Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones

• URL: http://arxiv.org/abs/2407.02405v1
• Date: Tue, 2 Jul 2024 16:24:57 GMT
• Title: Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones
• Authors: Lorenzo Lamberti, Vlad Niculescu, Michał Barcis, Lorenzo Bellone, Enrico Natalizio, Luca Benini, Daniele Palossi,
• Abstract summary: This work moves from PULP-Dronet, a State-of-the-Art convolutional neural network for autonomous navigation on nano-drones, to Tiny-PULP-Dronet, a novel methodology to squeeze by more than one order of magnitude model size. This massive reduction paves the way towards affordable multi-tasking on nano-drones, a fundamental requirement for achieving high-level intelligence.
• Score: 12.96119439129453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pocket-sized autonomous nano-drones can revolutionize many robotic use cases, such as visual inspection in narrow, constrained spaces, and ensure safer human-robot interaction due to their tiny form factor and weight -- i.e., tens of grams. This compelling vision is challenged by the high level of intelligence needed aboard, which clashes against the limited computational and storage resources available on PULP (parallel-ultra-low-power) MCU class navigation and mission controllers that can be hosted aboard. This work moves from PULP-Dronet, a State-of-the-Art convolutional neural network for autonomous navigation on nano-drones. We introduce Tiny-PULP-Dronet: a novel methodology to squeeze by more than one order of magnitude model size (50x fewer parameters), and number of operations (27x less multiply-and-accumulate) required to run inference with similar flight performance as PULP-Dronet. This massive reduction paves the way towards affordable multi-tasking on nano-drones, a fundamental requirement for achieving high-level intelligence.

Related papers

• Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW [52.280742520586756]
  Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. We present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones.
  arXiv  Detail & Related papers  (2024-08-06T13:11:36Z)
• On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors [53.59319391812798]
  Sub-SI50gram nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception. When deployed in unknown environments, these models often underperform due to domain shift. We propose for the first time, on-device learning aboard nano-drones, where the first part of the in-field mission is dedicated to self-supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-03-06T22:04:14Z)
• Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones [9.806742394395322]
  Miniaturized unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. This work proposes a new automatic optimization pipeline for visual pose estimation tasks using Deep Neural Networks (DNNs) Our results improve the state-of-the-art reducing inference latency by up to 3.22x at iso-error.
  arXiv  Detail & Related papers  (2024-02-23T11:35:57Z)
• High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks [51.23613834703353]
  Relative drone-to-drone localization is a fundamental building block for any swarm operations. We present a vertically integrated system based on a novel vision-based fully convolutional neural network (FCNN) Our model results in an R-squared improvement from 32 to 47% on the horizontal image coordinate and from 18 to 55% on the vertical image coordinate, on a real-world dataset of 30k images.
  arXiv  Detail & Related papers  (2024-02-21T12:34:31Z)
• Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones [5.382126081742012]
  We present a novel adaptive deep learning-based mechanism for the efficient execution of a vision-based human pose estimation task. On a real-world dataset and the actual nano-drone hardware, our best-performing system shows 28% latency reduction while keeping the same mean absolute error (MAE), 3% MAE reduction while being iso-latency, and the absolute peak performance, i.e., 6% better than SoA model.
  arXiv  Detail & Related papers  (2024-01-26T23:04:26Z)
• Multi-model fusion for Aerial Vision and Dialog Navigation based on human attention aids [69.98258892165767]
  We present an aerial navigation task for the 2023 ICCV Conversation History. We propose an effective method of fusion training of Human Attention Aided Transformer model (HAA-Transformer) and Human Attention Aided LSTM (HAA-LSTM) models.
  arXiv  Detail & Related papers  (2023-08-27T10:32:52Z)
• Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones [9.967643080731683]
  This paper presents a lightweight CNN depth estimation network deployed on nano drones for obstacle avoidance. Inspired by Knowledge Distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network. The proposed method is validated on the KITTI dataset and tested on a nano drone Crazyflie, with an ultra-low power microprocessor GAP8.
  arXiv  Detail & Related papers  (2023-03-18T10:45:34Z)
• Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs [69.19616451596342]
  Miniaturized unmanned aerial vehicles (UAVs) are an emerging and trending topic. We leverage a novel neural architecture search (NAS) technique to automatically identify several convolutional neural networks (CNNs) for a visual pose estimation task. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of 10Hz@10mW and 50Hz@90mW.
  arXiv  Detail & Related papers  (2023-03-03T14:02:09Z)
• Gesture Control of Micro-drone: A Lightweight-Net with Domain Randomization and Trajectory Generators [0.0]
  This study presents a computationally-efficient deep convolutional neural network that utilizes Gabor filters and spatial separable convolutions. The model aids a human operator in controlling a micro-drone via gestures. Using a low-cost DJI Tello drone for experiment verification, the computationally-efficient model shows promising results.
  arXiv  Detail & Related papers  (2023-01-29T15:38:15Z)
• Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing [52.50284630866713]
  Existing systems often require hand-engineered components for state estimation, planning, and control. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
  arXiv  Detail & Related papers  (2022-10-26T19:03:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.