TY - GEN
T1 - An Event-Based Tracking Control Framework for Multirotor Aerial Vehicles Using a Dynamic Vision Sensor and Neuromorphic Hardware
AU - Aspragkathos, Sotirios N.
AU - Ntouros, Evangelos
AU - Karras, George C.
AU - Linares-Barranco, B.
AU - Serrano-Gotarredona, T.
AU - Kyriakopoulos, Kostas J.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we present an event-based control framework for the efficient tracking of contour-based areas, such as road pavements, using a multirotor aerial vehicle equipped with a bio-inspired Dynamic Vision Sensor (DVS). Concerning the detection part, the DVS camera captures events, which are asynchronously fed into a Neuromorphic Hough Transform algorithm running on a SpiNN-3 board and implemented as a Spiking Neural Network (SNN). Next, the asynchronous output of the detection module is fed into an analytically formulated event-based Partitioned Visual Servoing (PVS) algorithm, running on conventional processing hardware, which allows the multirotor to autonomously track and navigate along the detected contour. The proposed architecture achieves efficient tracking of contour-based areas, while constantly maintaining the latter inside the DVS camera's field of view. A set of real-time experiments in various settings employing an octorotor equipped with a downward-looking DVS and a SpiNN-3 board demonstrate the effectiveness of the suggested framework.
AB - In this paper, we present an event-based control framework for the efficient tracking of contour-based areas, such as road pavements, using a multirotor aerial vehicle equipped with a bio-inspired Dynamic Vision Sensor (DVS). Concerning the detection part, the DVS camera captures events, which are asynchronously fed into a Neuromorphic Hough Transform algorithm running on a SpiNN-3 board and implemented as a Spiking Neural Network (SNN). Next, the asynchronous output of the detection module is fed into an analytically formulated event-based Partitioned Visual Servoing (PVS) algorithm, running on conventional processing hardware, which allows the multirotor to autonomously track and navigate along the detected contour. The proposed architecture achieves efficient tracking of contour-based areas, while constantly maintaining the latter inside the DVS camera's field of view. A set of real-time experiments in various settings employing an octorotor equipped with a downward-looking DVS and a SpiNN-3 board demonstrate the effectiveness of the suggested framework.
UR - http://www.scopus.com/inward/record.url?scp=85182524784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182524784&partnerID=8YFLogxK
U2 - 10.1109/IROS55552.2023.10342437
DO - 10.1109/IROS55552.2023.10342437
M3 - Conference contribution
AN - SCOPUS:85182524784
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6349
EP - 6355
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Y2 - 1 October 2023 through 5 October 2023
ER -