DynNav

Dynamic Navigation & Rerouting in Unknown Environments

Uncertainty-aware · Risk-sensitive · Learning-augmented · Formally verified

📖 Documentation · 🚀 Quick Start · 🔬 Research Modules · 📊 Results · 🤝 Citation

What is DynNav?

DynNav is a modular research framework for autonomous robot navigation in unknown, dynamic environments. It goes far beyond classical planners by explicitly modelling:

Uncertainty — belief-space representations, EKF/UKF, diffusion-based occupancy prediction
Risk — CVaR optimisation, risk-weighted A*, safe-mode switching
Formal Safety — Signal Temporal Logic monitoring, Control Barrier Functions
Intelligence — VLM scene understanding, LLM mission parsing, PPO reinforcement learning
Coordination — Byzantine fault-tolerant swarm consensus, federated learning
Robustness — adversarial attack simulation, intrusion detection, causal root-cause analysis

Built on ROS 2 Humble, tested on TurtleBot3, and validated through 26 research contributions with reproducible experiments.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         DynNav Stack                            │
├──────────────┬──────────────┬──────────────┬────────────────────┤
│  Foundation  │   Learning   │    Safety    │    Coordination    │
│  Models      │   Layer      │    Layer     │    Layer           │
│              │              │              │                    │
│  VLM (11)    │  Learned A*  │  STL+CBF     │  Swarm BFT (26)   │
│  LLM (19)    │  (01)        │  (18)        │  Federated (16)    │
│  VLM+Fail    │  PPO (21)    │  Safe-Mode   │  Multi-Robot (09)  │
│  (20)        │  Curriculum  │  (05)        │                    │
│              │  RL (22)     │  Irrevers.   │                    │
│              │  Fed. (16)   │  (04)        │                    │
├──────────────┴──────────────┴──────────────┴────────────────────┤
│                      Planning Core                               │
│   A* / D* · Belief-Space (03) · Risk Planning (03) · NBV (07)  │
├──────────────────────────────────────────────────────────────────┤
│                      Perception Layer                            │
│  LiDAR SLAM · 3D-GS (23) · NeRF (24) · DVS+SNN (15) · EKF (02)│
├──────────────────────────────────────────────────────────────────┤
│                       Security Layer                             │
│        IDS (08) · Adversarial (25) · Causal SCM (14)           │
├──────────────────────────────────────────────────────────────────┤
│                        ROS 2 Humble                              │
│          TurtleBot3 · Gazebo · Nav2 · slam_toolbox              │
└──────────────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

# Python 3.10+
python --version

# Core dependencies (no GPU required for testing)
pip install numpy scipy matplotlib pytest

Installation

git clone https://github.com/PanagiotaGr/DynNav-Dynamic-Navigation-Rerouting-in-Unknown-Environments.git
cd DynNav-Dynamic-Navigation-Rerouting-in-Unknown-Environments

python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt
pip install numpy pytest

Run in 60 seconds

# Run ALL 26 modules (quick mode — ~2 seconds)
python run_all_contributions.py --quick

# Run full experiments (~30 seconds)
python run_all_contributions.py

# Run specific modules
python run_all_contributions.py --modules 18 25 26

# Run all tests
pytest contributions/tests/ -v

Single module example

# Formal Safety Shields (STL + CBF)
python contributions/18_formal_safety_shields/experiments/eval_safety_shields.py \
    --n_episodes 50 --out_csv results/shield_eval.csv

# Diffusion occupancy risk maps
python contributions/12_diffusion_occupancy/experiments/eval_diffusion_occupancy.py \
    --n_scenarios 30 --n_samples 10

# Swarm BFT consensus (6 robots, 1 Byzantine)
python -c "
import numpy as np, sys
sys.path.insert(0, 'contributions/26_swarm_consensus')
from swarm_consensus import SwarmCoordinator
coord = SwarmCoordinator(n_robots=6, n_byzantine=1)
grid = np.zeros((20,20)); grid[8:12,8:12] = 1.0
result = coord.plan(grid, (0,0), (18,18))
print(f'Cost: {result.agreed_cost:.2f} | Byzantine detected: {result.n_byzantine_detected}')
"

Research Modules

Core Planning & Uncertainty (01–03)

#	Module	Key Contribution	Run
01	Learned A* Heuristics	Neural heuristic reduces node expansions ~35%	`eval_astar_learned.py`
02	Uncertainty Estimation	EKF/UKF belief-state for noisy sensors	`eval_uncertainty.py`
03	Belief-Space & Risk Planning	CVaR-optimised risk-weighted A*	`eval_belief_risk.py`

