Safe Wind-Aware Navigation for Collaborative Autonomous Aircraft in Low Altitude Airspace

(joint work with Drs. Kamalapurkar, Jacob, Kara, Vance and Fala at OSU)

  • Small unmanned aircraft systems (sUAS) technologies found many civil, commercial, and military applications.
  • Infrastructure, such as NASA UAS traffic management (UTM) for low-altitude airspace management and monitoring, is being developed.
  • Safety and efficiency of sUAS operations are strongly impacted by low-altitude gusts:
    • Negatively affect pilot operations, reduced flight time, damage
    • Airspace management and allocation made conservative and inefficient.

Our Hypothesis

‘In-time’ or ‘real-time’ wind field information, communicated effectively to pilots and traffic management, can enhance safety, efficiency, and robustness of future sUAS operations in low-altitude airspace.

Proposed Concept of Operation

Integration diagram

Simulator We built a human-in-the-loop simulator based on AirSim to evaluate operators’ performance in the presence of wind disturbances.

Simulator

Our experiments show that in a survey mission for a football stadium, operators’ performance improves if wind conditions are presented to them in a user interface.

Figure below compares the traveled distance with a basic user interface and a wind-aware user interface.

Experiments

We conducted outdoor experiments to perform dynamics-based wind velocity estimation (without a wind velocity sensor).

Yearly posters

2022 Poster

2023 Poster

2022 Poster

2021 Poster

2020 Poster

Relevant Publications

2024

  1. Urban Impacts on UAS Dynamics-based Wind Observations
    Revard, Braydon, Chen, Hao, Elbing, Brian, Jacob, Jamey, and Bai, He
    In 104th AMS Annual Meeting 2024
  2. Comparison of Nonlinear Filters for Quadcopter Wind Estimation
    Chen, Hao, Revard, Braydon, Bai, He, and Jacob, Jamey D
    In AIAA SCITECH 2024 Forum 2024

2023

  1. Wind Field Estimation Using Multiple Quadcopters
    Chen, Hao, Bai, He, and Taylor, Clark N
    IFAC-PapersOnLine 2023
  2. A Study on Workload Assessment and Usability of Wind-Aware User Interface for Small Unmanned Aircraft System Remote Operations
    Tabassum, Asma, Bai, He, and Fala, Nicoletta
    In International Conference on Human-Computer Interaction 2023
  3. Data-Driven Koopman-based Modeling and Control of Quadcopter in a Turbulent Wind Field
    Tabassum, Asma, and Bai, He
    In 2023 IEEE Conference on Control Technology and Applications (CCTA) 2023
  4. Dynamic covariance prediction using variational Wishart processes with uncertain inputs
    Uzzaman, Nahid, and Bai, He
    In 2023 American Control Conference (ACC) 2023
  5. Parametrized input inference for approximate stochastic optimal control
    Syed, Shahbaz P Qadri, and Bai, He
    In 2023 American Control Conference (ACC) 2023
    PDF

2022

  1. Invariant-EKF Design for Quadcopter Wind Estimation
    Chen, Hao, Bai, He, and Taylor, Clark N.
    In American Control Conference 2022
    PDF

2021

  1. Emulating UAV Motion by Utilizing Robotic Arm for mmWave Wireless Channel Characterization
    Kachroo, Amit, Thornton, Collin A, Sarker, Md Arifur Rahman, Choi, Wooyeol, Bai, He, Song, Ickhyun, O’Hara, John F, and Ekin, Sabit
    IEEE Transactions on Antennas and Propagation 2021
  2. Variance Reduction of Quadcopter Trajectory Tracking in Turbulent Wind
    Tabassum, Asma, Vuppala, Rohit KSS, Bai, He, and Kara, Kursat
    IFAC-PapersOnLine 2021
  3. Model-based and model-free designs for an extended continuous-time LQR with exogenous inputs
    Mukherjee, Sayak, Bai, He, and Chakrabortty, Aranya
    Systems & Control Letters 2021
  4. Extended invariant-EKF designs for state and additive disturbance estimation
    Coleman, Kevin, Bai, He, and Taylor, Clark N
    Automatica 2021
  5. Distributed Bayesian Parameter Inference for Physics-Informed Neural Networks
    Bai, He, Bhar, Kinjal, George, Jemin, and Busart, Carl
    In 2021 60th IEEE Conference on Decision and Control (CDC) 2021

2020

  1. Online Observer-Based Inverse Reinforcement Learning
    Self, Ryan, Coleman, Kevin, Bai, He, and Kamalapurkar, Rushikesh
    IEEE Control Systems Letters 2020
  2. Decentralized Langevin dynamics for Bayesian learning
    Parayil, Anjaly, Bai, He, George, Jemin, and Gurram, Prudhvi
    Advances in Neural Information Processing Systems 2020
  3. Invariant-EKF design for a unicycle robot under linear disturbances
    Coleman, Kevin, Bai, He, and Taylor, Clark N
    In 2020 American Control Conference (ACC) 2020