On-Demand Grocery Delivery from Multiple Local Stores with Autonomous Robots

Conference paper (2021)

Authors

M. Kronmüller Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Andres Fielbaum Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 M. Kronmüller, Andres Fielbaum, J. Alonso-Mora
DOI
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https://doi.org/10.1109/MRS50823.2021.9620599
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

The advances in the area of autonomous delivery robots combined with customers' desire for fast delivery, bare potential for same-day delivery operations, specifically with small time windows between ordering and delivery. Most same-day deliveries are operated using a single depot and with vehicles' routes planned and fixed when leaving the depot. In this paper, we relax these two assumptions and focus on on-demand grocery delivery using a fleet of autonomous vehicles or robots. The problem features the opportunity to pick up goods at multiple local stores or depots, for example, supermarkets within the city, and allows robots to perform depot returns prior to being empty, if beneficial. This allows for more agile planning and on average shorter distance to the next depot. We propose a novel dynamic method for the same-day delivery problem, where we aim to deliver orders as fast as possible, minimally within the same day. In each time step (every few seconds or minutes) the following is executed: For each order potential pick-up locations are identified and feasible trips, i.e., sequences to pick up goods and deliver orders, are calculated. To assign trips to robots an integer-linear program is solved. We simulate one day of service in a city under different conditions with up to 30 autonomous robots, 30 depots and 10,500 orders. Results underpin the advantages of the proposed method and show its versatility with respect to different situations.