Improving Path Planning Performance through Multimodal Generative Models with Local Critics

This paper presents a novel method for accelerating path planning tasks in unknown scenes with obstacles by utilizing Wasserstein Generative Adversarial Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of the free conditioned configuration space. Our proposed approach invo...

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Veröffentlicht in:	arXiv.org 2023-06
Hauptverfasser:	Jorge Ocampo Jimenez, Suleiman, Wael
Format:	Artikel
Sprache:	eng
Schlagworte:	Coders Collision avoidance Conditioning Configuration space path planning Convergence Datasets Generative adversarial networks Homology Source code
Online-Zugang:	Volltext
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