A hexapod robot can identify its damaged legs by matching its IMU-measured body orientation against a fast simulator, using a genetic algorithm with an FFT-based filter, achieving 89% scenario-level accuracy in under 10 minutes.
Snapbot v2: a reconfigurable legged robot with a camera for self configuration recognition,
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Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots
A hexapod robot can identify its damaged legs by matching its IMU-measured body orientation against a fast simulator, using a genetic algorithm with an FFT-based filter, achieving 89% scenario-level accuracy in under 10 minutes.