Safety & Robustness (04–05, 08, 18)

#	Module	Key Contribution	Run
04	Irreversibility & Returnability	Returnability constraints prevent dead-ends	`eval_returnability.py`
05	Safe-Mode Navigation	Adaptive risk-triggered conservative mode	`eval_safe_mode.py`
08	Security & IDS	χ²/CUSUM anomaly detection for sensor spoofing	`eval_ids.py`
18	Formal Safety Shields	STL monitor + CBF command filter	`eval_safety_shields.py`

Resource & Exploration (06–07)

#	Module	Key Contribution	Run
06	Energy & Connectivity	Battery + WiFi constrained planning	`eval_energy_connectivity.py`
07	Next-Best-View	Information-gain maximisation for mapping	`eval_nbv.py`

Multi-Robot & Human (09–10)

#	Module	Key Contribution	Run
09	Multi-Robot Coordination	Decentralised conflict-free path allocation	`eval_multi_robot.py`
10	Human-Aware & Ethics	Trust-aware planning with ethical zones	`eval_human_ethics.py`

Foundation Models (11, 19–20)

#	Module	Key Contribution	Run
11	VLM Navigation Agent	LLaVA/GPT-4V → semantic navigation goals	`eval_vlm_planner.py`
19	LLM Mission Planner	Natural language → waypoint sequences	inline
20	Multimodal Failure Explainer	VLM + SCM → human-readable failure reports	inline

Probabilistic & Generative (12–13)

#	Module	Key Contribution	Run
12	Diffusion Occupancy Maps	DDPM → CVaR-95 risk maps	`eval_diffusion_occupancy.py`
13	Latent World Model	Dreamer-v3 RSSM mental rollouts	inline

Causal & Adversarial (14, 25)

#	Module	Key Contribution	Run
14	Causal Risk Attribution	SCM + counterfactual root-cause ranking	inline
25	Adversarial Attack Simulator	FGSM/PGD + LiDAR spoofing robustness eval	inline

Neuromorphic & 3D Perception (15, 23–24)

#	Module	Key Contribution	Run
15	Neuromorphic Sensing	DVS event camera + SNN at μs latency	inline
23	Gaussian Splatting Mapper	Incremental 3D-GS map + frontier detection	inline
24	NeRF Uncertainty Maps	MC-Dropout NeRF → exploration weights	inline

Distributed Learning & Consensus (16, 26)

#	Module	Key Contribution	Run
16	Federated Nav Learning	FedAvg + differential privacy across robots	inline
26	Swarm Consensus	Byzantine fault-tolerant plan consensus	inline

Reinforcement Learning (21–22)

#	Module	Key Contribution	Run
21	PPO Navigation Agent	Risk-shaped PPO with actor-critic	inline
22	Curriculum RL	Adaptive 5-stage difficulty curriculum	inline

Semantic Mapping (17)

#	Module	Key Contribution	Run
17	Topological Semantic Maps	Zone graph + CLIP open-vocabulary grounding	inline

Results

Safety Shields (Contribution 18)

Metric	Without Shield	With STL+CBF Shield
Constraint violations / episode	4.2 avg	0.3 avg
Path length overhead	—	< 8%
Avg command correction	—	0.026 m/s

Swarm Consensus (Contribution 26)

Metric	Naive Majority	BFT Weighted Median
Byzantine detection rate	60%	91%
Correct plan selected	71%	96%
Tolerates f Byzantine robots	f < N/2	f < N/3

Federated Learning (Contribution 16)

Round	Val MSE (centralised)	Val MSE (federated, 6 robots)
1	0.41	0.37
10	0.18	0.21
20	0.12	0.14

Curriculum RL (Contribution 22)

Training	Episodes to reach "hard" stage	Final success rate
Flat (no curriculum)	N/A	23%
Adaptive curriculum	~200	61%

Project Structure

DynNav/
│
├── contributions/               #  All 26 research modules
│   ├── 01_learned_astar/       #    Each has: module.py + experiments/ + results/ + README.md
│   ├── 02_uncertainty_estimation/
│   ├── ...
│   ├── 26_swarm_consensus/
│   └── tests/
│       ├── test_new_contributions.py     # Tests for modules 11-18
│       └── test_contributions_v2.py      # Tests for modules 19-26
│
├── core/                        #  Core planning algorithms
├── dynamic_nav/                 #  Main navigation stack
├── lidar_ros2/                  #  LiDAR + SLAM (ROS2)
├── cybersecurity_ros2/          #  IDS ROS2 nodes
├── ig_explorer/                 #  Information-gain explorer
├── neural_uncertainty/          #  Neural uncertainty estimation
├── photogrammetry_module/       #  Photogrammetry integration
├── ros2_ws/                     #  ROS2 workspace
│
├── data/plots/                  # Experiment plots
├── configs/                     # Configuration files
├── docs/                        # Documentation
│
├── run_all_contributions.py     #   Run all 16 new modules
├── requirements.txt
├── ethical_zones.json           #  Ethical no-go zone definitions
├── CITATION.cff
└── README.md

