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Extending QGroundControl for Automated Mission Planning of UAVs

Cristian Ramirez-Atencia, David Camacho

TL;DR

The paper tackles the need for automated mission planning and replanning in UAV swarms by extending QGroundControl with a graphical Mission Designer, an automated mission planner, and a Decision Support System (DSS) for ranking and filtering plans. It formalizes the planning problem as a multi-objective optimization (MCMPP) and uses knee-point MOEA searches complemented by VIKOR-based ranking to present operator-guided choices, all communicating through a Thrift JSON interface. The framework supports end-to-end workflow from mission design to execution and dynamic replanning, enabling simulated testing of planning algorithms and operator decision-making. The demonstrated use cases—including handling unresolvable missions—highlight practical impact for safer, more efficient autonomous UAV operations and set directions for operator training and augmented reality enhancements.

Abstract

Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.

Extending QGroundControl for Automated Mission Planning of UAVs

TL;DR

The paper tackles the need for automated mission planning and replanning in UAV swarms by extending QGroundControl with a graphical Mission Designer, an automated mission planner, and a Decision Support System (DSS) for ranking and filtering plans. It formalizes the planning problem as a multi-objective optimization (MCMPP) and uses knee-point MOEA searches complemented by VIKOR-based ranking to present operator-guided choices, all communicating through a Thrift JSON interface. The framework supports end-to-end workflow from mission design to execution and dynamic replanning, enabling simulated testing of planning algorithms and operator decision-making. The demonstrated use cases—including handling unresolvable missions—highlight practical impact for safer, more efficient autonomous UAV operations and set directions for operator training and augmented reality enhancements.

Abstract

Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.
Paper Structure (20 sections, 19 figures, 1 table)

This paper contains 20 sections, 19 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. Mission Designer
  4. Creating new missions
  5. Adding a new UAV
  6. Adding a new GCS
  7. Adding an objective or task
  8. Adding a NFZ
  9. Adding objective dependencies
  10. Automated Mission Planner and DSS
  11. Operator Profile
  12. Mission Planning
  13. DSS ranking and filtering
  14. Plan Visualization
  15. Mission Execution and Replanning
  16. ...and 5 more sections

Figures (19)

  • Figure S1: Architecture of the framework extended from QGroundControl, including mission (re)planning and decision support.
  • Figure S2: Mission Designer in the QGroundControl for adding new elements.
  • Figure S3: Range of GCSs represented as translucent orange circles.
  • Figure S4: Architecture of the Test Bed Interface for Mission Planning and Decision Support.
  • Figure S5: Mission Plans.
  • ...and 14 more figures