一句话：提出 Swarmbug，通过因果贡献度自动诊断并修复群体机器人由配置错误引发的行为缺陷。
群体机器人算法依赖大量可配置参数，且配置空间庞大、算法复杂并与机器人设置和性能耦合，使配置缺陷难以调试和修复。本文提出 Swarmbug，一种自动诊断并修复由错误配置导致的异常行为的群体调试系统。其核心是 Degree of Causal Contribution（Dcc）概念，通过行为因果分析抽象环境配置（如障碍物）对群体中无人机的影响；系统能自动生成、验证并排序修复方案。作者在四种不同的群体算法上评估，成功修复四个配置缺陷，表明方法通用有效，并通过真实无人机实验验证修复效果。
Swarm robotics collectively solve problems that are challenging for individual robots, from environmental monitoring to entertainment. The algorithms enabling swarms allow individual robots of the swarm to plan, share, and coordinate their trajectories and tasks to achieve a common goal. Such algorithms rely on a large number of configurable parameters that can be tailored to target particular scenarios. This large configuration space, the complexity of the algorithms, and the dependencies with the robots’ setup and performance make debugging and fixing swarms configuration bugs extremely challenging. This paper proposes Swarmbug, a swarm debugging system that automatically diagnoses and fixes buggy behaviors caused by misconfiguration. The essence of Swarmbug is the novel concept called the degree of causal contribution (Dcc), which abstracts impacts of environment configurations (e.g., obstacles) to the drones in a swarm via behavior causal analysis. Swarmbug automatically generates, validates, and ranks fixes for configuration bugs. We evaluate Swarmbug on four diverse swarm algorithms. Swarmbug successfully fixes four configuration bugs in the evaluated algorithms, showing that it is generic and effective. We also conduct a real-world experiment with physical drones to show the Swarmbug’s fix is effective in the real-world.
DOI 原文 ·
@inproceedings{paperbot6376,
title = {Swarmbug: debugging configuration bugs in swarm robotics},
author = {Chijung Jung and Ali Ahad and Jinho Jung and Sebastian Elbaum and Yonghwi Kwon},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468601}
}