  Adaptive autonomous navigation with no prior knowledge of extraneous
disturbance is of great significance for quadrotors in a complex and unknown
environment. The mainstream that considers external disturbance is to implement
disturbance-rejected control and path tracking. However, the robust control to
compensate for tracking deviations is not well-considered regarding energy
consumption, and even the reference path will become risky and intractable with
disturbance. As recent external forces estimation advances, it is possible to
incorporate a real-time force estimator to develop more robust and safe
planning frameworks. This paper proposes a systematic (re)planning framework
that can resiliently generate safe trajectories under volatile conditions.
Firstly, a front-end kinodynamic path is searched with force-biased motion
primitives. Then we develop a nonlinear model predictive control (NMPC) as a
local planner with Hamilton-Jacobi (HJ) forward reachability analysis for error
dynamics caused by external forces. It guarantees collision-free by
constraining the ellipsoid of the quadrotor body expanded with the forward
reachable sets (FRSs) within safe convex polytopes. Our method is validated in
simulations and real-world experiments with different sources of external
forces.
