  The goal of this paper is to develop a continuous optimization-based
refinement of the reference trajectory to 'push it out' of the
obstacle-occupied space in the global phase for Multi-rotor Aerial Vehicles in
unknown environments. Our proposed approach comprises two planners: a global
planner and a local planner. The global planner refines the initial reference
trajectory when the trajectory goes either through an obstacle or near an
obstacle and lets the local planner calculate a near-optimal control policy.
The global planner comprises two convex programming approaches: the first one
helps to refine the reference trajectory, and the second one helps to recover
the reference trajectory if the first approach fails to refine. The global
planner mainly focuses on real-time performance and obstacles avoidance,
whereas the proposed formulation of the constrained nonlinear model predictive
control-based local planner ensures safety, dynamic feasibility, and the
reference trajectory tracking accuracy for low-speed maneuvers, provided that
local and global planners have mean computation times 0.06s (15Hz) and 0.05s
(20Hz), respectively, on an NVIDIA Jetson Xavier NX computer. The results of
our experiment confirmed that, in cluttered environments, the proposed approach
outperformed three other approaches: sampling-based pathfinding followed by
trajectory generation, a local planner, and graph-based pathfinding followed by
trajectory generation.
