Private Bee 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

• Aircraft parametrical configuration
  

File:TMO02.pdf File:Rapport finalv2.pdf

• Aircraft takeoff
  

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.

Key Features

3D collaborative GPS system. Advanced obstacle avoidance capabilities, considering both static and dynamic objects. Integration of meteorological constraints into the flight model. Minimization of fuel consumption and flight time. Methodology The project's methodology included:

Physical modeling, utilizing control inputs for thrust and angular velocity to define state equations. A cost function that combined flight time and fuel consumption for optimization. Comprehensive constraints, covering end-to-end states, control bounds, and obstacle avoidance. Optimization Techniques We explored a range of optimization techniques, including Generalized LQR, Approximate Dynamic Programming, Finite Elements, and Pseudospectral methods. Ultimately, the Legendre Pseudospectral method was selected for resolution due to its proven convergence.

Resolution To solve the nonlinear optimization problem, we employed the Legendre Pseudospectral method, transforming it into a finite-dimensional problem using polynomial basis functions.

Challenges The project was not without its challenges, particularly in managing complex flight parameter constraints and ensuring real-time collaboration for efficient flight management.

Applications While initially designed for use in flying ambulance services, the insights and technology developed in this project hold broader applications in the field of aerial navigation. These extend to drone routing, search and rescue operations, and beyond.

This summary underscores the project's alignment with existing optimization concerns in supply chain management and various industrial applications, especially in navigating intricate constraint environments.