NavWareSet-Quant

Quantitative analysis of 541 robot tracks from the NavWareSet dataset. This project implements a comprehensive framework for evaluating robot social navigation behavior through kinematic and distance-based metrics.

Overview

This repository analyzes robot navigation behavior in human-populated environments across 5 distinct scenarios. The analysis computes 6 key metrics for each of 20 scenario-robot-behavior combinations (5 scenarios × 2 robots × 2 behaviors), providing quantitative insights into navigation performance and social compliance.

Project Structure

NavWareSet-Quant/
├── NavWareSet_Quantitative_Analysis.ipynb    # Main analysis notebook
├── plot_all_tracks.py                         # Visualization script for individual tracks
├── NavWareSet_Quantitative_Analysis_Results.csv  # Output results table
├── selected_tracks/                           # Track data directory
│   ├── 01_poses/                             # Frontal Approach tracks
│   ├── 02_poses/                             # Pedestrian Obstruction tracks
│   ├── 03_poses/                             # Blind Corner tracks
│   ├── 04_poses/                             # Perpendicular Crossing tracks
│   ├── 05_poses/                             # Circular Crossing tracks
│   └── ... (additional scenario directories)
└── README.md                                  # This file

Data Formats

The project handles two different CSV track formats seamlessly:

Format 1: Column-Based Format

Used for multi-participant scenarios where each row represents a single human participant at a given timestamp.

timestamp,x,y,column,robot_x,robot_y,robot_yaw_rad
1730817548102506496,0.5,1.2,1,2.1,3.5,-0.52
1730817548102506496,0.8,0.9,2,2.1,3.5,-0.52
1730817548202506496,0.5,1.3,1,2.2,3.6,-0.50

Columns:

• timestamp: Unix nanosecond timestamp
• x, y: Human participant coordinates (meters)
• column: Participant identifier (1-5)
• robot_x, robot_y: Robot position (meters)
• robot_yaw_rad: Robot orientation (radians)

Format 2: XY-Pairs Format

Used for fixed human position scenarios with up to 5 human participants.

timestamp,robot_x,robot_y,robot_yaw_rad,x1,y1,x2,y2,x3,y3,x4,y4,x5,y5
1730818675992630528,1.5,2.0,-0.25,0.2,1.0,0.9,1.5,1.8,0.5,2.1,1.1,2.5,0.9
1730818675992730528,1.6,2.1,-0.23,0.2,1.0,0.9,1.5,1.8,0.5,2.1,1.1,2.5,0.9

Columns:

• timestamp: Unix nanosecond timestamp
• robot_x, robot_y: Robot position (meters)
• robot_yaw_rad: Robot orientation (radians)
• x1-x5, y1-y5: Coordinates of up to 5 fixed human participants (meters)

Metrics

The analysis calculates 6 quantitative metrics for robot social navigation evaluation:

1. Vavg - Average Velocity (m/s)

Global average speed across all robot tracks. Computed as total distance traveled divided by total time.

2. Aavg - Average Acceleration (m/s²)

Time-weighted average of instantaneous acceleration changes. Indicates smoothness and aggressiveness of motion.

3. Javg - Average Jerk (m/s³)

Time-weighted average of rate of acceleration change. Measures motion comfort and predictability.

4. PLavg - Average Path Length (m)

Mean total distance traveled across all tracks in a scenario. Indicates navigation efficiency.

5. PSC - Personal Space Compliance (%)

Percentage of timestamps where the robot maintains minimum safe distance (default 0.5m) from all humans. Higher values indicate better social awareness.

Distance Adjustment: Measured distances are adjusted by subtracting 0.4m to account for the physical dimensions of the robot and humans (center-to-center → surface-to-surface approximation). This provides a more realistic measure of actual personal space violation, as it represents the combined radii of the robot (~0.1-0.2m) and human (~0.2m).

6. DHmin - Average Minimal Distance (m)

Average of the closest approach distance achieved in each track. Directly measures human-robot proximity.

