Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

Zimmermann, Martin; Vu, Minh Nhat; Beck, Florian; Nguyen, A. D.; Kugi, Andreas

doi:10.48550/arxiv.2211.06377

Cited by 1 publication

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“…This aspect is even more important when considering where these calculations must take place. As a matter of fact, the majority of the current literature addresses the problem of trajectory planning for semi-autonomous systems, i.e., all computations are not carried out on the onboard electronics but heavily exploit external workstations and motion capture systems (mocap) [12][13][14][15]. The latter approach has two main drawbacks: (i) it is not reproducible in an open environment, since the acquisition relies on a system of cameras and sensors properly set to track the motion; and (ii) it does not take into account the real computing power available onboard a vehicle, which is limited.…”

Section: Introductionmentioning

confidence: 99%

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Gelli,

Bigazzi,

Boni

et al. 2024

Drones

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In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.

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Section: Introductionmentioning

confidence: 99%