Controlled Flight of an Insect-Scale Flapping-Wing Robot via Integrated Onboard Sensing and Computation

• URL: http://arxiv.org/abs/2602.08328v1
• Date: Mon, 09 Feb 2026 07:03:53 GMT
• Title: Controlled Flight of an Insect-Scale Flapping-Wing Robot via Integrated Onboard Sensing and Computation
• Abstract summary: We present a 1.29-gram aerial robot capable of hovering and tracking trajectories with solely onboard sensing and computation.<n>In a 30-second flight experiment conducted outside a motion capture system, the robot avoided obstacles and ultimately landed on a sunflower.
• Score: 16.63542876415974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aerial insects can effortlessly navigate dense vegetation, whereas similarly sized aerial robots typically depend on offboard sensors and computation to maintain stable flight. This disparity restricts insect-scale robots to operation within motion capture environments, substantially limiting their applicability to tasks such as search-and-rescue and precision agriculture. In this work, we present a 1.29-gram aerial robot capable of hovering and tracking trajectories with solely onboard sensing and computation. The combination of a sensor suite, estimators, and a low-level controller achieved centimeter-scale positional flight accuracy. Additionally, we developed a hierarchical controller in which a human operator provides high-level commands to direct the robot's motion. In a 30-second flight experiment conducted outside a motion capture system, the robot avoided obstacles and ultimately landed on a sunflower. This level of sensing and computational autonomy represents a significant advancement for the aerial microrobotics community, further opening opportunities to explore onboard planning and power autonomy.

Related papers

• Towards insect-like distributed proprioception in actuators and appendages for flapping-wing insect-scale aerial robots [42.12444005548317]
  Chirp experiments with our sensors integrated into an insect-size flapping-wing robot show accurate tracking of stroke (RMSE = 0.44 deg) and pitch (RMSE = 2.44 deg) angles.<n>The proprioceptive hinge enables collision detection, reducing the chance of permanent damage if the robot's wing collides with an object.
  arXiv  Detail & Related papers  (2026-08-22T00:20:02Z)
• Nonvisual Classification of Ground-Condition by Artificial Proprioception in an Amoeba-Inspired Autonomous Walking Robot [0.0]
  We implement artificial proprioception by integrating a three-axis accelerometer, eight foot pressure sensors, and reservoir computing (RC)<n>Even when large fluctuations in the sensor outputs are caused by dynamic motions of a four-legged robot in walking, our system can classify the ground condition, flat or rough, with high accuracy.
  arXiv  Detail & Related papers  (2026-08-06T07:19:02Z)
• Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control [38.02123507620609]
  Aerial insects exhibit highly agile maneuvers such as sharp braking, saccades, and body flips under disturbance.<n>We demonstrate insect-like saccade movements with lateral speed and acceleration of 197 centimeters per second and 11.7 meters per second square.<n>The robot can also perform saccade maneuvers under 160 centimeters per second wind disturbance and large command-to-force mapping errors.
  arXiv  Detail & Related papers  (2025-08-05T03:40:11Z)
• 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)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers [11.29285364660789]
  Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation. Previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots. In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing.
  arXiv  Detail & Related papers  (2022-09-22T14:38:10Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• Coupling Vision and Proprioception for Navigation of Legged Robots [65.59559699815512]
  We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot. We show superior performance compared to wheeled robot (LoCoBot) baselines. We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute.
  arXiv  Detail & Related papers  (2021-12-03T18:59:59Z)
• CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification [53.56290185900837]
  We propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup.
  arXiv  Detail & Related papers  (2021-10-22T15:05:37Z)
• Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments [20.246040671823554]
  This paper presents an end-to-end autonomous navigation framework for bipedal robots. A vertically-actuated Spring-Loaded Inverted Pendulum (vSLIP) model is introduced to capture the robot's coupled dynamics of planar walking and vertical walking height. A variable walking height controller is leveraged to enable the bipedal robot to maintain stable periodic walking gaits while following the planned trajectory.
  arXiv  Detail & Related papers  (2021-09-13T05:36:14Z)
• The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception [0.0]
  flapping-wing robot perception suffers from high vibration levels and abrupt movements during flight. This paper presents a perception dataset for bird-scale flapping-wing robots.
  arXiv  Detail & Related papers  (2021-01-25T19:42:13Z)
• Task-relevant Representation Learning for Networked Robotic Perception [74.0215744125845]
  This paper presents an algorithm to learn task-relevant representations of sensory data that are co-designed with a pre-trained robotic perception model's ultimate objective. Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods.
  arXiv  Detail & Related papers  (2020-11-06T07:39:08Z)

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.