Adaptive • Aesthetic • Physically-consistent

SkyLord Eyes — Adaptive Drone Navigation & Coverage

A hybrid navigation algorithm that blends adaptive boustrophedon planning with physics-aware motion, dynamic speed profiling and elegant visualization — designed for precise coverage, realistic motion, and unpredictability where it matters.

Run Visualizer Algorithm Details
V7
Jet Trail Visualizer
High-fidelity 2D simulation with physics
FPS
60 FPS
Smooth playback for visual inspection
 Navigator Simulation Video
Spacing ≈ 42.6 m
Cruise 5.0 m/s
Coverage ~5 min
Abstract

SkyLord Eyes in one paragraph

SkyLord Eyes is an adaptive navigation and coverage algorithm that fuses perimeter-aware boustrophedon sweeps with dynamic route switching and physics-based motion modelling. The system produces realistic flight paths with smooth speed profiles and visual jet-trails for surveillance, monitoring and mapping tasks.

Key highlights:

  • Adaptive path generation for arbitrary shapes (rectangular, circular, polygonal).
  • Dynamic route switching to introduce unpredictability and robustness.
  • Physics-based motion with smoothed speed and yaw to ensure feasible drone trajectories.
  • High-fidelity visualization (SkyLord Eyes V7) with jet-trail and FOV cone.
Objectives

What SkyLord Eyes aims to achieve

Clear, measurable goals that guide design and evaluation.

Primary Objectives

  • Autonomously define and cover surveillance areas of arbitrary shape.
  • Generate dynamic sweep patterns (boustrophedon variants) tuned by altitude & FOV.
  • Simulate realistic drone motion (acceleration, yaw limits, speed smoothing).
  • Deliver live visualizations for operator assessment and demonstration.

Design Goals

  • Minimise redundant overlap while ensuring full coverage.
  • Introduce controlled unpredictability for security use-cases.
  • Produce aesthetically clear visual traces for mission reviews.
  • Provide an extensible foundation for AI-driven re-planning.
Algorithm Design

Core ideas & formulas

A concise but rigorous description for reviewers and implementers.

Environment Setup

The system models the environment in a 2D coordinate frame. Boundaries are defined by GPS-local coordinates. Supported shapes: rectangle, circle, arbitrary polygon (closed polyline).

Perimeter Mapping

Drone follows the environment boundary once to generate exact limits and to calibrate initial orientation. Perimeter data is then used to compute interior sweep lines.

Boustrophedon Sweep & Spacing (Key formula)

Spacing = 2 × h × tan(FOV / 2) × (1 − overlap)

Where h = flight altitude, FOV = camera field-of-view (degrees), and overlap ∈ [0,0.5] is the desired frame overlap. This yields stripe spacing that guarantees visual coverage.

Dynamic Route Switching & Variation

SkyLord Eyes alternates among multiple sweep modes — classic boustrophedon, angled-phase sweeps, sinusoidal sweeps and randomized phase shifts. Each segment includes a variable speed factor and may flip sweep angle periodically to avoid predictable patterns.

Speed profiling (per segment): 
v = clamp( v_base × f_turn × f_distance, v_min, v_max )

f_turn  := factor that reduces speed near sharp turns
f_distance := factor that adjusts speed on short/long segments
Speeds are smoothed using exponential moving filters to produce continuous acceleration/deceleration profiles for realistic motion.
Motion Physics

Feasible yaw & velocity updates

Equations used per simulation timestep Δt for realistic motion.
yaw_{t+1} = yaw_t + clamp(yaw_desired - yaw_t, -r_max*Δt, r_max*Δt)
v_{t+1}   = v_t   + clamp(v_cmd - v_t, -a_dec*Δt, a_acc*Δt)
x_{t+1}   = x_t   + v_{t+1} * cos(yaw_{t+1}) * Δt
y_{t+1}   = y_t   + v_{t+1} * sin(yaw_{t+1}) * Δt

These equations ensure turn-rate limits (r_max) and acceleration/deceleration caps (a_acc, a_dec) — creating smooth trajectories without abrupt changes that would be infeasible for a real UAV.

SkyLord Eyes V7

Jet Trail Navigation Visualizer

A high-fidelity 2D simulator built for development, tuning and compelling demos.

Visualizer Features

  • Rotating drone cursor aligned to heading (triangle marker).
  • Jet-trail with fading opacity to reveal mission progression.
  • Camera FOV wedge rendered in real-time.
  • HUD showing speed, heading, phase, current segment and telemetry.
  • Phase switching and randomized pattern generator for unpredictable coverage.

Simulation Parameters (example)

  • Environment: 240 × 180 m
  • Altitude: 45 m
  • FOV: 62°
  • Overlap: 25%
  • Stripe spacing: ≈ 42.6 m
  • Average cruise speed: 5.0 m/s
  • Coverage time: ≈ 5 minutes (single drone)
  • Visualization FPS: 60

Performance Observations

  • Motion smoothing eliminated abrupt yaw changes; turns are physically plausible.
  • Jet-trail visualization clarifies mission coverage and temporal order of scanning.
  • Phase-switching achieves unpredictable but complete coverage; useful for security/patrol use-cases.
Results

Simulation outcomes & metrics

Key Metrics

  • Stripe Spacing: ≈ 42.6 m (calculated)
  • Average Cruise Speed: 5.0 m/s
  • Coverage Time (240×180m): ≈ 5 minutes
  • Visualization Frame Rate: 60 FPS (smooth)

Observational Notes

  • Coverage completeness validated in simulation runs with perimeter-first mapping and subsequent sweep phases.
  • Jet-trail reveals redundant overlaps and helps fine-tune spacing/overlap parameters for efficiency.
  • Speed profiling reduces time spent in low-value turns; increases effective coverage per battery unit.
Applications

High-impact use cases

Surveillance & Security

Patrol large perimeters with unpredictable sweep patterns to deter intrusions.

Agricultural Scanning

Systematic crop scanning with geotagged sensor data and imagery for analytics.

Search & Rescue

Adaptive search patterns for maximizing probability of detection across irregular areas.

Aerial Mapping

Efficient, low-redundancy mapping with configurable overlap and stripe spacing.

AI-Controlled Fleets

Extensible for cloud-driven multi-drone coordination and formation coverage.

Demonstration & Education

Clear visualizations are ideal for demos, research papers and teaching algorithm behavior.

Future

Planned extensions & research directions

  • Integrate object-detection triggers: automatically re-route to inspect detected events.
  • Cloud-assisted real-time route optimization for multi-drone coordination and dynamic re-tasking.
  • Support irregular polygonal boundaries with robust decomposition and coverage proofs.
  • Test and validate on hardware: Raspberry Pi companion computer → Flight controller → Telemetry link.
  • Extend visualizer to 3D with elevation-aware coverage and terrain constraints.
Repository

Explore the Code

SkyLord Eyes on GitHub
Access the complete source code, installation guide, and simulation scripts.
View Repository
References

Source & further reading

  • SkyLord Eyes — Project Report. SkyLord Eyes – Adaptive Drone Navigation and Coverage Algorithm. (Project materials & visualizer).
  • Choset, H. et al. “Coverage Path Planning: The Boustrophedon Cellular Decomposition.” Annals of Mathematics and Artificial Intelligence, 2001.
  • Siegwart, Nourbakhsh & Scaramuzza. *Introduction to Autonomous Mobile Robots*. MIT Press.