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A Risk-Aware Path Planning Strategy for UAVs in Urban Environments
by
Rizzo, Alessandro
, Guglieri, Giorgio
, Primatesta, Stefano
in
Algorithms
/ Artificial Intelligence
/ Computer simulation
/ Control
/ Design parameters
/ Drone aircraft
/ Electrical Engineering
/ Engineering
/ Flight operations
/ Mechanical Engineering
/ Mechatronics
/ Path planning
/ Product design
/ Risk analysis
/ Robotics
/ Simulation
/ Strategy
/ Unmanned aerial vehicles
/ Urban environments
/ Urban planning
2019
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A Risk-Aware Path Planning Strategy for UAVs in Urban Environments
by
Rizzo, Alessandro
, Guglieri, Giorgio
, Primatesta, Stefano
in
Algorithms
/ Artificial Intelligence
/ Computer simulation
/ Control
/ Design parameters
/ Drone aircraft
/ Electrical Engineering
/ Engineering
/ Flight operations
/ Mechanical Engineering
/ Mechatronics
/ Path planning
/ Product design
/ Risk analysis
/ Robotics
/ Simulation
/ Strategy
/ Unmanned aerial vehicles
/ Urban environments
/ Urban planning
2019
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Do you wish to request the book?
A Risk-Aware Path Planning Strategy for UAVs in Urban Environments
by
Rizzo, Alessandro
, Guglieri, Giorgio
, Primatesta, Stefano
in
Algorithms
/ Artificial Intelligence
/ Computer simulation
/ Control
/ Design parameters
/ Drone aircraft
/ Electrical Engineering
/ Engineering
/ Flight operations
/ Mechanical Engineering
/ Mechatronics
/ Path planning
/ Product design
/ Risk analysis
/ Robotics
/ Simulation
/ Strategy
/ Unmanned aerial vehicles
/ Urban environments
/ Urban planning
2019
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A Risk-Aware Path Planning Strategy for UAVs in Urban Environments
Journal Article
A Risk-Aware Path Planning Strategy for UAVs in Urban Environments
2019
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Overview
This paper presents a risk-aware path planning strategy for Unmanned Aerial Vehicles in urban environments. The aim is to compute an effective path that minimizes the risk to the population, thus enforcing safety of flight operations over inhabited areas. To quantify the risk, the proposed approach uses a risk-map that associates discretized locations of the space with a suitable risk-cost. Path planning is performed in two phases: first, a tentative path is computed off-line on the basis on the information related to static risk factors; then, using a dynamic risk-map, an on-line path planning adjusts and adapts the off-line path to dynamically arising conditions. Off-line path planning is performed using riskA*, an ad-hoc variant of the A* algorithm, which aims at minimizing the risk. While off-line path planning has no stringent time constraints for its execution, this is not the case for the on-line phase, where a fast response constitutes a critical design parameter. We propose a novel algorithm called
Borderland
, which uses the
check and repair
approach to rapidly identify and adjust only the portion of path involved by the inception of relevant dynamical changes in the risk factor. After the path planning, a smoothing process is performed using Dubins curves. Simulation results confirm the suitability of the proposed approach.
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