidence: 99%

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

Watkins

2013

JCP

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“…To compute a shortest path for robot navigation, researchers model it as an optimization problem. One of the most commonly used search technique is Genetic Algorithm [10].Many researchers have applied GAs on path planning for 978-1-4799-4020-2/14/$31.00 ©2014 IEEE [13]. However most of their methods implement of 2-dimensional plane and one robot was used.…”

Section: Introductionmentioning

confidence: 99%

Path planning of underwater swarm robots using genetic algorithm

Vicmudo

Dadios

Vicerra

2014

2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and M

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Path planning is one of the most exciting challenges in building autonomous swarm robots. It consists on finding a route from the origin of the robot to its target destination. It becomes more difficult when some obstacles are added to the environment. This paper consists of multiple obstacles: the robots and their possible path. This paper will present the path planning of underwater swarm robot based on genetic algorithm. Swarm robots will determine the position of pre-defined object and genetic algorithm generates shortest path for each robot to reach the object without collision to one another. The xyz coordinates of possible path of robot are randomly generated and they are encoded into chromosome and their fitness is defined by the summation of their displacement using Euclidian distance formula for 3-dimensional plane. The simulation results demonstrated that proposed algorithm is able to plan safe collision free paths for swarm robots. It also shown that using more population, the optimum path will be obtained. The implementation of genetic algorithm is done using computer simulation and explains the process in section two of this paper.

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“…Recently various researches of mobile-robots path planning using different path primitives and planning schemes in a given static environments has been presented in the literature, genetic algorithm in particular [1]- [6], [11]- [18]. In general, path planning in robotics can be formulated as nonlinear constrained optimization problems in configuration or Cartesian space.…”

Section: Introductionmentioning

confidence: 99%

Collision-Free Composite ?3Splines Generation for Nonholonomic Mobile Robots by Parallel Variable-Length Genetic Algorithm

Wei

Liu

2008

2008 International Conference on Computational Intelligence for Modelling Control &Amp; Automation

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A genetic algorithm for robust motion planning

Cited by 23 publications

References 3 publications

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

Path planning of underwater swarm robots using genetic algorithm

Collision-Free Composite ?3Splines Generation for Nonholonomic Mobile Robots by Parallel Variable-Length Genetic Algorithm

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A genetic algorithm for robust motion planning

Cited by 23 publications

References 3 publications

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

Path planning of underwater swarm robots using genetic algorithm

Collision-Free Composite ?3&#150;Splines Generation for Nonholonomic Mobile Robots by Parallel Variable-Length Genetic Algorithm

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Product

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Collision-Free Composite ?3Splines Generation for Nonholonomic Mobile Robots by Parallel Variable-Length Genetic Algorithm