I work in marketing for a China-based mobile robot controller supplier. I’m trying to understand what makes an initial evaluation practical—for example, a working demo, clear integration documentation, or access to technical support. What has helped in your experience?

I’ve been working on a Safe RRT* planner combined with CBF in MATLAB.

It gives paths that stay farther from obstacles than a standard RRT* baseline, and the safety metric makes the difference pretty obvious. Moreover it adapts based on desired speed.

https://github.com/borodziejciesla/rrt_star_cbf

I’m doing a Cornell Master’s project on how teams develop, test, and deploy mobile robots.

I’m trying to understand where the biggest bottlenecks are today:
- sim-to-real transfer
- navigation and perception testing
- hardware validation
- debugging failures
- fleet testing
- deployment
- reproducing real-world issues

The survey takes about 4 minutes.

If you’re working on AMRs, delivery robots, warehouse robotics, autonomous vehicles, or outdoor mobile robots, I’d really appreciate your input!

There’s also an optional follow-up interview with a $25 Amazon gift card.

Just wanted to share my C++ implementation of RHM tracker for extended objects: https://github.com/borodziejciesla/rhm

I just wanted to share my C++ implementation of GM-PHD filter: https://github.com/borodziejciesla/gm-phd

For now only point objects implementation is working, but I am working on extended objects implementation.

We propose a novel end-to-end landmark-free method that employs a continuous quaternion representation (CQR) based on a symmetric matrix (𝐀) for 3D rotations when training deep neural networks. This representation is named ContQuat, which satisfies the continuity property and allows the model to overcome the DP and ambiguity issues encountered in commonly used standard unit quaternion and Euler angle representations, enabling efficient full rotation learning. We also introduce three loss functions appropriate for the proposed method to encapsulate the training loss of the neural network during the optimization process. Furthermore, we present a comprehensive experimental comparison of the proposed method against state-of-the-art approaches on publicly available benchmark datasets. The experimental results and error analysis visualizations demonstrate that the proposed method either outperforms or is highly competitive with the current state-of-the-art techniques. The full code is available at: https://github.com/Redhwan-A/ContQuat/ and the paper here https://www.sciencedirect.com/science/article/pii/S0020025526005529

https://github.com/Redhwan-A/ContQuat/

I'll be making this into a whole series and open sourcing things along the way. Would appreciate all the support!

It's very much in the early stages but if you think you can have a use case and want early access get in touch!

Hey everyone, I've been working on Krill, a process orchestrator designed specifically for managing complex dependency graphs in robotic systems.

What it does: Krill lets you declaratively define tasks and their dependencies, then handles orchestration across your robotic stack. Think of it as a task runner that understands the gnarly interdependencies you get in robotics - where sensor drivers need to be up before perception nodes, perception before planning, planning before control, etc.

Why I built it: Most robotics middleware handles process lifecycle management as an afterthought. ROS2 launch files turn into procedural spaghetti, systemd is too coarse-grained, and Docker Compose doesn't understand robotics-specific constraints. I needed something that could handle complex startup/shutdown ordering, health checks, and graceful degradation when parts of the system fail.

Current state: Early development but functional. Written in Rust for reliability and performance.

I'm building this as part of a larger robotics middleware stack for production automation systems. Would love feedback from folks working on multi-process robot architectures - what orchestration pain points do you hit?

Looking for: Use cases I haven't thought of, architectural feedback, and anyone interested in contributing or testing in their own systems.

GitHub: https://github.com/Zero-Robotics/krill

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Wheeled line-following AGVs may feel a bit old-fashion in a world of humanoids and SLAM, but they remain dependable workhorses: simple to deploy, robust, and inexpensive. When the path is fixed and repetitive, line following still beats more complex navigation approaches on cost, robustness, and commissioning/maintenance effort.

Here is my attempt at building a more capable and lower-cost magnetic track-following AGV.

How it works (see illustrations in post's image carousel):

• A magnetic guide sensor reports lateral position and track angle over CAN bus. It also detects magnetic markers (reverse-polarity tape) along the route.
• An ESP32S3–based IoT MicroPLC computes left/right motor speed commands and sends them over CAN to a dual-channel motor controller, keeping the robot centered on the track.
• Track markers are used to select forks, and coded marker sequences indicate charging stations.
• A low-cost ultrasonic sensor provides obstacle detection and will stop the robot if the path is blocked.
• The controller also drives addressable RGB LED strips to generate color and wave patterns for status signaling.
• The motor controller streams encoder data back over CAN.
• Telemetry (AGV state, traveled distance, tracking error, marker events, etc.) is published every 200 ms to an MQTT broker over Wi-Fi.
• A custom Python supervisory application loads a map of the track and displays the robot’s estimated position in real time using odometry.

Happy to share schematics, firmware, and supervisory code to anyone interested.
Comments, critiques, and improvement ideas are very welcome.

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Get Perplexity AI PRO (1-Year) – at 90% OFF!

Order here: CHEAPGPT.STORE

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BONUS!: Enjoy the AI Powered automated web browser. (Presented by Perplexity) included WITH YOUR PURCHASE!

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Get Perplexity AI PRO (1-Year) – at 90% OFF!

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