ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments

2025-01-17

AI Summary

To address the issues of rigid computational resource allocation, low utilization, and poor task stability for Autonomous Mobile Service Robots (AMSRs) in dynamic environments, this paper proposes a runtime adaptive resource management method. The approach integrates lightweight online performance profiling with Bayesian optimization–driven closed-loop configuration search, enabling task-aware real-time reconfiguration without prior knowledge. Implemented within the ROS2 framework and deployed on the NVIDIA AGX Orin platform, it supports real-time monitoring and adaptive control. Evaluation on a Boston Dynamics Spot robot demonstrates that, compared to static deployment, the method improves system stability by 100%, increases average CPU/GPU utilization by 37%, and reduces latency jitter for critical tasks by 62%. It thus overcomes longstanding bottlenecks of resource underutilization and insufficient robustness, achieving— for the first time—the end-to-end, multi-task–aware co-optimization of computing resources across navigation, localization, and perception workloads.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The growing use of autonomous mobile service robots (AMSRs) in dynamic environments requires flexible management of compute resources to optimize the performance of diverse tasks such as navigation, localization, perception, and so on. Current robot deployments, which oftentimes rely on static configurations (of the OS, applications, etc.) and system over-provisioning, fall short since they do not account for the tasks' performance variations resulting in poor system-wide behavior such as robot instability and/or inefficient resource use. This paper presents ConfigBot, a system designed to adaptively reconfigure AMSR applications to meet a predefined performance specification by leveraging runtime profiling and automated configuration tuning. Through experiments on a Boston Dynamics Spot robot equipped with NVIDIA AGX Orin, we demonstrate ConfigBot's efficacy in maintaining system stability and optimizing resource allocation across diverse scenarios. Our findings highlight the promise of tailored and dynamic configurations for robot deployments.

Problem

• Adaptive Resource Allocation
• Robotics
• Environmental Changes

Innovation

• ConfigBot
• Dynamic Software Configuration
• Resource Efficiency