The present letter tackles the problem of planning a collision-free path in a known environment from a general point of view. We address the problem by using an unsupervised learning algorithm to generate a sparse graph representing the topological structure of the environment and use it for planning paths in 3D spaces. We propose GNGraph, an integrated solution combining the Growing Neural Gas algorithm to generate the sparse graph, a stop criterion to guarantee the graph’s connectivity and a collision check to assess the edges and nodes validity. The proposed solution has been tested on simulated and real environment maps, and compared against a state-of-the-art graph planning algorithm among other global planning methods.

GNGraph: Self-Organizing Maps for Autonomous Aerial Vehicle Planning

Herrera-Alarcon E. P.
;
Satler M.;Vannucci M.;Avizzano C. A.
2022

Abstract

The present letter tackles the problem of planning a collision-free path in a known environment from a general point of view. We address the problem by using an unsupervised learning algorithm to generate a sparse graph representing the topological structure of the environment and use it for planning paths in 3D spaces. We propose GNGraph, an integrated solution combining the Growing Neural Gas algorithm to generate the sparse graph, a stop criterion to guarantee the graph’s connectivity and a collision check to assess the edges and nodes validity. The proposed solution has been tested on simulated and real environment maps, and compared against a state-of-the-art graph planning algorithm among other global planning methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11382/548511
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