Multi-Agent SLAM! with Distributed Data Fusion and Frontier Searching
Recent Work
Robust MQTT Communications & Security
The MQTT infrastructure now enables secure, real-time data exchange between robot agents and the Map Manager. With brokers, subscribers, and publishers designed to run on distributed platforms, the system is designed to scale smoothly to multiple robot agents. Integrating data from distributed sources has enhanced the completeness of the global maps.
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| PolyMap MQTT pipeline |
Frontier Searching Implementation
I have integrated frontier searching techniques—drawing inspiration from Yamauci, 1997—to systematically prioritize and explore unknown regions of the map. This approach is central to refining our mapping strategy by dynamically guiding the system to areas that require further investigation.
Enhanced Visualization
The python visualization is now somewhat fully functional, offering a clear and dynamic representation of the evolving global map. Real-time updates provide immediate robot telemetry and map fusion data, ensuring that the state of the environment is always accurately reflected.
YouTube Video Demo
Here is a YouTube video showing PolyMap in action. You can see the simulated robots navigate in a virtual environment, exchange data via MQTT, and merge their local maps into a dynamic global view—all in real time.
Current Challenges
Robot Agent Behavior
While our simulation demonstrates significant progress, the robot agents exhibit limited exploratory behavior and are challenging to fine-tune. They often get stuck in corners, a behavior likely influenced by the coarse resolution of the current scanning technology. This is a complete topic that I will expand on more in the future.
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| Polymap: going long on Frontiers |
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| PolyMap: Robot Agent Exploration |
Operational Mode Transitions
In the system, robots cycle through distinct operational states that define their behavior during navigation. These include:
- Scan Mode: Actively gathering sensor data.
- Idle Mode: Maintaining a standby state when no immediate action is required.
- Obstruction Mode: Responding to detected obstacles.
- Frontier Mode: Venturing into uncharted areas for further exploration.
While these states provide a structured approach to managing robot behavior, the transitions between them need further review and refinement. Enhancing these transitions is critical to achieving smoother performance and greater responsiveness during navigation.
Upcoming Developments
Enhanced Sensing
We are set to incorporate a 'radar-like scan' into both our simulation and physical platforms. This upgrade will augment the capabilities of our single ultrasonic sensor, providing richer, more detailed environmental data.
Physical Implementation – Meet MinOne
The physical build of “MinOne” is now underway. This robot will serve as the real-world counterpart to our simulation, enabling us to bridge virtual testing with tangible, hands-on experiments.
Next-Generation Visualization
In our continuous effort to improve user experience and performance, we are evaluating the Godot engine as a potential upgrade for our visualization tool, aiming to deliver a more immersive and high-performance display.
Further Enhancements
Moving forward, we will focus on fine-tuning robot behaviors and sensor parameters, as well as exploring additional sensor integrations to boost mapping fidelity. The goal remains to develop a scalable and robust platform for multi-agent SLAM exploration and discovery.
PolyMap's evolution is driven by a relentless passion for iterative improvement and DIY robotics innovation. This project is about building something tangible—each challenge overcome and every feature refined brings PolyMap closer to bridging the gap between simulation and reality and expanding the boundaries of collaborative robotic mapping.
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