Probabilistic Weather Forecasting Analysis for Unmanned Aerial Vehicle Path Planning

Zhang, Bin; Tang, Liang; Roemer, Michael

doi:10.2514/1.61651

Cited by 45 publications

(16 citation statements)

References 17 publications

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“…Therefore, its evolutionary encoding has a different interpretation and is customized for HAPSs instead of satellites or other types of UAVs. Besides, similar to other works that take into account the uncertainty associated to weather conditions [ 44 , 45 , 46 ] or to other elements of the mission (e.g., the target location and movement in search and rescue missions [ 47 , 48 ] or the probability of target detection and destruction in hostile environments [ 49 , 50 ]), in this work the uncertainties are incorporated into the models used to evaluate how probable is that each HAPS is at a mission area at a given time, which affects the outcome of the objective and constraint values.…”

Section: Related Worksupporting

confidence: 54%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

Sensors

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Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

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Section: Related Worksupporting

confidence: 54%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

Sensors

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“…This planning, as shown above, can generate a smooth path in which waypoints can be located on any points on the edge of grids. Since the proposed approach does not involve linear interpolation, it is valid for cost function with nonlinear factors [36]- [40].…”

Section: Experimental Scenariosmentioning

confidence: 98%

A Recursive Receding Horizon Planning for Unmanned Vehicles

Zhang

Tang

DeCastro

et al. 2015

IEEE Trans. Ind. Electron.

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This paper proposes a recursive receding horizon path planning algorithm for unmanned vehicles in nonuniform environments. In the proposed algorithm, the map is described by grids in which nodes are defined on corners of grids. The planning algorithm considers the map as four areas, namely, implementation, observation, explored, and unknown. The Implementation area is a subset of the Observation area, whereas the Explored area is the union of all the previous Observation areas. The path is planned with a receding horizon planning strategy to generate waypoints and in-between map updates. When a new map update occurs, the path is replanned within the current Observation area if necessary. If no such path exists, the search is extended to the Explored area. Paths can be planned by recursively searching available nodes inside the Explored area that can be connected to available nodes on the boundary of the Explored area. A robot platform is employed to conduct a series of experiments in a laboratory environment to verify the proposed path planning algorithm.Index Terms-Nonuniform environment, path planning, receding horizon planning (RHP), recursive searching, unmanned robot.

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“…In B. Zhang and Roemer (2014); Zhang et al (2017), the authors introduced a probabilistic analysis of weather forecasting. The analysis is based on ensemble weather forecasting data provided by the Center for Analysis and Prediction of Storm (CAPS) Jung et al (2008).…”

Section: Introductionmentioning

confidence: 99%

On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development

Hentzen

Kamgarpour

Soler

et al. 2018

Aerospace Science and Technology

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Dealing with meteorological uncertainty poses a major challenge in air traffic management (ATM). Convective weather (commonly referred to as storms or thunderstorms) in particular represents a significant safety hazard that is responsible for one quarter of weather-related ATM delays in the US. With commercial air traffic on the rise and the risk of potentially critical capacity bottlenecks looming, it is vital that future trajectory planning tools are able to account for meteorological uncertainty. We propose an approach to model the uncertainty inherent to forecasts of convective weather regions using statistical analysis of state-of-the-art forecast data. The developed stochastic storm model is tailored for use in an optimal control algorithm that maximizes the probability of reaching a waypoint while avoiding hazardous storm regions. Both the aircraft and the thunderstorms are modeled stochastically. The performance of the approach is illustrated and validated through simulated case studies based on recent nowcast data and storm observations.

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Probabilistic Weather Forecasting Analysis for Unmanned Aerial Vehicle Path Planning

Cited by 45 publications

References 17 publications

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

A Recursive Receding Horizon Planning for Unmanned Vehicles

On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development

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