This paper proposes an approach for collision detection and obstacle classification based on the physical interaction between a drone and the environment. Our method does not require any special collision-detection sensor, since it uses only proprioceptive data (accelerations). Time-series classification of acceleration data is carried out by a deep neural network, which uses acceleration time-series to classify the drone’s flight mode into three classes: no collision, collision with a soft obstacle, and collision with a hard obstacle. Experimental results showed that the classification achieved accuracy of 98.7% on the testing set. Also, the method was used to perform on-line flight state predictions, when data is applied continuously into the neural network’s input, thus showing the potential of this method for collision detection and classification in several applications.
Drone Collision Detection and Classification using Proprioceptive Data
21.06.2022
5077039 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Onboard visual drone detection for drone chasing and collision avoidance
American Institute of Physics | 2021
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