Trajectory Optimization and Replanning Framework for a Micro Air Vehicle in Cluttered Environments

Abstract

In the present study, we propose a trajectory optimization and replanning algorithm for micro air vehicles (MAVs) in cluttered environments. To generate the path of an MAV in a cluttered environment, we first design an offline global path optimization algorithm. This algorithm generates a global trajectory for safe aerial delivery; this trajectory enables an MAV to avoid static obstacles marked in the navigation map and satisfies the MAV's initial and arrival velocities. The MAV's trajectory is replanned by exploiting dynamic movement primitives (DMPs) and a time adjustment algorithm to enable computationally efficient unknown obstacle avoidance in local path planning. To validate the applicability of the proposed algorithm, we compare simulation results with those obtained using an existing approach based on DMPs. Furthermore, an autonomous flight is demonstrated in an outdoor environment using a custom-made MAV driven by the proposed approach.