Computational geometry: an introduction
Computational geometry: an introduction
New methods for computing visibility graphs
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Computational Geometry: Theory and Applications
The guilty net for the traveling salesman problem
Computers and Operations Research - Special issue on neural networks and operations research
New insertion and postoptimization procedures for the traveling salesman problem
Operations Research
Computers and Operations Research
An Optimal Algorithm for Euclidean Shortest Paths in the Plane
SIAM Journal on Computing
Chained Lin-Kernighan for Large Traveling Salesman Problems
INFORMS Journal on Computing
Planning Tours of Robotic Arms among Partitioned Goals
International Journal of Robotics Research
New lower bound techniques for robot motion planning problems
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
A Sensor Placement Algorithm for a Mobile Robot Inspection Planning
Journal of Intelligent and Robotic Systems
Self-organizing map with input data represented as graph
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Information Sciences: an International Journal
Self-organizing map for the multi-goal path planning with polygonal goals
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Inspection planning in the polygonal domain by Self-Organizing Map
Applied Soft Computing
Visiting convex regions in a polygonal map
Robotics and Autonomous Systems
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An application of the self-organizing map (SOM) to the Traveling Salesman Problem (TSP) has been reported by many researchers, however these approaches are mainly focused on the Euclidean TSP variant. We consider the TSP as a problem formulation for the multi-goal path planning problem in which paths among obstacles have to be found. We apply a simple approximation of the shortest path that seems to be suitable for the SOM adaptation procedure. The approximation is based on a geometrical interpretation of SOM, where weights of neurons represent nodes that are placed in the polygonal domain. The approximation is verified in a set of real problems and experimental results show feasibility of the proposed approach for the SOM based solution of the non-Euclidean TSP.