Rethinking Energy Management for Autonomous Ground Robots on a Budget

2025-02-03

AI Summary

Autonomous ground robots (AGRs) operate under strict energy budgets, yet existing energy-efficiency optimization and task-scheduling strategies exhibit conflicting objectives: the former minimizes power consumption, while the latter assumes fixed energy costs to maximize performance. Method: This paper uncovers the coupled effects of computation frequency and locomotion speed on both energy consumption and task performance, and proposes the Budget-Driven Predictable Energy Consumption Controller (PECC) framework—the first to jointly optimize computational and locomotion resources under energy constraints. PECC integrates empirical hardware modeling, closed-loop feedback control, and joint optimization algorithms, validated via simulation and real-world experiments. Contribution/Results: On physical platforms, PECC achieves a 17% increase in execution speed and 95% energy utilization; in simulation, it attains a 31% speed improvement with 91% energy utilization—significantly enhancing task execution efficacy within prescribed energy budgets.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

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

Abstract

Autonomous Ground Robots (AGRs) face significant challenges due to limited energy reserve, which restricts their overall performance and availability. Prior research has focused separately on energy-efficient approaches and fleet management strategies for task allocation to extend operational time. A fleet-level scheduler, however, assumes a specific energy consumption during task allocation, requiring the AGR to fully utilize the energy for maximum performance, which contrasts with energy-efficient practices. This paper addresses this gap by investigating the combined impact of computing frequency and locomotion speed on energy consumption and performance. We analyze these variables through experiments on our prototype AGR, laying the foundation for an integrated approach that optimizes cyber-physical resources within the constraints of a specified energy budget. To tackle this challenge, we introduce PECC (Predictable Energy Consumption Controller), a framework designed to optimize computing frequency and locomotion speed to maximize performance while ensuring the system operates within the specified energy budget. We conducted extensive experiments with PECC using a real AGR and in simulations, comparing it to an energy-efficient baseline. Our results show that the AGR travels up to 17% faster than the baseline in real-world tests and up to 31% faster in simulations, while consuming 95% and 91% of the given energy budget, respectively. These results prove that PECC can effectively enhance AGR performance in scenarios where prioritizing the energy budget outweighs the need for energy efficiency.

Problem

• Optimize energy consumption in Autonomous Ground Robots
• Integrate computing frequency and locomotion speed
• Maximize performance within specified energy budget

Innovation

• Predictable Energy Consumption Controller
• Optimizes computing frequency
• Balances locomotion speed