Design and Implement an Enhanced Simulator for Autonomous Delivery Robot

TL;DR

This paper presents the design and implementation of an open-source simulator for autonomous delivery robots, enabling safer, cost-effective testing of navigation, cooperation, and reinforcement learning in urban scenarios.

Contribution

It introduces a novel open-source simulator based on ZebraT, with detailed development steps and multiple applications for advancing autonomous delivery research.

Findings

Successful simulation of urban navigation scenarios

Demonstrated cooperation between autonomous vehicle and robot

Reinforcement learning applied for task training

Abstract

As autonomous driving technology is getting more and more mature today, autonomous delivery companies like Starship, Marble, and Nuro has been making progress in the tests of their autonomous delivery robots. While simulations and simulators are very important for the final product landing of the autonomous delivery robots since the autonomous delivery robots need to navigate on the sidewalk, campus, and other urban scenarios, where the simulations can avoid real damage to pedestrians and properties in the real world caused by any algorithm failures and programming errors and thus accelerate the whole developing procedure and cut down the cost. In this case, this study proposes an open-source simulator based on our autonomous delivery robot ZebraT to accelerate the research on autonomous delivery. The simulator developing procedure is illustrated step by step. What is more, the applications on the simulator that we are working on are also introduced, which includes autonomous navigation in the simulated urban environment, cooperation between an autonomous vehicle and an autonomous delivery robot, and reinforcement learning practice on the task training in the simulator. We have published the proposed simulator in Github.