Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp

• URL: http://arxiv.org/abs/2110.00646v2
• Date: Mon, 7 Mar 2022 14:51:40 GMT
• Title: Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp
• Authors: Marina Gonz\'alez-\'Alvarez, Julien Dupeyroux, Federico Corradi, Guido de Croon
• Abstract summary: In this paper, we propose an evolved altitude controller based on an SNN for a robotic airship. We also present an SNN-based controller architecture, an evolutionary framework for training the network in a simulated environment, and a control strategy for ameliorating the gap with reality.
• Score: 4.350434044677268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational resources to perform the required control tasks. Neuromorphic computing stands for a promising research direction for addressing such problem. By mimicking the biological process for transferring information between neurons using spikes or impulses, spiking neural networks (SNNs) allow for low power consumption and asynchronous event-driven processing. In this paper, we propose an evolved altitude controller based on an SNN for a robotic airship which relies solely on the sensory feedback provided by an airborne radar. Starting from the design of a lightweight, low-cost, open-source airship, we also present an SNN-based controller architecture, an evolutionary framework for training the network in a simulated environment, and a control strategy for ameliorating the gap with reality. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network and a linear controller. The results show an accurate tracking of the altitude command with an efficient control effort.

Related papers

• Neuromorphic Attitude Estimation and Control [17.895261339368815]
  This research presents the first neuromorphic control system using a spiking neural network (SNN) We apply this method to low-level attitude estimation and control for a quadrotor, deploying the SNN on a tiny Crazyflie. Our work shows the feasibility of performing neuromorphic end-to-end control, laying the basis for highly energy-efficient and low-latency neuromorphic autopilots.
  arXiv  Detail & Related papers  (2024-11-21T08:54:45Z)
• PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search [64.28335667655129]
  Multiple object tracking is a critical task in autonomous driving. As tracking accuracy improves, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency. In this paper, we explore the use of the neural architecture search (NAS) methods to search for efficient architectures for tracking, aiming for low real-time latency while maintaining relatively high accuracy.
  arXiv  Detail & Related papers  (2024-03-23T04:18:49Z)
• Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning [66.10854214036605]
  A central question in robotics is how to design a control system for an agile mobile robot. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour.
  arXiv  Detail & Related papers  (2023-10-17T02:40:27Z)
• Neuromorphic Control using Input-Weighted Threshold Adaptation [13.237124392668573]
  It is still challenging to replicate even basic low-level controllers such as proportional-integral-derivative (PID) controllers. We propose a neuromorphic controller that incorporates proportional, integral, and derivative pathways during learning. We demonstrate the stability of our bio-inspired algorithm with flights in the presence of disturbances.
  arXiv  Detail & Related papers  (2023-04-18T07:21:24Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• 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)
• Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors [3.7384509727711923]
  Low-level controllers are often neglected and remain outside of the neuromorphic loop. We propose a parsimonious and adjustable neuromorphic PID controller, endowed with a minimal number of 93 neurons. Our results confirm the suitability of such low-level neuromorphic controllers, ultimately with a very high update frequency.
  arXiv  Detail & Related papers  (2021-09-21T14:27:11Z)
• DikpolaSat Mission: Improvement of Space Flight Performance and Optimal Control Using Trained Deep Neural Network -- Trajectory Controller for Space Objects Collision Avoidance [0.0]
  This paper shows how the controller demonstration is carried out by having the spacecraft follow a desired path. The obstacle avoidance algorithm is built into the control features to respond spontaneously using inputs from the neural network. Multiple algorithms for optimizing flight controls and fuel consumption can be implemented using knowledge of flight dynamics in trajectory.
  arXiv  Detail & Related papers  (2021-05-30T23:35:13Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs [0.0]
  We develop spiking neural networks for controlling landings of micro air vehicles. We demonstrate that the resulting neuromorphic controllers transfer robustly from a simulation to the real world. To the best of our knowledge, this work is the first to integrate spiking neural networks in the control loop of a real-world flying robot.
  arXiv  Detail & Related papers  (2020-03-06T10:19:02Z)

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.