Trajectory optimization under kinematical constraints for moving target search

Raap, Manon; Zsifkovits, Martin; Pickl, Stefan

doi:10.1016/j.cor.2016.12.016

Cited by 17 publications

(5 citation statements)

References 11 publications

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“…Most publications assume a stationary object, even if the target is a person or an animal, because the speed of the object is usually much slower than that of the drone. In the case of a moving object, the probability map evolves over time . Due to imperfect sensors, a drone detects an object with some probability

π < 1

if it visits the cell where the object is located , so it may make sense to visit some cells several times.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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π < 1

if it visits the cell where the object is located , so it may make sense to visit some cells several times.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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“…Aircraft trajectory optimization has been widely addressed in the literature (Betts, 1998;Delahaye et al, 2014). This problem has been studied for different types of aircraft: airplanes (Hagelauer and Mora-Camino, 1998;Sridhar et al, 2011;Rodionova et al, 2014), rotorcraft (Guntzer et al, 2014;Morris et al, 2016) and more recently UAVs (Unmanned Aerial Vehicles) (Raap et al, 2017;Coelho et al, 2017;Dasdemir et al, 2020). Several objectives to be minimized are considered, such as fuel consumption and operational costs (Hagelauer and Mora-Camino, 1998), traffic congestion (Rodionova et al, 2014), contrails formation (Sridhar et al, 2011) and noise annoyance (Prats et al, 2010;Guntzer et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In our work, both the horizontal and vertical paths, along with speed, are degrees of freedom in the optimization. As regards to the solution of aircraft trajectory optimization problems, many different approaches can be found in the literature, including integer linear programming (Raap et al, 2017), optimal control (Prats et al, 2010;Sridhar et al, 2011) and heuristic methods (Rodionova et al, 2014;Dasdemir et al, 2020). For the considered problem of rotorcraft noise abatement trajectory design, population-based heuristic methods are typically used (Padula et al, 2009;Guntzer et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Rotorcraft low-noise trajectories design: black-box optimization using surrogates

et al. 2023

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This paper addresses the noise-minimal trajectory optimization problem for a specific type of aircraft: rotorcraft. It relies on a realistic noise footprint computation software provided by industry that is black-box. Locally optimal trajectories are computed through a tailored solution approach based on the Mesh-Adaptive Direct Search algorithm. We propose multiple surrogates defined according to our knowledge of the problem, including a surrogate relying on the physics of the problem (approximating the rotorcraft noise model), and another based on a machine learning (neural network) method. The proposed solution approach is further enhanced by the computation of an appropriate starting guess through a path planning algorithm tailored to the problem, and by the reduction of the variable space domain. The performance of the proposed methodology both in terms of quality of the solutions (trajectories exhibiting significant noise reduction compared to those currently flown in practice) and computing time is illustrated through numerical experiments on realworld case studies.

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“…Until now, a large number of related studies have been proposed. For example, Raap et al [9] proposed a novel model to achieve search-trajectory planning for a single dynamic target. Kilic and Mostarda [10] proposed a new framework for path planning by optimizing the charging station grid to use a single UAV to reach multiple static ships.…”

Section: Introductionmentioning

confidence: 99%

An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather

Huang

et al. 2023

JMSE

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The international trade heavily relies on maritime transportation. Due to the vastness of the ocean, once an accident happens, fast maritime search and rescue (MSR) is a must, as it is of life-and-death matter. Using unmanned air vehicles (UAVs) is an effective approach to completing complex MSR tasks, especially when the environment is dangerous and changeable. However, how to effectively plan paths for multi-UAVs under severe weather, e.g., to rescue the most urgent targets in the shortest time, is a challenging task. In this study, an improved NSGA-II based on multi-task optimization (INSGA-II-MTO) is proposed to plan paths for multi-UAVs in the MSR tasks. In the INSGA-II-MTO, a novel population initialization method is proposed to improve the diversity of an initial population. Further, two tasks are introduced during the execution of the search algorithm. Namely, one assistant task, which solves a simplified MSR problem through multi-task optimization, is implemented to provide necessary evolutional knowledge to a main task that solves an original MSR problem. The performance of the proposed INSGA-II-MTO is compared with other competitors in three MSR scenarios. Experimental results indicate that the proposed algorithm performs best among the compared ones. It is observed that the INSGA-II-MTO can find a set of shorter total paths and handle the most urgent task in the shortest possible time. Therefore, the proposed method is an effective and promising approach to solving multi-UAVs MSR problems to reduce human causalities and property losses.

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Trajectory optimization under kinematical constraints for moving target search

Cited by 17 publications

References 11 publications

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Rotorcraft low-noise trajectories design: black-box optimization using surrogates

An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather

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