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GP-AE-1V1F9LYAerospaceOpen for request

UAV terrain-aware path-planning simulator

A flight-planning simulator for comparing terrain-aware UAV routes under elevation, obstacle, geofence, wind, vehicle, and energy constraints.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • PostGIS
  • Python
  • GeoPandas
  • CesiumJS
  • Docker

Project definition

Problem statement

A geometrically short UAV route may cross terrain, violate a geofence, require unsafe climb rates, or consume more energy after wind and altitude changes are considered.

The engineering problem is to represent the mission space and vehicle limits, compare planning methods fairly, and verify every planned segment without controlling a real aircraft.

Project objectives

  • Prepare terrain, obstacle, geofence, start, goal, and optional waypoint data for selected study areas.
  • Model UAV speed, altitude, climb, descent, turn, endurance, payload, and simplified wind effects.
  • Compare selected grid, graph, and sampling-based terrain-aware planning methods.
  • Check terrain clearance, geofence, motion, energy, and mission-reserve constraints.
  • Measure route length, flight time, energy, clearance, success, and computation time.

System design

System modules

01

Mission map

Defines terrain, obstacles, restricted polygons, corridors, start and goal points, and waypoint tasks.

02

Vehicle model

Stores flight envelope, payload, speed, climb, turn, energy, reserve, and simplified wind-response parameters.

03

Planner workbench

Runs selected A*, Theta*, RRT*, or graph planners under fixed grids, budgets, costs, and seeds.

04

Path verifier

Samples each route segment and checks terrain clearance, geofences, motion limits, energy, and reserve.

05

Flight visualiser

Shows 2D and 3D routes, elevation profiles, violations, energy use, and planner comparisons.

Methodology

System workflow

  1. 01
    Load mission

    The student selects a prepared terrain area, vehicle, start, goal, and constraint set.

  2. 02
    Build planning space

    Terrain, obstacle, and geofence data are aligned to the chosen resolution and safety margins.

  3. 03
    Run planners

    Selected methods solve the same mission with recorded parameters and repeated seeds.

  4. 04
    Verify routes

    Every candidate is checked for clearance, motion, wind, energy, and reserve constraints.

  5. 05
    Compare scenarios

    The visualiser compares planners and reruns the mission under changed wind, payload, or geofence inputs.

Demonstration scenario

A simulated survey UAV must cross hilly terrain while avoiding a restricted polygon. A shortest-distance planner and an energy-aware planner generate different routes. The 3D view compares terrain clearance, climb profile, wind-adjusted energy, reserve, and computation time.

Engineering

Technical architecture

Web application
Nuxt, Vue, and CesiumJS for mission setup, terrain display, path playback, profiles, and comparisons.
Planning API
FastAPI for spatial inputs, planning jobs, verification, scenarios, metrics, and exports.
Spatial data layer
PostgreSQL and PostGIS for terrain metadata, zones, routes, samples, scenarios, and results.
Planning engine
Python and GeoPandas for terrain costs, graph and sampling planners, smoothing, and constraint checks.
Reproducibility
Versioned maps, vehicle data, resolution, safety margins, planner budgets, seeds, and regression missions.

Testing

Evaluation

Evaluation measures

  • Valid-route success rate across prepared missions
  • Minimum terrain and obstacle clearance
  • Route length, estimated time, energy use, and reserve margin
  • Geofence, climb, turn, and vehicle-constraint satisfaction
  • Planning time and route variability across methods and seeds
  • Robustness to terrain resolution, wind, payload, and map uncertainty

System boundaries

  • The system is a simulator and does not connect to, command, or upload missions to a UAV.
  • Vehicle, propulsion, atmosphere, wind, terrain, and energy behaviour use documented simplified models.
  • Prepared geofences are scenario inputs and not an authoritative source of current airspace restrictions.
  • Real flight planning requires current regulations, permissions, surveys, certified systems, and qualified operators.

Included

  1. 01Mission, vehicle, terrain, and geofence interface
  2. 02Route-planning, energy, wind, and verification modules
  3. 032D and 3D path, clearance, energy, and comparison views
  4. 04Prepared terrain, mission scenarios, planners, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, flight-planning diagrams, simulation results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-AE-1V1F9LY
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.