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.

翻译：将自动驾驶机器人用于向客户配送货物是一种令人兴奋的新型可靠且可持续的服务模式。然而在现实世界中，出于安全考虑，自动驾驶机器人仍需人类监督。我们解决了优化自动驾驶机器人时序以最大化配送量的实际难题，同时确保同时运行的机器人数量始终不超过安全监控阈值。我们评估了最新混合机器学习-优化方法COIL（学习隐空间中的约束优化）在探索该问题变体时的性能，并与基准遗传算法进行了对比。我们还研究了提升COIL速度与效率的新方法。实验表明，在所有测试的问题变体中，只有COIL能够找到同时运行适当数量机器人的有效解。此外，当COIL完成隐空间表征学习后，其优化速度比遗传算法快10%，使其成为每日机器人再优化的理想选择——在将每日配送请求分配给机器人的同时，保持同时运行的机器人数量处于安全范围。