Autonomous Drone Path Planning for Environment Sensing
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초록
Recent research in animal behavior has shown that gradient information plays an important role in finding food and home. It is also important in optimization of performance because it indicates how the inputs should be adjusted for maximization/minimization of aperformance index. We introduce perturbation as an additional input to obtain gradient information. Unlike the typical approach of cal-culating the gradient from the derivative, the proposed processing is very robust to noise since it is performed as a summation. Exper- imental results prove the validity of the process of spatial gradient acquisition. Quantitative indices for measuring the effect of theamplitude and the frequency are developed based on linear regression analysis. Drones are very useful for environmental monitoringand an autonomous path planning is required for unstructured environment. Guiding the drone for finding the origin of the interested physical property is done by estimating the gradient of the sensed value and generating the drone trajectories in the direction which max-imizes the sensed value. Simulation results show that the proposed method can be successfully applied to identify the source of the phys-ical quantity of interest by utilizing it for path planning of an autonomous drone in 3D environment.
Abstract
Recent research in animal behavior has shown that gradient information plays an important role in finding food and home. It is also important in optimization of performance because it indicates how the inputs should be adjusted for maximization/minimization of aperformance index. We introduce perturbation as an additional input to obtain gradient information. Unlike the typical approach of cal-culating the gradient from the derivative, the proposed processing is very robust to noise since it is performed as a summation. Exper- imental results prove the validity of the process of spatial gradient acquisition. Quantitative indices for measuring the effect of theamplitude and the frequency are developed based on linear regression analysis. Drones are very useful for environmental monitoringand an autonomous path planning is required for unstructured environment. Guiding the drone for finding the origin of the interested physical property is done by estimating the gradient of the sensed value and generating the drone trajectories in the direction which max-imizes the sensed value. Simulation results show that the proposed method can be successfully applied to identify the source of the phys-ical quantity of interest by utilizing it for path planning of an autonomous drone in 3D environment.
참고문헌 (12)
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