Mission-Oriented Additive Manufacturing of Modular Mini-UAVs

Bronz, Murat; Tal, Ezra; Favalli, Federico; Karaman, Sertaç

doi:10.2514/6.2020-0064

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Detailed descriptions of the experimental platform, shown in Fig. 8, and the trajectory-tracking flight control system are given in [28] and [20], respectively. A video of the experiments can be found at https://aera.mit.edu/projects/TailsitterAerobatics.…”

Section: Flight Experimentsmentioning

confidence: 99%

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

Tal¹,

Ryou²,

Karaman³

2022

Preprint

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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.

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Section: Flight Experimentsmentioning

confidence: 99%

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

Tal¹,

Ryou²,

Karaman³

2022

Preprint

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“…Experiments were performed in an 18 m × 8 m indoor flight space using the tailsitter vehicle shown in Fig. 1 and described in [27]. The vehicle is 3D-printed using Onyx filament with carbon fiber reinforcement.…”

Section: A Experimental Setupmentioning

confidence: 99%

Global Trajectory-tracking Control for a Tailsitter Flying Wing in Agile Uncoordinated Flight

Tal

Karaman

2021

Aiaa Aviation 2021 Forum

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We propose a novel control law for accurate tracking of agile trajectories using a tailsitter flying wing micro unmanned aerial vehicle (UAV) that transitions between vertical take-off and landing (VTOL) and forward flight. Our global control formulation enables agile maneuvering throughout the flight envelope, including uncoordinated flight conditions with sideslip. We derive a differential flatness transform for the nonlinear tailsitter dynamics with a simplified aerodynamics model. Using this transform, the proposed controller incorporates accurate tracking of the position reference along with its temporal derivatives velocity, acceleration and jerk, as well as the yaw reference and yaw rate. The inclusion of jerk and yaw rate references through an angular velocity feedforward term increases tracking performance on agile trajectories with fast-changing accelerations. The control design is based on a simplified aerodynamics model that does not require extensive modeling of the aircraft dynamics. By applying incremental nonlinear dynamic inversion (INDI), the controller only depends on a local input-output relation to incrementally update control inputs, resulting in robustness against modeling inaccuracies. We achieve INDI with nonlinear dynamics inversion based on the derived differential flatness transform. The resulting control algorithm is extensively evaluated in flight tests, where it demonstrates accurate trajectory tracking and challenging agile maneuvers, such as uncoordinated sideways flight, aggressive transitions while turning, and differential thrust turning. Supplemental MaterialA video of the experiments can be found at https://youtu.be/tGQO-6DPT1M.

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“…Fig. 9 shows an example trajectory planned for this model, as well as results from flying the trajectory with the tailsitter aircraft described in [18].…”

Section: Tailsittermentioning

confidence: 99%

Multi-Modal Motion Planning Using Composite Pose Graph Optimization

Beyer

Balabanska

Tal

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This work presents a motion planning framework for multi-modal vehicle dynamics. An approach for transcribing cost function, vehicle dynamics, and state and control constraints into a sparse factor graph is introduced. By formulating the motion planning problem in pose graph form, the motion planning problem can be addressed using efficient optimization techniques, similar to those already widely applied in dual estimation problems, e.g., pose graph optimization for simultaneous localization and mapping (SLAM). Optimization of trajectories for vehicles under various dynamics models is demonstrated. The motion planner is able to optimize the location of mode transitions, and is guided by the pose graph optimization process to eliminate unnecessary mode transitions, enabling efficient discovery of optimized mode sequences from rough initial guesses. This functionality is demonstrated by using our planner to optimize multi-modal trajectories for vehicles such as an airplane which can both taxi on the ground or fly. Extensive experiments validate the use of the proposed motion planning framework in both simulation and real-life flight experiments using a vertical take-off and landing (VTOL) fixed-wing aircraft that can transition between hover and horizontal flight modes.

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Mission-Oriented Additive Manufacturing of Modular Mini-UAVs

Cited by 11 publications

References 10 publications

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

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

Global Trajectory-tracking Control for a Tailsitter Flying Wing in Agile Uncoordinated Flight

Multi-Modal Motion Planning Using Composite Pose Graph Optimization

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