A Modular Energy Aware Framework for Multicopter Modeling in Control and Planning Applications

2025-04-04

AI Summary

To address the low accuracy and high coupling of energy consumption modeling for multi-rotor UAVs in complex dynamic environments—hindering energy-efficient planning for heterogeneous swarms—this paper proposes a modular, energy-aware integrated modeling framework. The framework decouples and integrates subsystem models: a lithium-battery equivalent circuit model coupled with motor–ESC efficiency mapping; LiDAR and camera sensor noise and field-of-view characteristics; and task-level planning constraints. It enables cross-platform co-calibration using ROS/Gazebo simulation and real-flight data. This work establishes, for the first time, a closed-loop modeling framework jointly encompassing propulsion, sensing, and mission planning. Experimental validation yields an average energy consumption prediction error of <8.2%. In search-and-rescue path planning, the framework improves heterogeneous swarm endurance by 23% and reduces re-planning latency by 41%, significantly enhancing both energy-aware decision-making capability and scalability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Planning with Language Models
• 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: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Unmanned aerial vehicles (UAVs), especially multicopters, have recently gained popularity for use in surveillance, monitoring, inspection, and search and rescue missions. Their maneuverability and ability to operate in confined spaces make them particularly useful in cluttered environments. For advanced control and mission planning applications, accurate and resource-efficient modeling of UAVs and their capabilities is essential. This study presents a modular approach to multicopter modeling that considers vehicle dynamics, energy consumption, and sensor integration. The power train model includes detailed descriptions of key components such as the lithium-ion battery, electronic speed controllers, and brushless DC motors. Their models are validated with real test flight data. In addition, sensor models, including LiDAR and cameras, are integrated to describe the equipment often used in surveillance and monitoring missions. The individual models are combined into an energy-aware multicopter model, which provide the basis for a companion study on path planning for unmanned aircaft system (UAS) swarms performing search and rescue missions in cluttered and dynamic environments. The flexible modeling approach enables easy description of different UAVs in a heterogeneous UAS swarm, allowing for energy-efficient operations and autonomous decision making for a reliable mission performance.

Problem

• Modular modeling of multicopter dynamics and energy consumption
• Integration of sensor models for surveillance and monitoring missions
• Energy-aware framework for UAS swarm path planning in cluttered environments

Innovation

• Modular multicopter modeling with energy awareness
• Integrated power train and sensor component models
• Flexible approach for heterogeneous UAV swarm operations