Difference between revisions of "Private Bee 3D GPS"
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Presentation of work done for a 3D GPS | Presentation of work done for a 3D GPS | ||
| + | Project is to calculate an optimized flight path with IA. | ||
| + | |||
| + | == Path Flight Optimization == | ||
| + | |||
| + | Presentation : | ||
| + | [[File:BELMANT-CLERC-DIAPORAMA.pdf]] | ||
| + | |||
| + | Final report : | ||
| + | [[File:Insa minibee gps final.pdf]] | ||
| + | |||
| + | |||
| + | Objectives | ||
| + | Develop a Hybrid VTOL (Vertical Take-Off and Landing) aircraft for flying ambulance applications. | ||
| + | Navigate optimally in a 3D environment from point S to F. | ||
| + | Key Features | ||
| + | 3D collaborative GPS system. | ||
| + | Obstacle avoidance, accounting for static and dynamic objects. | ||
| + | Meteorological constraints integrated into the flight model. | ||
| + | Fuel consumption and flight time minimization. | ||
| + | Methodology | ||
| + | Physical Modeling: Uses control inputs for thrust and angular velocity to define state equations | ||
| + | Cost Function: Linear combination of flight time and fuel consumption for optimization. | ||
| + | Constraints: Involves end-to-end states, control bounds, and obstacle avoidance. | ||
| + | Optimization Techniques | ||
| + | Generalized LQR, Approximate Dynamic Programming, Finite Elements, and Pseudospectral methods reviewed. | ||
| + | Legendre Pseudospectral method chosen for resolution due to proven convergence. | ||
| + | Resolution | ||
| + | Transformation to Finite Dimensional Problem using polynomial basis. | ||
| + | Legendre Pseudospectral method applied for solving nonlinear optimization problem. | ||
| + | Challenges | ||
| + | Complex constraints on flight parameters. | ||
| + | Ensuring real-time collaboration and efficient flight management. | ||
| + | Applications | ||
| + | Use case in flying ambulance service. | ||
| + | Applicable to broader range of aerial navigation tasks, such as drone routing, search and rescue. | ||
| + | This summary aligns well with existing optimization concerns in supply chain management and other industrial applications, particularly in navigating complex constraint environments. | ||
| + | |||
| + | == 3D GPS == | ||
| + | [[File:Présentation 14 03 2019.pdf]] | ||
| Line 8: | Line 47: | ||
* Aircraft parametrical configuration<BR> | * Aircraft parametrical configuration<BR> | ||
[[File:TMO02.pdf]] | [[File:TMO02.pdf]] | ||
| + | [[File:Rapport finalv2.pdf]] | ||
* Aircraft takeoff<BR> | * Aircraft takeoff<BR> | ||
[[File:Rapport Bee-plane.pdf]] | [[File:Rapport Bee-plane.pdf]] | ||
| + | |||
| + | == Presentation of 3D GPS Flight Optimization Project == | ||
| + | |||
| + | In our relentless pursuit of cutting-edge aviation technology, we present the remarkable work accomplished in the realm of 3D GPS flight optimization, specifically designed to calculate an optimized flight path using artificial intelligence (AI). | ||
| + | |||
| + | === Path Flight Optimization === | ||
| + | Presentation: [[File:BELMANT-CLERC-DIAPORAMA.pdf]] | ||
| + | Final Report: [[File:Insa minibee gps final.pdf]] | ||
| + | |||
| + | Objectives | ||
| + | Our primary objective was to develop a Hybrid VTOL (Vertical Take-Off and Landing) aircraft with a specific focus on its application as a flying ambulance. This project aimed to navigate optimally in a complex 3D environment from point S to F. | ||
| + | |||
| + | |||
| + | |||
| + | [[Category:Private Bee 3D GPS]] | ||
Latest revision as of 19:14, 22 December 2024
Presentation of work done for a 3D GPS
Project is to calculate an optimized flight path with IA.
Path Flight Optimization
Presentation : File:BELMANT-CLERC-DIAPORAMA.pdf
Final report : File:Insa minibee gps final.pdf
Objectives
Develop a Hybrid VTOL (Vertical Take-Off and Landing) aircraft for flying ambulance applications.
Navigate optimally in a 3D environment from point S to F.
Key Features
3D collaborative GPS system.
Obstacle avoidance, accounting for static and dynamic objects.
Meteorological constraints integrated into the flight model.
Fuel consumption and flight time minimization.
Methodology
Physical Modeling: Uses control inputs for thrust and angular velocity to define state equations
Cost Function: Linear combination of flight time and fuel consumption for optimization.
Constraints: Involves end-to-end states, control bounds, and obstacle avoidance.
Optimization Techniques
Generalized LQR, Approximate Dynamic Programming, Finite Elements, and Pseudospectral methods reviewed.
Legendre Pseudospectral method chosen for resolution due to proven convergence.
Resolution
Transformation to Finite Dimensional Problem using polynomial basis.
Legendre Pseudospectral method applied for solving nonlinear optimization problem.
Challenges
Complex constraints on flight parameters.
Ensuring real-time collaboration and efficient flight management.
Applications
Use case in flying ambulance service.
Applicable to broader range of aerial navigation tasks, such as drone routing, search and rescue.
This summary aligns well with existing optimization concerns in supply chain management and other industrial applications, particularly in navigating complex constraint environments.
3D GPS
File:Présentation 14 03 2019.pdf
- Aircraft parametrical configuration
File:TMO02.pdf File:Rapport finalv2.pdf
- Aircraft takeoff
Presentation of 3D GPS Flight Optimization Project
In our relentless pursuit of cutting-edge aviation technology, we present the remarkable work accomplished in the realm of 3D GPS flight optimization, specifically designed to calculate an optimized flight path using artificial intelligence (AI).
Path Flight Optimization
Presentation: File:BELMANT-CLERC-DIAPORAMA.pdf Final Report: File:Insa minibee gps final.pdf
Objectives Our primary objective was to develop a Hybrid VTOL (Vertical Take-Off and Landing) aircraft with a specific focus on its application as a flying ambulance. This project aimed to navigate optimally in a complex 3D environment from point S to F.