🚀 LIMO Cobot Upgrade Guide
The LIMO Pro + MyCobot + YOLO system is more than a robot — it’s a platform.
This guide shows you everything you can modify, extend, or enhance to push it far beyond its default capabilities.
We’ve split upgrades into three categories:
- 🔧 Core Improvements — foundational tweaks for better performance.
- 🤖 Smart Features — new abilities that expand what the robot can do.
- 🧪 Experimental Add-ons — bold innovations for advanced builders.
🔧 Core Improvements
These upgrades make the robot faster, smoother, and more reliable.
- 🗺 Mapping & Navigation
- 🎯 Object Detection
- ⚡ Performance
- Switch to RTAB-Map for live mapping
Why: Explore unknown spaces without pre-scanning.
How:- Install rtabmap_ros
bash sudo apt install ros-foxy-rtabmap-ros
- Replace Cartographer launch in full_system.launch.py with rtabmap.launch.py.
- Launch rtabmap_explore for full autonomy.
Tip: RTAB-Map is heavier — reduce camera FPS if CPU spikes.
- Nav2 Parameter Tuning
Why: Achieve smooth turns and precise stops.
How: Edit nav2_params.yaml → adjust max_vel_x, acc_lim_theta, planner_frequency.
Tip: Test changes in a small test zone first.
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Increase YOLO Accuracy
Why: Current model has limited dataset (~150 images).
How:- Expand dataset in Roboflow or label with labelImg.
- Retrain with train_yolo.py.
- Replace .pt file in object_detector.py.
Tip: Include images from multiple angles & lighting conditions.
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Multi-Class Support
Why: Identify more than one type of object.
How:- Add new classes during training.
- In object_detector.py, filter by cls_name to decide pick/drop behavior.
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Offload YOLO to GPU (Jetson or CUDA PC)
Why: Lower CPU load, faster detection.
How: Install GPU-optimized PyTorch + YOLO, run detector on dedicated GPU. -
Cache TF Transforms
Why: Faster coordinate conversion.
How: Store recent TF lookups in a dictionary in object_detector.py and reuse them.
🤖 Smart Features
Upgrades that add intelligence and adaptability.
- 📦 Multi-Object Handling
- 🧠 User Recognition
- 🎙 Voice Interaction
- 🌐 Web Dashboard
- Queue Multiple Targets
Why: Avoid wasting time returning to base after each object.
How: Store multiple /target_pose messages in a Python list in mission_manager.py and iterate until empty.
- Vector Memory for Personalized Delivery
Why: Robot knows the user and returns their object only to them.
How:- Use face_recognition to create an embedding database.
- Link recognition results to drop logic in mission_manager.py.
Tip: Keep a small database in SQLite or JSON for quick lookup.
- Natural Voice Commands
Why: Hands-free operation in noisy environments.
How:- Integrate vosk or whisper.cpp for offline speech-to-text.
- Map phrases (“start”, “stop”, “come here”) to ROS2 actions.
Tip: Give voice feedback via espeak or pyttsx3.
- Browser-Based Control Panel
Why: Monitor & control from any device.
How:- Install rosbridge_server and web_video_server.
- Create a dashboard with roslibjs or use Foxglove Studio.
Tip: Restrict remote commands for safety.
🧪 Experimental Add-ons
For advanced builders ready to explore bold ideas.
Show Experimental Concepts
🤝 Multi-Robot Collaboration
- What: Multiple LIMOs share maps & divide pickup tasks.
- How: Sync map_server data over DDS multicast and run a central mission allocator.
🧮 AI Path Planning
- What: Use RL or heuristic algorithms to optimize object pickup order.
- How: Implement A* or integrate stable-baselines3.
🔋 Self-Docking & Charging
- What: Robot returns to a dock when battery is low.
- How: Add docking station with AprilTag or AR marker for precise alignment.
✋ Gesture Control
- What: Recognize human gestures for quick commands.
- How: Use MediaPipe or OpenPose to track hand signals.
📌 Contribution Guide
Fork the repo, build a proof-of-concept, and submit it via GitHub Issues
Every upgrade should keep the system modular and maintainable — avoid hardcoding, and make changes configurable via ROS2 parameters.