Mapping Unknown Environments With Instrumented Honey Bees

Recent innovations in miniature sensors are driving a shift from robotic to bio-hybrid systems for exploration of unstructured environments. The ubiquity of honey bees in modern agriculture and ecology along with their superior agility, olfactory sense, and collective foraging skills make them a promising complement to traditional robots. This paper explores the potential of such systems based on a custom honey bee foraging simulator and models of state-of-the art miniature flight recorders which can measure solar heading at regular time intervals, as well as exploratory data collected from the sensor mounted on an autonomous quadrotor. The size and functionality of the sensor is heavily influenced by its memory footprint, therefore, we investigate the impact of sensor sampling time on map accuracy. Our results indicate that a sampling rate down to 5Hz can be used to sense obstacle locations in a 5-acre field with an accuracy corresponding to 70% of the obstacle radius, and within 4% of its true area. This technique shows promise for using instrumented honey bees to map and monitor unstructured environments which are difficult or costly for robots to robustly navigate, monitor, and map.

I. Introduction

Mapping unknown environments is necessary for applications ranging from exploration to search and rescue, and has been a major focus of robotics research since 1980 [1]. While the methods vary, algorithms depend heavily on high quality sensor data to produce accurate maps [2]. Obtaining such data is difficult, as robots mapping unstructured environments depend on sophisticated locomotion mechanisms and multi-modal sensing. Furthermore, monitoring large dy-namic environments typically requires several robots to work in parallel. For example, recent research on mapping has involved quadrotors with multiple LiDARS, thermal cameras, and inertial sensors, as well as ground robots with RGB-D cameras and 2D laser scanners [3]–[7].