Lima, Rogerio (West Virginia University - WVU), Pereira, Guilherme (West Virginia University)

Drone Collision Detection and Classification Using Proprioceptive Data

Scheduled for presentation during the Regular Session "Perception and Cognition" (ThA2), Thursday, June 23, 2022, 11:10−11:30, Bokar

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 17, 2024

Abstract

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.