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

Sebastian Gasche, Christian Kallies, Andreas Himmel, Rolf Findeisen

TL;DR

The paper tackles the challenge of achieving accurate, computationally tractable energy-aware multicopter modeling for control and planning. It proposes a modular grey-box framework that couples 6-DOF multicopter dynamics with a component-based ECM built from a Thevenin Li-ion battery, DC-DC ESC, and simplified BLDC motor models, complemented by camera and LiDAR sensor constraints. The ECM is formulated as a nonlinear state-space model and a discrete-time LPV variant, enabling online control and planning; a hovering-based linearization supports MPC/MILP-style planning in swarms. Validation against flight data demonstrates SOC estimation errors under 2% and reliable energy consumption predictions, while the framework supports energy-aware path planning in heterogeneous UAS swarms for surveillance and search-and-rescue in cluttered and dynamic environments.

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.

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

TL;DR

The paper tackles the challenge of achieving accurate, computationally tractable energy-aware multicopter modeling for control and planning. It proposes a modular grey-box framework that couples 6-DOF multicopter dynamics with a component-based ECM built from a Thevenin Li-ion battery, DC-DC ESC, and simplified BLDC motor models, complemented by camera and LiDAR sensor constraints. The ECM is formulated as a nonlinear state-space model and a discrete-time LPV variant, enabling online control and planning; a hovering-based linearization supports MPC/MILP-style planning in swarms. Validation against flight data demonstrates SOC estimation errors under 2% and reliable energy consumption predictions, while the framework supports energy-aware path planning in heterogeneous UAS swarms for surveillance and search-and-rescue in cluttered and dynamic environments.

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.

Paper Structure

This paper contains 28 sections, 78 equations, 14 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Unmanned Aerial Vehicle Modeling
  3. Multicopter Kinematics
  4. Multicopter Dynamics
  5. Multicopter Models
  6. Adapting For Specific Multicopter Configurations
  7. Vehicle Capabilities
  8. Energy Consumption Modeling
  9. Lithium-Ion Battery
  10. Electric Speed Controller
  11. Brushless Direct Current Motor with a fixed Rotor
  12. Combined Energy Consumption Model
  13. Power Train Capabilities
  14. Sensor and Communication Models
  15. Camera Model
  16. ...and 13 more sections

Figures (14)

  • Figure 1: Frames of reference (black: inertial frame, red: body-fixed frame); Forces and torques acting on the body's center of mass (blue)
  • Figure 2: Aerodynamic forces and torques of a quadcopter in X-configuration
  • Figure 3: Common power train of an electric-propelled uav
  • Figure 4: Simplified battery circuit & Thevenin model
  • Figure 5: Generalized discharge curve of a LiPo cell (black) Florea(2020), linear approximation (blue), and lpv approximations (red, magenta, green)
  • ...and 9 more figures

Theorems & Definitions (9)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Remark 5
  • Remark 6
  • Remark 7
  • Remark 8
  • Remark 9