gps-denied-desktop/docs/_metodology/01_research_phase.md

# 1. Research phase


## 1.1 **👨‍💻Developers**: Problem statement
 Discuss the problem and create in the `docs/00_problem` next files and folders:  
 - `problem_description.md`: Our problem to solve with the end result we want to achieve. 
 - `input_data`: Put to this folder all the necessary input data and expected results for the further tests. Analyze very thoroughly input data and form system's restrictions and acceptance ctiteria 
 - `restrictions.md`: Restrictions we have in real world in the -dashed list format.
 - `acceptance_criteria.md`: Acceptance criteria for the solution in the -dashed list format. 
   The most important part, determines how good the system should be.

### Example:
 - `problem_description.md`
    We have wing type UAV (airplane). It should fly autonomously to predetermined GPS destination. During the flight it is relying on the signal form GPS module.  
    But when adversary jam or spoof GPS, then UAV either don't know where to fly, or fly to the wrong direction.
    So, we need to achieve that UAV can fly correctly to the destination without GPS or when GPS is spoofed. We can use the camera pointing downward and other sensor data like altitude, available form the flight controller. Airplane is running Ardupliot. 
 - `input_data`
    - orthophoto images from the UAV for the analysis
    - list of expected GPS for the centers for each picture in csv format: picture name, lat, lon
    - video from the UAV for the analysis
    - list of expected GPS for the centers of video per timeframe in csv format: timestamp, lat, lon for each 1-2 seconds
    - ...
 - `restrictions.md`
    - We're limiting our solution to airplane type UAVs.
    - Additional weight it could take is under 1 kg.
    - The whole system should cost under $2000.
    - The flying range is restricted by eastern and southern part of Ukraine. And so on.  
 - `acceptance_criteria.md`
    - UAV should fly without GPS for at least 30 km in the sunshine weather.
    - UAV shoulf fly with maximum mistake no more than 40 meters from the real GPS
    - UAV should fly correctly with little foggy weather with maximum mistake no more than 100 meters from the real GPS
    - UAV should fly for minimum of 500 meters with missing inernal Satellite maps and the drifting error should be no more than 50 meters. 


## 1.2 **✨AI Research**: Restrictions and Acceptance Criteria assesment
 Put to the research context:
  - `problem_description.md`
  - `restrictions.md`
  - `acceptance_criteria.md`
  - Samples of the input data
 Run it in DeepResearch tool (Gemini, DeepSeek, or other)
```
The problem is described in `problem_description.md`.
 - System should process data samples in the attached files (if any). They are for reference only.  
 - Restrictions for the input data are stated in `restrictions.md`.
 - Acceptance criteria for the output of the system are stated in `acceptance_criteria.md`.  

  You are a professional software architect. Your task is to check how realistic these acceptance criteria are. Check how critical each criterion is. Find out more acceptance criteria for this specific domain.
  Research the impact of each value in the acceptance criteria on the whole system quality. 
  Assess acceptable ranges for each value in each acceptance criterion in the state-of-the-art solutions, and propose corrections in the next table:
   - Acceptance criterion name
   - Our values
   - Your researched criterion values
   - Status: Is the criterion added by your research to our system, modified, or removed
  Assess the restrictions we've put on the system. Are they realistic? Should we add more strict restrictions, or vise versa, add more requirements in restrictions to use our system. Propose corrections in the next table:
   - Restriction name
   - Our values
   - Your researched restriction values 
   - Status: Is a restriction added by your research to our system, modified, or removed
```
 **👨‍💻Developers**: Revise the result, discuss them and overwrite `acceptance_criteria.md` and `restrictions.md`
 

## 1.3 **✨AI Research**: Research the problem in great detail
  Replace md files with actual data
  Put to the research context samples of the input data
  Run it in DeepResearch tool (Gemini, DeepSeek, or other)  
 ```
The problem is described here:
 
  `problem_description.md`

 The system should process data samples in the attached files (if any). They are for reference only.
 The system has next restrictions and conditions:
 
   `restrictions.md`
 
 - Output of our system should meet these acceptance criteria:
   
   `acceptance_criteria.md`

 You are a professional software architect. Your task is to research all the possible ways to solve a problem, possibly split it to subproblems and research all the possible ways to solve subproblems respectfully, and find out most efficient state-of-the-art solutions.
 Produce the resulting solution draft in the next format:
   - Short Product solution description. Brief component interaction diagram.
   - Architecture approach that meets restrictions and acceptance criteria. For each component, analyze the best possible approaches to solve, and form a table comprising all approaches. Each new approach would be a row, and has the next columns:
     - Tools (library, platform) to solve component tasks
     - Advantages of this approach
     - Limitations of this approach
     - Requirements for this approach
     - How does it fit for the problem component that has to be solved, and the whole solution 
   - Testing strategy. Research the best approaches to cover all the acceptance criteria. Form a list of integration functional tests and non-functional tests.

Be concise in formulating. The fewer words, the better, but do not miss any important details.
```
 **👨‍💻Developer**: Revise the result from AI. Research the problem as well, and add/modify/remove some solution details in the draft.
 Store it to the `docs/01_solution/solution_draft.md`


## 1.4 **✨AI Research**: Solution draft assessment
Replace md files with actual data  
Run it in DeepResearch tool (Gemini, DeepSeek, or other)  
 ```
Read carefully about the problem:

  `problem_description.md`
 
 System has next restrictions and conditions:

  `restrictions.md`
 
 Output of the system should address next acceptance criteria:
 
  `acceptance_criteria.md`
 
 Here is a solution draft:
 
  `solution_draft.md`

 You are a professional software architect. Your task is to identify all potential weak points and problems. Address them and find out ways to solve them. Based on your findings, form a new solution draft in the same format.

If your finding requires a complete reorganization of the flow and different components, state it.
Put all the findings regarding what was weak and poor at the beginning of the report. Put here all new findings, what was updated, replaced, or removed from the previous solution.

Then form a new solution design without referencing the previous system. Remove Poor and Very Poor component choices from the component analysis tables, but leave Good and Excellent ones.
In the updated report, do not put "new" marks, do not compare to the previous solution draft, just make a new solution as if from scratch

```
 **👨‍💻Developer**: Research by yourself as well - how to solve additional problems which AI figured out, and add them to the result. Rename previous `solution_draft.md` to `xx_solution_draft.md`. And then store the result draft to the `docs/01_solution/solution_draft.md`, and repeat the process. When the next solution wouldn't differ much from the previous one, store the last draft as `docs/01_solution/solution.md`