This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of obstacles, goals, and an initial state, they can be asked to guess what a new path would look like. We performed experiments on a 64 x 64 pixel grid that generated a training set by using randomly positioned obstacles and goals. The heuristic search algorithm A* was used to create training paths due to its significant presence in the literature and ease of implementation. We experimented with architectural elements and hyperparameters, converging to a pix2pix-based architecture in which the generator was trained to generate plausible paths given obstacles and two points. A discriminator tried to determine whether these maps were real or fake. Additionally, we defined a qualitative path-generation "success rate" metric derived from the Frechet inception distance (FID) and optimized our architecture's parameters, ultimately reaching a 74% success rate on the validation set. Furthermore, we discuss the applicability of this approach to safety-critical settings, concluding that this architecture's performance and reliability are insufficient to offset the downsides of a black-box approach to path generation.

Assessing an Image-to-Image Approach to Global Path Planning for a Planetary Exploration / Daddi, Guglielmo; Notaristefano, Nicolaus; Stesina, Fabrizio; Corpino, Sabrina. - In: AEROSPACE. - ISSN 2226-4310. - ELETTRONICO. - 9:11(2022), p. 721. [10.3390/aerospace9110721]

Assessing an Image-to-Image Approach to Global Path Planning for a Planetary Exploration

GUGLIELMO DADDI;Nicolaus Notaristefano;Fabrizio Stesina;Sabrina Corpino
2022

Abstract

This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of obstacles, goals, and an initial state, they can be asked to guess what a new path would look like. We performed experiments on a 64 x 64 pixel grid that generated a training set by using randomly positioned obstacles and goals. The heuristic search algorithm A* was used to create training paths due to its significant presence in the literature and ease of implementation. We experimented with architectural elements and hyperparameters, converging to a pix2pix-based architecture in which the generator was trained to generate plausible paths given obstacles and two points. A discriminator tried to determine whether these maps were real or fake. Additionally, we defined a qualitative path-generation "success rate" metric derived from the Frechet inception distance (FID) and optimized our architecture's parameters, ultimately reaching a 74% success rate on the validation set. Furthermore, we discuss the applicability of this approach to safety-critical settings, concluding that this architecture's performance and reliability are insufficient to offset the downsides of a black-box approach to path generation.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2983243