Animal-Inspired Agile Flight Using Optical Flow Sensing

TL;DR

This paper explores how optical flow sensing, inspired by animals like pigeons and bats, can enable agile, high-speed flight through cluttered environments in robotic systems, analyzing control laws, implementation challenges, and performance limits.

Contribution

It introduces a theory of optical flow-based control for obstacle navigation and compares it with traditional measurement-based feedback laws, addressing practical and fundamental limits.

Findings

Optical flow control laws enable effective obstacle avoidance.

Performance depends on clutter density and sensing accuracy.

Implementation challenges include sensor noise and processing speed.

Abstract

There is evidence that flying animals such as pigeons, goshawks, and bats use optical flow sensing to enable high-speed flight through forest clutter. This paper discusses the elements of a theory of controlled flight through obstacle fields in which motion control laws are based on optical flow sensing. Performance comparison is made with feedback laws that use distance and bearing measurements, and practical challenges of implementation on an actual robotic air vehicle are described. The related question of fundamental performance limits due to clutter density is addressed.