@inproceedings{651,
  author = {Bahar Haghighat and Johannes Boghaert and Zev Minsky-Primus and Julia Ebert and Fanghzheng Liu and Martin swarm and Ariel Ekblaw and Radhika Nagpal},
  title = {An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots},
  abstract = {<p>The remoteness and hazards that are inherent to the oper- ating environments of space infrastructures promote their need for au- tomated 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 pro- pose using a swarm of miniaturized vibration-sensing mobile robots re- alizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evo- lutionary 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 inspec- tion task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three perfor- mance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the in- spection 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.</p>},
  year = {2022},
  journal = {Intl. Conf. on Swarm Intelligence (ANTS)},
  language = {eng},
}