Fast and modular modeling of multi-legged robots (MLRs) is essential for
resilient control, particularly under significant morphological changes caused
by mechanical damage. Conventional fixed-structure models, often developed with
simplifying assumptions for nominal gaits, lack the flexibility to adapt to
such scenarios. To address this, we propose a fast modular whole-body modeling
framework using Boltzmann-Hamel equations and screw theory, in which each leg’s
dynamics is modeled independently and assembled based on the current robot
morphology. This singularity-free, closed-form formulation enables efficient
design of model-based controllers and damage identification algorithms. Its
modularity allows autonomous adaptation to various damage configurations
without manual re-derivation or retraining of neural networks. We validate the
proposed framework using a custom simulation engine that integrates contact
dynamics, a gait generator, and local leg control. Comparative simulations
against hardware tests on a hexapod robot with multiple leg damage confirm the
model’s accuracy and adaptability. Additionally, runtime analyses reveal that
the proposed model is approximately three times faster than real-time, making
it suitable for real-time applications in damage identification and recovery.

Questo articolo esplora i giri e le loro implicazioni.

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