Dynamically Feasible Motion Planning for Micro Air Vehicles Using an Egocylinder

Abstract

Onboard obstacle avoidance is a challenging, yet indespensible component of micro air vehicle (MAV) autonomy. Prior approaches for deliberative motion planning over vehicle dynamics typically rely on 3-D voxel-based world models, which require complex access schemes or extensive memory to manage resolution and maintain an acceptable motion-planning horizon. In this paper, we present a novel, lightweight motion planning method, for micro air vehicles with full configuration flat dynamics, based on perception with stereo vision and a 2.5-D egocylinder obstacle representation. We equip the egocylinder with temporal fusion to enhance obstacle detection and provide a rich, 360$^{\circ }$ representation of the environment well beyond the visible field-of-regard of a stereo camera pair. The natural pixel parameterization of the egocylinder is used to quickly identify dynamically feasible maneuvers onto radial paths, expressed directly in egocylinder coordinates, that enable finely detailed planning at extreme ranges within milliseconds. We have implemented our obstacle avoidance pipeline with an Asctec Pelican quadcopter, and demonstrate the efficiency of our approach experimentally with a set of challenging field scenarios.