P-CAL: Pre-computed Alternative Lanes for Aggressive Aerial Collision Avoidance - Robotics Institute Carnegie Mellon University

P-CAL: Pre-computed Alternative Lanes for Aggressive Aerial Collision Avoidance

Conference Paper, Proceedings of 12th International Conference on Field and Service Robotics (FSR '19), August, 2019

Abstract

We here address the issue of air vehicles flying autonomously at a high speed in complex environments. Typically, autonomous navigation through a complex environment requires a continuous heuristic search on a graph generated by a k-connected grid or a probabilistic scheme. The process is expensive especially if the paths must be kino-dynamically feasible. Aimed at tackling the problem from a different angle, we consider the case that the environment is mostly known from a prior map. The proposed method suggests the computation needed to find safe paths during fast flight can be greatly reduced if we pre-compute and carefully arrange a set of alternative paths before the flight. During the navigation, the vehicle selects a pre-computed path to navigate without the need to generate a new path. The result is that majority of the processing is migrated to offline path generation. Effectively, the onboard computation is significantly reduced, taking < 3% of a CPU thread on a modern embedded computer. In experiments, it enables a lightweight aerial vehicle to maneuver aggressively through a cluttered forest environment at 10m/s.

BibTeX

@conference{Zhang-2019-117451,
author = {Ji Zhang and Rushat Gupta Chadha and Vivek Velivela and Sanjiv Singh},
title = {P-CAL: Pre-computed Alternative Lanes for Aggressive Aerial Collision Avoidance},
booktitle = {Proceedings of 12th International Conference on Field and Service Robotics (FSR '19)},
year = {2019},
month = {August},
}