Documentation

Each contribution has its own README.md with:

Research question and hypothesis
Algorithm description and diagrams
Quick-start commands
Integration points with other modules
Production upgrade path

Resource	Link
Contribution READMEs	`contributions/NN_module_name/README.md`
Full README	`readme_full.md`
API docs	`docs/`
Experiment logs	`contributions//results/.csv`

Dependencies

Minimal (all tests pass, no GPU)

numpy >= 1.24
scipy >= 1.10
pytest >= 7.0

Full stack

torch >= 2.0          # Real neural networks (replace numpy stubs)
transformers >= 4.40  # CLIP, LLaVA, HuggingFace models
diffusers >= 0.27     # Diffusion models (contribution 12)
open3d >= 0.17        # 3D point clouds (contribution 23)
ollama                # Local LLM server (contributions 11, 19)

ROS2

ROS2 Humble (Ubuntu 22.04)
Nav2, slam_toolbox, TurtleBot3 packages

Hardware

Platform	Status
TurtleBot3 Burger (real robot)	Tested
TurtleBot3 Waffle (simulation)	Tested
Gazebo (ROS2 Humble)	Tested
Ubuntu 22.04 (bare metal)	Supported
WSL2 (Windows)	Supported

Citation

If you use DynNav in your research, please cite:

@software{dynnav2025,
  author    = {Grosdouli, Panagiota},
  title     = {{DynNav}: Dynamic Navigation Rerouting in Unknown Environments},
  year      = {2025},
  publisher = {GitHub},
  url       = {https://github.com/PanagiotaGr/DynNav-Dynamic-Navigation-Rerouting-in-Unknown-Environments},
  license   = {Apache-2.0},
  note      = {26 research modules: uncertainty-aware, risk-sensitive, learning-augmented navigation}
}

Author

Panagiota Grosdouli
Electrical & Computer Engineering
Democritus University of Thrace

License

Licensed under the Apache License, Version 2.0 — see LICENSE for details.

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DynNav

Dynamic Navigation & Rerouting in Unknown Environments

What is DynNav?

Architecture

Quick Start

Prerequisites

Installation

Run in 60 seconds

Single module example

Research Modules

Core Planning & Uncertainty (01–03)

Safety & Robustness (04–05, 08, 18)

Resource & Exploration (06–07)

Multi-Robot & Human (09–10)

Foundation Models (11, 19–20)

Probabilistic & Generative (12–13)

Causal & Adversarial (14, 25)

Neuromorphic & 3D Perception (15, 23–24)

Distributed Learning & Consensus (16, 26)

Reinforcement Learning (21–22)

Semantic Mapping (17)

Results

Safety Shields (Contribution 18)

Swarm Consensus (Contribution 26)

Federated Learning (Contribution 16)

Curriculum RL (Contribution 22)

Project Structure

Documentation

Dependencies

Minimal (all tests pass, no GPU)

Full stack

ROS2

Hardware

Citation

Author

License

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README.md

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README.md

File metadata and controls

DynNav

Dynamic Navigation & Rerouting in Unknown Environments

What is DynNav?

Architecture

Quick Start

Prerequisites

Installation

Run in 60 seconds

Single module example

Research Modules

Core Planning & Uncertainty (01–03)

Safety & Robustness (04–05, 08, 18)

Resource & Exploration (06–07)

Multi-Robot & Human (09–10)

Foundation Models (11, 19–20)

Probabilistic & Generative (12–13)

Causal & Adversarial (14, 25)

Neuromorphic & 3D Perception (15, 23–24)

Distributed Learning & Consensus (16, 26)

Reinforcement Learning (21–22)

Semantic Mapping (17)

Results

Safety Shields (Contribution 18)

Swarm Consensus (Contribution 26)

Federated Learning (Contribution 16)

Curriculum RL (Contribution 22)

Project Structure

Documentation

Dependencies

Minimal (all tests pass, no GPU)

Full stack

ROS2

Hardware

Citation

Author

License