Real-time Motion Planning of Curvature Continuous Trajectories for Urban UAV Operations in Wind

Patrikar, Jay; Dugar, Vishal; Arcot, Vaibhav; Scherer, Sebastian

doi:10.1109/icuas48674.2020.9213837

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Computational studies of urban wind fields have demonstrated that turbulent wind can significantly impact the safety of drone operations [6], especially near buildings where gusts and wakes dominate [7]. To mitigate wind effects, prior work has investigated using CFD-based wind simulations for path planning [8][9][10] as well as for assessing control design methodologies (e.g., evaluating station-keeping performance near buildings [11]). Recently, micro weather and wind data providers have begun to provide mission planning and support services for drones and future urban air mobility vehicles operating in urban environments [12,13]; however, such data services are not yet widely adopted.…”

Section: Related Workmentioning

confidence: 99%

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Kakavitsas,

Willis,

Jacobik

et al. 2024

Preprint

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This paper presents a software pipeline that enables simulating a quadrotor’s flight in realistic urban wind fields where complex wind phenomena are common and have significant impact on vehicle dynamics. The pipeline integrates the OpenStreetMap database for obtaining real-world building geometry, the OpenFOAM computational fluid dynamics (CFD) solver for computing a three-dimensional, steady, wind field, the Gazebo robotics simulation environment, and the PX4 software-in-the-loop autopilot. A 3D wind plugin is developed to interpolate a pre-computed CFD wind field at runtime during the simulation. The approach is demonstrated by comparing the flight performance of a quadrotor flying over a university campus environment with and without the wind field.

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Section: Related Workmentioning

confidence: 99%

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Kakavitsas,

Willis,

Jacobik

et al. 2024

Preprint

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“…Planning under Uncertainty: Obstacle inflation is often used to achieve safe planning under uncertainty [10]. In practice, many works hand-define static safety buffers [4], [11], [12] to generate safe trajectories, however an expert designer has to opaquely adjust margin sizes to balance between safety and performance. Majority of the path planning work also does not explicitly address external disturbance [10], [13]- [16].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance

Ho,

Patrikar,

Bonatti

et al. 2021

Preprint

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Safe navigation in real time is challenging because engineers need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common safety strategy is to inflate obstacles by hand-defined margins. However, arbitrary static margins often fail in more dynamic scenarios, and using worst-case assumptions is overly conservative for most settings where disturbances over time. In this work, we propose a middle ground: safety margins that adapt on-the-fly. In an offline phase, we use Monte Carlo simulations to pre-compute a library of safety margins for multiple levels of disturbance uncertainties. Then, at runtime, our system estimates the current disturbance level to query the associated safety margins that best trades off safety and performance. We validate our approach with extensive simulated and real-world flight tests. We show that our adaptive method significantly outperforms static margins, allowing the vehicle to operate up to 1.5 times faster than worstcase static margins while maintaining safety. Video: https://youtu.be/SHzKHSUjdUU

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“…An overview of the impact of external stimuli such as wind gusts on sUAV operations is provided in [18]. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation [19]. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Decentralized Reinforcement Learning-Based Multi-Quadrotor Flocking

et al. 2021

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Enabling coordinated motion of multiple quadrotors is an active area of research in the field of small unmanned aerial vehicles (sUAVs). While there are many techniques found in the literature that address the problem, these studies are limited to simulation results and seldom account for wind disturbances. This paper presents the experimental validation of a decentralized planner based on multi-objective reinforcement learning (RL) that achieves waypoint-based flocking (separation, velocity alignment, and cohesion) for multiple quadrotors in the presence of wind gusts. The planner is learned using an object-focused, greatest mass, state-action-reward-state-action (OF-GM-SARSA) approach. The Dryden wind gust model is used to simulate wind gusts during hardware-in-the-loop (HWIL) tests. The hardware and software architecture developed for the multi-quadrotor flocking controller is described in detail. HWIL and outdoor flight tests results show that the trained RL planner can generalize the flocking behaviors learned in training to the real-world flight dynamics of the DJI M100 quadrotor in windy conditions.

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Real-time Motion Planning of Curvature Continuous Trajectories for Urban UAV Operations in Wind

Cited by 8 publications

References 29 publications

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance

Implementation of Decentralized Reinforcement Learning-Based Multi-Quadrotor Flocking

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