TY - JOUR
T1 - Indoor Visual Exploration with Multi-Rotor Aerial Robotic Vehicles
AU - Rousseas, Panagiotis
AU - Karras, George C.
AU - Bechlioulis, Charalampos P.
AU - Kyriakopoulos, Kostas J.
N1 - Funding Information:
This work has been funded by the European Union’s Horizon 2020 Research and Innovation Program PATHOCERT—Pathogen Contamination Emergency Response Technologies under grant agreement No. 883484.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/7
Y1 - 2022/7
N2 - In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task.
AB - In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task.
KW - autonomous navigation
KW - multi-agent systems
KW - multi-rotor aerial vehicles
KW - robotic exploration
KW - unmanned aerial vehicles
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U2 - 10.3390/s22145194
DO - 10.3390/s22145194
M3 - Article
C2 - 35890874
AN - SCOPUS:85135109543
SN - 1424-8220
VL - 22
JO - Sensors
JF - Sensors
IS - 14
M1 - 5194
ER -