  Motion planners for mobile robots in unknown environments face the challenge
of simultaneously maintaining both robustness against unmodeled uncertainties
and persistent feasibility of the trajectory-finding problem. That is, while
dealing with uncertainties, a motion planner must update its trajectory,
adapting to the newly revealed environment in real-time; failing to do so may
involve unsafe circumstances. Many existing planning algorithms guarantee these
by maintaining the clearance needed to perform an emergency brake, which is
itself a robust and persistently feasible maneuver. However, such maneuvers are
not applicable for systems in which braking is impossible or risky, such as
fixed-wing aircraft. To that end, we propose a real-time robust planner that
recursively guarantees persistent feasibility without any need of braking. The
planner ensures robustness against bounded uncertainties and persistent
feasibility by constructing a loop of sequentially composed funnels, starting
from the receding horizon local trajectory's forward reachable set. We
implement the proposed algorithm for a robotic car tracking a speed-fixed
reference trajectory. The experiment results show that the proposed algorithm
can be run at faster than 16 Hz, while successfully keeping the system away
from entering any dead-end, to maintain safety and feasibility.
