Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2306.04781v1
• Date: Wed, 7 Jun 2023 21:02:20 GMT
• Title: Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach
• Abstract summary: We present a novel multi-robot controller to navigate in turbulent flows. Our method does not learn to compensate for the air-flow at a specific time and space. This is made possible via a deep reinforcement learning approach, implemented via a Graph Convolutional Neural Network (GCNN)-based architecture.
• Score: 35.48454200357273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in turbulent flows, decoupling the trajectory-tracking control from the turbulence compensation via a nested control architecture. Unlike previous works, our method does not learn to compensate for the air-flow at a specific time and space. Instead, our method learns to compensate for the flow based on its effect on the team. This is made possible via a deep reinforcement learning approach, implemented via a Graph Convolutional Neural Network (GCNN)-based architecture, which enables robots to achieve better wind compensation by processing the spatial-temporal correlation of wind flows across the team. Our approach scales well to large robot teams -- as each robot only uses information from its nearest neighbors -- , and generalizes well to robot teams larger than seen in training. Simulated experiments demonstrate how information sharing improves turbulence compensation in a team of aerial robots and demonstrate the flexibility of our method over different team configurations.

Related papers

• Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning [7.276372008305617]
  This paper presents an online learning algorithm for controlling the complex airflow field in a multi-fan vertical wind tunnel.<n>We show that our algorithm can produce an airflow profile specifically designed for passive soaring, greatly enhancing flight performance of a soaring robot.
  arXiv  Detail & Related papers  (2026-08-04T09:24:07Z)
• AI-Enhanced Automatic Design of Efficient Underwater Gliders [60.45821679800442]
  Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions. We introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes. Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model.
  arXiv  Detail & Related papers  (2025-04-30T23:55:44Z)
• HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments [8.974071308749007]
  We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture. Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths. We propose a structured framework to learn robot navigation policies with reinforcement learning.
  arXiv  Detail & Related papers  (2024-11-19T00:56:35Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [80.86089324742024]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem. CNN processes localized perception; a graph neural network (GNN) facilitates robot communications. Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• 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)
• 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)
• Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower [30.032847855193864]
  blowing controller must continually adapt to unexpected changes from its actions. We introduce a multi-frequency version of the spatial action maps framework. This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control.
  arXiv  Detail & Related papers  (2022-04-05T17:55:58Z)
• Optimizing Airborne Wind Energy with Reinforcement Learning [0.0]
  Reinforcement Learning is a technique that learns to associate observations with profitable actions without requiring prior knowledge of the system. We show that in a simulated environment Reinforcement Learning finds an efficient way to control a kite so that it can tow a vehicle for long distances.
  arXiv  Detail & Related papers  (2022-03-27T10:28:16Z)
• Learning High-Speed Flight in the Wild [101.33104268902208]
  We propose an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments.
  arXiv  Detail & Related papers  (2021-10-11T09:43:11Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions [13.00214468719929]
  Realtime model learning is challenging for complex dynamical systems, such as drones flying in variable wind conditions. We propose an online composite adaptation method that treats outputs from a deep neural network as a set of basis functions. We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.
  arXiv  Detail & Related papers  (2021-03-02T18:43:59Z)
• Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning [4.724825031148412]
  We propose structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We demonstrate that our model outperforms previous methods in challenging crowd navigation scenarios. We successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i.
  arXiv  Detail & Related papers  (2020-11-09T23:15:31Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)

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.