Energy-Optimal Spatial Iterative Learning within a Virtual Tube

• URL: http://arxiv.org/abs/2606.31487v1
• Date: Tue, 30 Jun 2026 11:01:06 GMT
• Title: Energy-Optimal Spatial Iterative Learning within a Virtual Tube
• Abstract summary: This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption.<n>In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark.
• Score: 11.095806833251403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on accurate system models and computationally expensive optimization procedures. This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption. Without requiring explicit models of UAV dynamics or energy consumption, the proposed method improves energy efficiency while maintaining a low computational cost. The per-iteration computational complexity is O(n), where n denotes the number of path points. In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark. Simulation results and real-world flight experiments across multiple UAV platforms validate the effectiveness, computational efficiency, and practical applicability of the proposed approach.

Related papers

• Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation [2.435798495038311]
  Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.<n>Traditional energy-aware schedulers struggle with unpredictable workloads due to their reliance on static execution thresholds.<n>We propose two novel, hardware-agnostic dynamic scheduling strategies treating applications as a "black box"
  arXiv  Detail & Related papers  (2026-06-23T09:27:59Z)
• Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning [9.044455355747482]
  This paper proposes an imitation learning-based framework to approximate mixed-integer Economic Model Predictive Control (EMPC) for microgrid energy management.<n>The proposed method trains a neural network to imitate expert EMPC control actions from offline trajectories, enabling fast, real-time decision making without solving optimization problems online.
  arXiv  Detail & Related papers  (2025-10-22T21:39:18Z)
• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving [64.15371139980802]
  Large Language Models (LLMs) have recently advanced the field of Automated Theorem Proving (ATP)<n>We show that different test-time scaling strategies for ATP models introduce significant computational overhead for inference.<n>We propose two complementary methods that can be integrated into a unified EconRL pipeline for amplified benefits.
  arXiv  Detail & Related papers  (2025-09-16T03:00:13Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.<n>In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.<n>The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method [9.298950359150092]
  This paper introduces a novel energy-based self-adjustable learning rate optimization method designed for unconstrained optimization problems. It incorporates an auxiliary variable $r to facilitate efficient energy approximation without backtracking while adhering to the unconditional energy dissipation law. Notably, VAV demonstrates superior stability with larger learning rates and achieves faster convergence in the early stage of the training process.
  arXiv  Detail & Related papers  (2024-11-10T19:39:40Z)
• Efficient Data-Driven MPC for Demand Response of Commercial Buildings [0.0]
  We propose a data-driven and mixed-integer bidding strategy for energy management in small commercial buildings. We consider rooftop unit heating, air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. We apply our approach in several demand response (DR) settings, including a time-of-use, and a critical rebate bidding.
  arXiv  Detail & Related papers  (2024-01-28T20:01:44Z)
• Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning [9.936452412191326]
  This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results in model-free DRL-based methods for power systems, but model-free methods suffer from poor sample efficiency and training time. We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model is utilized with the policy learning framework.
  arXiv  Detail & Related papers  (2022-12-06T02:50:53Z)
• Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes [57.70237375696411]
  The paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions.
  arXiv  Detail & Related papers  (2022-08-30T09:27:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.