DroneSafePath

A Python-based drone deconfliction system for the FlytBase Robotics Assignment. Simulates 3D drone trajectories, detects conflicts within a 10m safety buffer, and visualizes results using interactive Plotly plots with constant trajectories, moving points, a 60-step time slider, a tracking camera, and red conflict markers. Supports four test scenarios (one no-conflict, three with conflicts). Outputs include HTML plots and MP4 animations. Implemented as a self-contained Jupyter notebook or modular codebase.

Deliverables

1. Code Repository

• Solution: A Python solution simulating multiple drone trajectories and performing spatial-temporal conflict detection.
  • Single Notebook Option: drone_deconfliction.ipynb embeds test data (JSON strings) for portability, eliminating FileNotFoundError issues.
• Modularity: Code is organized into distinct modules:
  • Data loading and validation.
  • Conflict detection (spatial-temporal checks).
  • Visualization with Plotly.
• Documentation: Inline comments and docstrings explain functionality.
• Test Scenarios:
  • no_conflict: Clear paths.
  • conflict_1: Conflict at t≈12.5 (~8.66m).
  • conflict_2: Dual conflicts at t≈10 (~6.71m), t≈20 (~8.94m).

Setup Instructions

Prerequisites

• Python 3.8+ (Anaconda recommended).
• VS Code with Python and Jupyter extensions (optional for notebook).
• Dependencies:

  pip install plotly numpy kaleido