Real-time motion planning with a fixed-wing UAV using an agile maneuver space

Levin, Josh; Nahon, Meyer; Paranjape, Aditya A.

doi:10.1007/s10514-019-09863-2

Cited by 27 publications

(16 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[22] explored real-time trajectory motion-planning of fixed-wing UAVs using simplified models restricted to low angle-of-attack domains. In [4], [23], [24], [25], [26], researchers have developed motion planners for aerobatic fixed-wing UAVs using trajectory libraries generated offline using high fidelity physics models. In some cases, especially in the presence of high winds, NMPC has been utilized to control fixed-wing UAVs [27].…”

Section: Related Workmentioning

confidence: 99%

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Polevoy¹,

Basescu²,

Scheuer³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to maneuver in constrained spaces through the use of direct nonlinear model predictive control (NMPC) [1]. However, this approach has been limited to a priori known maps and ground truth state measurements. In this paper, we present a direct NMPC approach that leverages NanoMap [2], a light-weight point-cloud mapping framework to generate collision-free trajectories using onboard stereo vision. We first explore our approach in simulation and demonstrate that our algorithm is sufficient to enable vision-based navigation in urban environments. We then demonstrate our approach in hardware using a 42-inch fixed-wing UAV and show that our motion planning algorithm is capable of navigating around a building using a minimalistic set of goal-points. We also show that storing a point-cloud history is important for navigating these types of constrained environments.

show abstract

Section: Related Workmentioning

confidence: 99%

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Polevoy¹,

Basescu²,

Scheuer³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Trajectory optimization subject to the six-degree-of-freedom (6DOF) nonlinear flight dynamics model is computationally costly, e.g., optimization of the 4.5 m knife-edge maneuver presented by [5] takes 3-5 minutes of computation time (using direct collocation with twelve states and five control inputs) according to [6]. Existing works address computational expense in various ways, e.g., by considering only (extended) point-mass equations of motion [7], [8], [9], by using a planner with pre-computed maneuvers [10], [11], by incorporating human-piloted expert demonstrations [12], or by combining multiple simplified local dynamics models [13]. In practice, these methods may impose limitations on the generated trajectories, especially when planning aerobatic maneuvers that rapidly progress through unconventional flight conditions.…”

Section: Introductionmentioning

confidence: 99%

Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness

Tal¹,

Ryou²,

Karaman³

2022

Preprint

View full text Add to dashboard Cite

This paper proposes a novel algorithm for aerobatic trajectory generation for a vertical take-off and landing (VTOL) tailsitter flying wing aircraft. The algorithm differs from existing approaches for fixed-wing trajectory generation, as it considers a realistic six-degree-of-freedom (6DOF) flight dynamics model, including aerodynamics equations. Using a global dynamics model enables the generation of aerobatics trajectories that exploit the entire flight envelope, enabling agile maneuvering through the stall regime, sideways uncoordinated flight, inverted flight etc. The method uses the differential flatness property of the global tailsitter flying wing dynamics, which is derived in this work. By performing snap minimization in the differentially flat output space, a computationally efficient algorithm, suitable for online motion planning, is obtained. The algorithm is demonstrated in extensive flight experiments encompassing six aerobatics maneuvers, a time-optimal drone racing trajectory, and an airshow-like aerobatic sequence for three tailsitter aircraft. SUPPLEMENTAL MATERIALVideo of the experiments can be found at https://aera.mit. edu/projects/TailsitterAerobatics.

show abstract

“…Mueller and D'Andrea [15] proposed a framework for the efficient calculation of motion primitives. McGill University has presented a series of representative works based on the idea of motion primitives and maneuver optimization [37][38][39]. Dynamic motion primitives (DMPs) is a typical primitive which has been successfully utilized in the design of car driving motion libraries [40].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

et al. 2021

View full text Add to dashboard Cite

Performing autonomous maneuvering flight planning and optimization remains a challenge for unmanned aerial vehicles (UAVs), especially for fixed-wing UAVs due to its high maneuverability and model complexity. A novel hybrid-driven fixed-wing UAV maneuver optimization framework, inspired by apprenticeship learning and nonlinear programing approaches, is proposed in this paper. The work consists of two main aspects: (1) Identifying the model parameters for a certain fixed-wing UAV based on the demonstrated flight data performed by human pilot. Then, the features of the maneuvers can be described by the positional/attitude/compound key-frames. Eventually, each of the maneuvers can be decomposed into several motion primitives. (2) Formulating the maneuver planning issue into a minimum-time optimization problem, a novel nonlinear programming algorithm was developed, which was unnecessary to determine the exact time for the UAV to pass by the key-frames. The simulation results illustrate the effectiveness of the proposed framework in several scenarios, as both the preservation of geometric features and the minimization of maneuver times were ensured.

show abstract

Real-time motion planning with a fixed-wing UAV using an agile maneuver space

Cited by 27 publications

References 36 publications

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

Contact Info

Product

Resources

About