SYSTEM STATUS: ONLINE // V 2.0

Vehicle
AutoRoute
Navigator

> An intelligent route planner prioritizing safety limits and onboard visual perception.
> Executed via ROS 2 + YOLOv8 on TurtleBot3.
Initialize System_

// CRITICAL ERROR

Navigation systems typically optimize strictly for T_min (Time).

This ignores multi-dimensional human constraints: Safety, Comfort, and Visual Experience. Current industrial stacks are blind to these variables.

// PATCH APPLIED

NavX introduces Multi-Criteria Path Scoring.

Dynamic re-routing based on YOLOv8 visual hazard detection. No cloud dependency. Pure onboard computation.

Core Capabilities

01

Transparent Scoring

Route transparency via explicit cost functions. User visibility into decision logic.

02

Closed-Loop Logic

Automatic re-planning triggers < 2000ms upon new object detection or hazard threshold breach.

03

ROS 2 Native

Seamless integration with Nav2 stack. Waypoint execution and cancellation handling.

04

Explainable UI

Real-time readout: "−18% risk, +0.3 scenic". Quantifiable trade-offs.

ALGORITHM_COST_FUNCTION

C = (w_t × Time) + (w_r × Risk) + (w_c × Comfort)

Profile Configuration

MODE T_Weight R_Weight C_Weight
VELOCITY 1.0 0.2 0.1
SECURITY 0.5 1.0 0.3
COMFORT 0.4 0.3 0.8

Hazard Classification

Real-time visual risk assessment pipeline.

[1_DETECTION]

Input: Razer Kiyo (720p 60FPS)
Model: YOLOv8
Output: Object Type, Bounding Box, Confidence

[2_ESTIMATION]

Distance: Calculated via bbox height
Density: Objects per route zone
Penalty: Close(x3), Med(x2), Far(x1)

HAZARD_SCORING_FORMULA

Risk = Σ (Confidence × ObjectWeight × DistancePenalty)

System Architecture

Distributed nodes. Modular design. Fail-safe.

HARDWARE_LAYER STATUS: ACTIVE RAZER CAM > YOLOv8 NODE HAZARD_SCORING RISK_MATRIX_CALC ● LIVE ROUTER (A*) COST = T(w) + R(w) + C(w) WAYPOINT_CLIENT ROS2 NAV_STACK FASTAPI_GATEWAY WEBSOCKET / REST MAP_VIEW CTRL USER_INTERFACE
> Router_Mod

Python A* implementation. Matrix evaluation.

> Signals_Node

Sensor fusion aggregation point.

> Waypoint_Client

Nav2 interface and action management.

SYSTEM_SPECIFICATIONS

HARDWARE & SOFTWARE INFRASTRUCTURE

> HARDWARE

Razer Kiyo Pro
TurtleBot3 Burger
Raspberry Pi 4 (4GB)

> AI_VISION

YOLOv8n (Nano)
60FPS Inference
Hazard Scoring Engine

> CORE_ROBOT

ROS 2 Humble
Nav2 Stack
Python 3.10

> DASHBOARD

React.js v18
TailwindCSS
FastAPI / WebSocket

INTERACTIVE_DEMO // V.1.4

Path Planning Visualization

Select a routing profile to observe real-time solver behavior.

HAZARD START TARGET
STATUS: MULTI_PATH_SIMULATION
■ VELOCITY
■ SECURITY
■ COMFORT
// VELOCITY_PROFILE
Optimizes for T_min. Accepts higher risk thresholds for direct routing.
RISK:
HIGH
[] SECURITY_PROFILE
Maximizes obstacle clearance. Reroutes around all detected hazard zones.
RISK:
LOW
~~ COMFORT_PROFILE
Minimizes lateral acceleration (G-force). Smooth curves only.
RISK:
MED
PERSONNEL_DATABASE // ACESS_LEVEL_1

Command Unit

ACTIVE_AGENTS: 09
SYNC_STATUS: 100%