Autonomous UAVs landing site selection from point cloud in unknown environments

Yang, Linjie; Wang, Chenglong; Wang, Luping

doi:10.1016/j.isatra.2022.04.005

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…The system detects the candidate region with the biggest area and then it estimates the degree of similarity between the reference region and the corresponding ones. In [12] the authors present a complete modular framework for landing site selection, which comprises a point cloud prepossessing module, a coarse landing site selection module, a fine terrain evaluation module and a landing optimal model. The pipeline was designed to achieve real-time performance.…”

Section: Related Workmentioning

confidence: 99%

Embedded light-weight approach for safe landing in populated areas

Mitroudas¹,

Balaska²,

Psomoulis³

et al. 2023

Preprint

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Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.

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Section: Related Workmentioning

confidence: 99%

Embedded light-weight approach for safe landing in populated areas

Mitroudas¹,

Balaska²,

Psomoulis³

et al. 2023

Preprint

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“…One crucial aspect contributing to this safety is the detection of emergency landing sites. The motivation behind creating this dataset comes from a noticeable gap in the existing literature, the lack of a specialized dataset specifically crafted for identifying emergency landing sites for aircrafts in distress unexpected situations, as most safe landing zone detection related literature focus majorly on detecting landing areas for autonomous UAVs landings [ 7 , 8 ]. Our dataset has been meticulously curated to highlight landscapes that are suitable for fixed-wing aircraft, addressing this critical need in aviation safety.…”

Section: Introductionmentioning

confidence: 99%

A pixel-wise labelled dataset of Moroccan aircraft emergency landing sites for semantic segmentation applications

ILLI,

Bouzaachane,

El Hadaj

et al. 2024

Data in Brief

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“…The risk-state MPC is presented based on the landing risk gradient. In [7], a framework is proposed for landing in an uncertain environment based on point cloud in a coarse to fine manner. It has four modules: preprocessing point cloud, selection of course landing site, evaluation of fine terrain, and optimal landing model.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Landing of an UAV Using H∞ Based Model Predictive Control

Latif

Shahzad²,

Bhatti

et al. 2022

Drones

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Possibly the most critical phase of an Unmanned Air Vehicle (UAV) flight is landing. To reduce the risk due to pilot error, autonomous landing systems can be used. Environmental disturbances such as wind shear can jeopardize safe landing, therefore a well-adjusted and robust control system is required to maintain the performance requirements during landing. The paper proposes a loop-shaping-based Model Predictive Control (MPC) approach for autonomous UAV landings. Instead of conventional MPC plant model augmentation, the input and output weights are designed in the frequency domain to meet the transient and steady-state performance requirements. Then, the H∞ loop shaping design procedure is used to synthesize the state-feedback controller for the shaped plant. This linear state-feedback control law is then used to solve an inverse optimization problem to design the cost function matrices for MPC. The designed MPC inherits the small-signal characteristics of the H∞ controller when constraints are inactive (i.e., perturbation around equilibrium points that keep the system within saturation limits). The H∞ loop shaping synthesis results in an observer plus state feedback structure. This state estimator initializes the MPC problem at each time step. The control law is successfully evaluated in a non-linear simulation environment under moderate and severe wind downburst. It rejects unmeasured disturbances, has good transient performance, provides an excellent stability margin, and enforces input constraints.

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Autonomous UAVs landing site selection from point cloud in unknown environments

Cited by 9 publications

References 30 publications

Embedded light-weight approach for safe landing in populated areas

Embedded light-weight approach for safe landing in populated areas

A pixel-wise labelled dataset of Moroccan aircraft emergency landing sites for semantic segmentation applications

Autonomous Landing of an UAV Using H∞ Based Model Predictive Control

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