Robust algorithm for real-time route planning

Szczerba, Robert J.; Galkowski, Peggy; Glicktein, I.S.; Ternullo, N.J.

doi:10.1109/7.869506

Cited by 285 publications

(130 citation statements)

References 6 publications

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“…It is well known that the A* is a general AI search algorithm which is highly competitive with other path finding algorithms, and yet very easy to implement (Melchior et al, 2003;Szczerba et al, 2000;Trovato, 1996) . For UAV's trajectory generation it has the advantage to present a good performance when the terrain is quite irregular and the manoeuvres must be done close to the ground's surface.…”

Section: Trajectory Generation Using A*mentioning

confidence: 99%

UAV Trajectory Planning for Static and Dynamic Environments

Ruz¹,

Arevalo²,

Pajares³

et al. 2009

Aerial Vehicles

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Section: Trajectory Generation Using A*mentioning

confidence: 99%

UAV Trajectory Planning for Static and Dynamic Environments

Ruz¹,

Arevalo²,

Pajares³

et al. 2009

Aerial Vehicles

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“…For instance, authors have proposed estimating the cost of travelling through particular grid cells, whereby factors such as weather conditions can influence the cost value [14]. In [15], the authors present a personalized mobile traffic information system capable of handling weather information, including rainfall.…”

Section: Discussionmentioning

confidence: 99%

A Software System for the Discovery of Situations Involving Drivers in Storms

Stocker

Kauhanen

Hiirsalmi

et al. 2015

IFIP Advances in Information and Communication Technology

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Part 3: Decision Support Tools and SystemsInternational audienceWe present an environmental software system that obtains, integrates, and reasons over situational knowledge about natural phenomena and human activity. We focus on storms and driver directions. Radar data for rainfall intensity and Google Directions are used to extract situational knowledge about storms and driver locations along directions, respectively. Situational knowledge about the environment and about human activity is integrated in order to infer situations in which drivers are potentially at higher risk. Awareness of such situations is of obvious interest. We present a prototype user interface that supports adding scheduled driver directions and the visualization of situations in space-time, in particular also those in which drivers are potentially at higher risk. We think that the system supports the claim that the concept of situation is useful for the modelling of information about the environment, including human activity, obtained in environmental monitoring systems. Furthermore, the presented work shows that situational knowledge, represented by heterogeneous systems that share the concept of situation, is relatively straightforward to integrate

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“…The represented techniques of UCAV path planning are like PSO [1,2], dynamic planning [3], * algorithm [4,5], ant colony algorithm [6], genetic algorithm [7,8], and so on [9][10][11][12][13]. Reference [14] discussed sparse algorithm, another effective way, which greatly improves the efficiency of the search, but it is easy to fall into a death cycle under the conditions of lacking maneuver ability. Reference [5] adopted sparse algorithm, but angle heuristic function is not taken into consideration and the number of constraint conditions is small; the simulation result is not scientific.…”

Section: Introductionmentioning

confidence: 99%

An Improved Heuristic Algorithm for UCAV Path Planning

Kun

Kong

et al. 2017

Journal of Optimization

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The study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil field. This paper presents a new mathematical model and an improved heuristic algorithm based on Sparse * Search (SAS) for UCAV path planning problem. In this paper, flight constrained conditions will be considered to meet the flight restrictions and task demands. With three simulations, the impacts of the model on the algorithms will be investigated, and the effectiveness and the advantages of the model and algorithm will be validated.

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Robust algorithm for real-time route planning

Cited by 285 publications

References 6 publications

UAV Trajectory Planning for Static and Dynamic Environments

UAV Trajectory Planning for Static and Dynamic Environments

A Software System for the Discovery of Situations Involving Drivers in Storms

An Improved Heuristic Algorithm for UCAV Path Planning

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