AeriaLPiPS: A Local Planner for Aerial Vehicles with Geometric Collision Checking

Abstract

Real-time navigation in non-trivial environments by micro aerial vehicles (MAVs) predominantly relies on modelling the MAV with idealized geometry, such as a sphere. Simplified, conservative representations increase the likelihood of a planner failing to identify valid paths. That likelihood increases the more a robot's geometry differs from the idealized version. Few current approaches consider these situations; we are unaware of any that do so using perception space representations. This work introduces the egocan, a perception space obstacle representation using line-of-sight free space estimates, and 3D Gap, a perception space approach to gap finding for identifying goal-directed, collision-free directions of travel through 3D space. Both are integrated, with real-time considerations in mind, to define a local planner module of a hierarchical navigation system. The result is Aerial Local Planning in Perception Space (AeriaLPiPS). AeriaLPiPS is shown to be capable of safely navigating a MAV with non-idealized geometry through various environments, including those impassable by traditional real-time approaches. The open source implementation of this work is available at github.com/ivaROS/AeriaLPiPS.