MirrorBallBot Uses Mirror to Expand Vision
Hackster.io reports that maker Andrea Favero built a DIY robot, MirrorBallBot, that uses computer vision and an angled mirror to balance a rolling ball on a tilting platform and chase a moving fingertip. To get a wide field of view without placing the camera far away, Favero mounted a mirror in the optical path of a Raspberry Pi Camera Module 3 Wide, which expands the effective field of view by roughly 60% while keeping the robot compact, according to Hackster. A Raspberry Pi 4 Model B handles vision, inverse kinematics, and PID control, while three RP2040-Zero boards drive stepper motors through TMC2209 drivers, synchronized over a custom I2C protocol. A 7-inch touchscreen lets users tune PID parameters with on-screen sliders, draw custom trajectories, and run automatic ball-color calibration, alongside a finger-follower mode.
What happened
Hackster News reports Andrea Favero created MirrorBallBot, a compact balance-bot that uses computer vision and an angled mirror to balance and actively chase a finger-mounted target. Hackster News describes the mirror as increasing the camera's effective viewing distance and expanding the field of view by roughly 60%, enabling a larger apparent platform without increasing the robot's physical footprint.
Technical details
Per Hackster News, the build uses a Raspberry Pi Camera Module 3 Wide in the optical path and a Raspberry Pi 4 Model B as the central controller. The Pi handles vision, inverse kinematics calculations, and PID control while a 7-inch touchscreen provides the user interface. Motion control is split across three RP2040-Zero boards, each driving a stepper motor through TMC2209 drivers. The project implements a custom I2C packet protocol and a dedicated synchronization signal so the three motors start in lockstep, according to the article.
Editorial analysis - technical context
Using an angled mirror to create a virtual increase in camera distance is a low-cost optical trick that trades optical complexity for mechanical or enclosure size. Projects that need wider scene coverage but must remain compact often face trade-offs among sensor selection, optics cost, and system size. Optical folding with mirrors can be especially attractive for prototyping where distortion and alignment are manageable.
Context and significance
For practitioners, MirrorBallBot is a clear example of pragmatic system integration: commodity vision hardware, microcontroller-based motor control, and simple control algorithms (PID, inverse kinematics) are combined with a physical optics trick to solve a spatial-coverage problem. The build documents practical issues relevant to robotics hobbyists and educators, including synchronization of multiple motor drivers and a lightweight inter-board communications approach.
What to watch
For builders, replication questions include mirror alignment tolerances, latency introduced by the vision pipeline, and how the custom I2C protocol handles error recovery and step synchronization under load. Observers interested in compact robot perception should watch for follow-up documentation or code that details calibration and timing choices.
Key Points
- 1An angled mirror folds the optical path to expand a compact camera's effective field of view by about 60 percent, letting a small robot perceive a larger platform without moving the camera far away.
- 2Compute is split: a Raspberry Pi 4 runs vision, inverse kinematics, and PID control, while three RP2040-Zero boards handle timing-sensitive stepper pulses via TMC2209 drivers over a custom I2C sync protocol.
- 3Editorial analysis: Combining commodity cameras, PID control, and a simple optical trick makes MirrorBallBot a practical template for teaching real-time robotics and computer-vision integration.
Scoring Rationale
This is a well-executed DIY maker project with practical value for robotics hobbyists and educators rather than a frontier research advance. Its computer-vision and integration details, including the mirror optical trick, multi-board motor synchronization, and PID control, are useful for practitioners building compact robotics prototypes. Relevance to AI/ML practitioners is modest and centered on classic computer vision and control.
Sources
Public references used for this report.
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