The OptiState framework can be extended to handle more complex robot dynamics by incorporating advanced modeling techniques and sensor fusion strategies. For multi-body systems, the framework can be adapted to include additional state variables and dynamics equations to account for the interactions between multiple bodies. This would involve expanding the state estimation model to include the states of each body, their relative positions, and the forces acting between them. By integrating multi-body dynamics principles and constraints into the Kalman filter and GRU components, the framework can effectively estimate the states of complex robotic systems with interconnected bodies.
Moreover, for robots with flexible appendages, such as robotic arms or manipulators, the OptiState framework can be enhanced to include flexible body dynamics models. This would require incorporating flexible body dynamics equations, such as modal analysis or finite element methods, into the state estimation process. By integrating sensor measurements related to the flexibility and deformation of the appendages, such as strain sensors or tactile sensors, the framework can adapt to the varying dynamics of flexible structures. Additionally, machine learning algorithms can be trained on data from these sensors to improve the estimation of flexible body states and uncertainties.