  Indoor motion planning focuses on solving the problem of navigating an agent
through a cluttered environment. To date, quite a lot of work has been done in
this field, but these methods often fail to find the optimal balance between
computationally inexpensive online path planning, and optimality of the path.
Along with this, these works often prove optimality for single-start
single-goal worlds. To address these challenges, we present a multiple waypoint
path planner and controller stack for navigation in unknown indoor environments
where waypoints include the goal along with the intermediary points that the
robot must traverse before reaching the goal. Our approach makes use of a
global planner (to find the next best waypoint at any instant), a local planner
(to plan the path to a specific waypoint), and an adaptive Model Predictive
Control strategy (for robust system control and faster maneuvers). We evaluate
our algorithm on a set of randomly generated obstacle maps, intermediate
waypoints, and start-goal pairs, with results indicating a significant
reduction in computational costs, with high accuracies and robust control.
