Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery

Author:

Bentley Peter12ORCID,Lim Soo Ling1ORCID,Arcaini Paolo3ORCID,Ishikawa Fuyuki3ORCID

Affiliation:

1. University College London, London, United Kingdom

2. Autodesk, London, United Kingdom

3. National Institute of Informatics, Tokyo, Japan

Publisher

ACM

Reference27 articles.

1. Autonomous delivery robots and their potential impacts on urban freight energy consumption and emissions

2. P. J. Bentley, S. L. Lim, A. Gaier, and L. Tran, "COIL: Constrained optimization in learned latent space---learning representations for valid solutions," in Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO) Companion, 2022, pp. 1870--1877.

3. P. J. Bentley, S. L. Lim, A. Gaier, and L. Tran, "Evolving through the looking glass: Learning improved search spaces with variational autoencoders," in Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN), 2022, pp. 371--384.

4. Scheduling last-mile deliveries with truck-based autonomous robots

5. Two-echelon urban deliveries using autonomous vehicles

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3. Investigating Multi- and Many-Objective Search for Stability-Aware Configuration of an Autonomous Delivery System;2023 30th Asia-Pacific Software Engineering Conference (APSEC);2023-12-04

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