Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation
By Miguel Angel Porta García, Oscar Montiel, Oscar Castillo, Roberto Sepúlveda, Patricia Melin. Applied Soft Computing, 2009
407 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/f060c99d5ccc893680df8bcfd2044cfef7d56768
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Michalis Mavrovouniotis
- Shengxiang Yang
- Changhe Li
- Amna Khan
- M. Simic
- Mohamed Elbanhawi
- Zulfiqar Habib
- Iram Noreen
In Computer Vision and Pattern Recognition
In Control and Systems Engineering
- Amna Khan
- M. Simic
- Shengxiang Yang
- Michalis Mavrovouniotis
- Mohamed Elbanhawi
- Zulfiqar Habib
- Changhe Li
- Iram Noreen
Citations in the corpus
- Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions (2016, background): “Evolutionary algorithms such as Particle Swarm Optimization (PSO) [17-19], Ant Colony Optimization (ACO) [20] and Genetic Algorithm (GA) [21] are suitable for multi-objective problems.”
- Sampling-Based Robot Motion Planning: A Review (2014, methodology): “Methods such as Fuzzy Logic Control [33]–[35], Neural Networks [36], Genetic Algorithms [37], [38], Ant ColonyOptimization [39] and SimulatedAnnealing [20] have all been applied in robot path planning.”
- A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, background): “Garcia et al. [237] uses an ACO to solve the problem of path planning for mobile robots with obstacle avoidance.”
- Building comprehensible customer churn prediction models with advanced rule induction techniques (2011, unknown): “…has been applied to a wide variety of problems (Dorigo & Stützle, 2004), such as the vehicle routing problem (Bullnheimer, Hartl, & Strauss, 1999; Garcia, Montiel, Castillo, Sepúlveda, & Melin, 2009; Wade & Salhi, 2004), scheduling (Color-ni, Dorigo, Maniezzo, & Trubian, 1994; Blum, 2005), and…”