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.

翻译：纳升无人机凭借手掌大小的外形尺寸，在物联网生态系统中日益重要。由于尺寸、载荷（<10克）和功耗的严格限制，其机载芯片组在面对复杂多目标任务（如安全飞行、探索、目标检测）时，实现高度自主性极具挑战，这些限制严格约束了内存与计算能力。本工作通过结合依赖飞行时间距离传感器数据的仿生导航策略与基于视觉的卷积神经网络（CNN）目标检测方法，解决了这一复杂问题。我们经过实地验证的纳升无人机配备了两个微控制器单元（MCU）：采用单核ARM Cortex-M4（STM32）处理器执行安全导航与探索策略，以及并行超低功耗八核RISC-V（GAP8）处理器进行CNN片内推理，其总功耗（包含图像传感器与外存）仅为134mW。目标检测任务在实地采集数据集上的平均精度均值达到50%（帧率1.6帧/秒）。我们比较了四种仿生探索策略，发现伪随机策略在未知约36平方米房间内3分钟飞行中取得了83%的最高覆盖率。通过结合检测CNN与探索策略，在未见环境中对六个目标物体的平均检测率达90%。