TY - GEN
T1 - An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots
AU - Haghighat, Bahar
AU - Boghaert, Johannes
AU - Minsky-Primus, Zev
AU - Ebert, Julia
AU - Liu, Fanghzheng
AU - Nisser, Martin
AU - Ekblaw, Ariel
AU - Nagpal, Radhika
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present sources accurately and complete the predefined inspection coverage threshold.
AB - The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present sources accurately and complete the predefined inspection coverage threshold.
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U2 - 10.1007/978-3-031-20176-9_2
DO - 10.1007/978-3-031-20176-9_2
M3 - Conference contribution
AN - SCOPUS:85142734675
SN - 9783031201752
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 27
BT - Swarm Intelligence - 13th International Conference, ANTS 2022, Proceedings
A2 - Dorigo, Marco
A2 - Strobel, Volker
A2 - Camacho-Villalón, Christian
A2 - Hamann, Heiko
A2 - Hamann, Heiko
A2 - López-Ibáñez, Manuel
A2 - García-Nieto, José
A2 - Engelbrecht, Andries
A2 - Pinciroli, Carlo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Swarm Intelligence, ANTS 2022
Y2 - 2 November 2022 through 4 November 2022
ER -