Scenarios

The analysis covers 5 distinct navigation scenarios:

1. Frontal Approach (01_poses): Robot approaches human from front
2. Pedestrian Obstruction (02_poses): Robot navigates around obstructing human
3. Blind Corner (03_poses): Robot encounters human at hallway corner
4. Perpendicular Crossing (04_poses): Robot crosses perpendicular to human path
5. Circular Crossing (05_poses): Robot and human perform circular trajectories

Robot and Behavior Types

• Robots: HSR (Humanoid Service Robot), Jackal (wheeled platform)
• Behaviors: Compliant (socially-aware navigation), Non-compliant (minimal social awareness)

This creates 5 × 2 × 2 = 20 unique scenario combinations.

Usage

Running the Analysis

1. Open the Jupyter notebook:

   jupyter notebook NavWareSet_Quantitative_Analysis.ipynb

2. Execute all cells to:

   • Load all tracks from the selected_tracks/ directory
   • Calculate metrics for each scenario combination
   • Generate results table
   • Export results to CSV

Visualizing Individual Tracks

To plot detailed visualizations of robot-human interactions for a specific track:

python plot_all_tracks.py

This script:

• Auto-detects CSV format
• Plots robot trajectory (blue line)
• Plots human positions (red points or circles based on format)
• Shows robot and human relative motion
• Works with both CSV formats

Interpreting Results

Results are saved to NavWareSet_Quantitative_Analysis_Results.csv with columns:

• Scenario: Navigation scenario name
• Robot: Robot type (HSR/Jackal)
• Robot Behavior: Compliant/Non-compliant
• Vavg[1]: Average velocity (m/s)
• Aavg[1]: Average acceleration (m/s²)
• Javg[1]: Average jerk (m/s³)
• PLavg (avg) [1]: Average path length (m)
• PSC [2]: Personal Space Compliance (%)
• DHmin avg [3]: Average minimal distance (m)

Implementation Details

Format Detection

All metric functions include automatic format detection:

if 'column' in df.columns:
    # Format 1: Column-based (multiple rows per timestamp)
else:
    # Format 2: XY-pairs format

Key Functions

• global_average_speed(scene_list): Computes kinematic velocity
• global_average_acceleration(scene_list): Time-weighted acceleration
• global_average_jerk(scene_list): Rate of acceleration change
• global_average_path_length(scene_list): Mean path length
• global_average_personal_space_compliance(scene_list, threshold=0.5): Safety distance compliance
• global_average_minimal_distance(scene_list): Closest approach distance

Custom Thresholds

Modify the Personal Space Compliance threshold by editing the function call:

# Default: 0.5m threshold
psc = global_average_personal_space_compliance(scene_list, threshold=0.5)

# Custom: 0.3m threshold
psc = global_average_personal_space_compliance(scene_list, threshold=0.3)

Dependencies

• Python 3.8+
• pandas: Data manipulation and analysis
• numpy: Numerical computations
• pathlib: File path operations
• matplotlib: Visualization (for plotting script)

Install dependencies:

pip install pandas numpy matplotlib

Files Description

File	Purpose
NavWareSet_Quantitative_Analysis.ipynb	Main analysis pipeline with all metric calculations
plot_all_tracks.py	Standalone visualization script for individual tracks
NavWareSet_Quantitative_Analysis_Results.csv	Generated results table (output)
selected_tracks/	Track data organized by scenario

Results Summary

The analysis processes 545 hand-selected robot tracks across all scenario combinations and generates a 20-row results table with 6 metrics each, enabling comparison of:

• Robot types (HSR vs Jackal)
• Navigation behaviors (Compliant vs Non-compliant)
• Scenario difficulty (kinematic vs distance-based metrics)
• Social awareness effectiveness (PSC metric)

Notes

• Timestamps are in Unix nanoseconds; division by 1e9 converts to seconds
• Path lengths are cumulative distances; shorter paths indicate more efficient navigation
• All distances are in meters; all times are in seconds
• The framework automatically detects CSV format, ensuring robustness across dataset variations