Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones

TL;DR

This paper presents a nano-drone system combining bio-inspired navigation and CNN-based object detection, achieving high coverage and detection rates within strict size, power, and computational constraints.

Contribution

It introduces a novel nano-drone platform with dual MCUs and a low-power CNN for autonomous exploration and object detection in constrained environments.

Findings

Object detection mean average precision of 50% at 1.6 fps

Coverage area of 83% in 36m^2 room within 3 minutes

90% detection rate of six target objects in new environments

Abstract

Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions (e.g., safe flight, exploration, object detection) is extremely challenging for the onboard chip-set due to tight size, payload (<10g), and power envelope constraints, which strictly limit both memory and computation. Our work addresses this complex problem by combining bio-inspired navigation policies, which rely on time-of-flight distance sensor data, with a vision-based convolutional neural network (CNN) for object detection. Our field-proven nano-drone is equipped with two microcontroller units (MCUs), a single-core ARM Cortex-M4 (STM32) for safe navigation and exploration policies, and a parallel ultra-low power octa-core RISC-V (GAP8) for onboard CNN inference, with a power envelope of just 134mW, including image sensors and external memories. The object detection task achieves a mean average precision of 50% (at 1.6 frame/s) on an in-field collected dataset. We compare four bio-inspired exploration policies and identify a pseudo-random policy to achieve the highest coverage area of 83% in a ~36m^2 unknown room in a 3 minutes flight. By combining the detection CNN and the exploration policy, we show an average detection rate of 90% on six target objects in a never-seen-before environment.