使用变分自编码器学习用于最后一英里配送的安全监控自主机器人的有效搜索空间
Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery
AI总结：
本文针对最后一英里配送中安全监控约束下的自主机器人调度问题，评估并改进 COIL，使其在所有问题变体中找到有效解，并在学习潜在表示后比遗传算法优化快 10%。
摘要：
使用自主机器人向客户配送货物是提供可靠且可持续服务的一种令人振奋的新方式。然而，在现实世界中，出于安全原因，自主机器人仍需要人工监督。我们解决优化自主机器人时序以最大化配送量的现实问题，同时确保绝不会有过多机器人同时运行，以便能够对其进行安全监控。我们评估了近期的一种混合机器学习—优化方法 COIL（在学习到的潜在空间中进行约束优化）的使用，并为探索该问题的不同变体将其与基线遗传算法进行比较。我们还研究了用于提升 COIL 速度和效率的新方法。我们表明，在所有测试的问题变体中，只有 COIL 能够找到使适当数量机器人同时运行的有效解。我们还表明，当 COIL 学会其潜在表示后，它的优化速度可比 GA 快 10%，这使其成为机器人每日重新优化的良好选择；在此过程中，每天的配送请求会被分配给机器人，同时维持同时运行机器人的安全数量。
英文摘要：
The use of autonomous robots for delivery of goods to customers is an exciting new way to provide a reliable and sustainable service. However, in the real world, autonomous robots still require human supervision for safety reasons. We tackle the realworld problem of optimizing autonomous robot timings to maximize deliveries, while ensuring that there are never too many robots running simultaneously so that they can be monitored safely. We assess the use of a recent hybrid machine-learningoptimization approach COIL (constrained optimization in learned latent space) and compare it with a baseline genetic algorithm for the purposes of exploring variations of this problem. We also investigate new methods for improving the speed and efficiency of COIL. We show that only COIL can find valid solutions where appropriate numbers of robots run simultaneously for all problem variations tested. We also show that when COIL has learned its latent representation, it can optimize 10% faster than the GA, making it a good choice for daily re-optimization of robots where delivery requests for each day are allocated to robots while maintaining safe numbers of robots running at once.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
