动态环境中自适应调整机器人资源分配，提升系统稳定性与效率。
ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments
- 基于运行时性能分析自动调优配置。
- 多实机测试验证系统稳定性和资源优化效果。
- 适合需要动态响应的智能机器人部署场景。
服务机器人在动态环境中的应用日益广泛，亟需灵活管理车载计算资源以优化导航、定位、感知等多样化任务的性能。当前机器人部署普遍依赖静态操作系统配置和系统过度预留，但无法适应资源使用的变化，导致机器人不稳定或资源利用低效。本文提出ConfigBot系统，通过运行时性能分析与自动化配置调优，实现对机器人应用的自适应重构，以满足预设性能要求。在多个真实机器人上进行实验，每个机器人运行不同的软件栈，具有多样化的性能需求且依赖具体上下文。结果表明，ConfigBot能有效维持系统稳定性并优化资源分配，凸显了自动化系统配置调优在机器人部署中的巨大潜力，尤其在应对动态变化时。
原文摘要 · Abstract (English)
The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation, localization, and perception. Current robot deployments often rely on static OS configurations and system over-provisioning. However, they are suboptimal because they do not account for variations in resource usage. This results in poor system-wide behavior such as robot instability or inefficient resource use. This paper presents ConifgBot, a novel system designed to adaptively reconfigure robot applications to meet a predefined performance specification by leveraging \emph{runtime profiling} and \emph{automated configuration tuning}. Through experiments on multiple real robots, each running a different stack with diverse performance requirements, which could be \emph{context}-dependent, we illustrate ConifgBot's efficacy in maintaining system stability and optimizing resource allocation. Our findings highlight the promise of automatic system configuration tuning for robot deployments, including adaptation to dynamic changes.
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