Robotic inspection offers a robust, scalable, and flexible alternative to deploying fixed sensor networks or humaninspectors. While prior work has mostly focused on single robot inspections, this work studies the deployment of a swarm ofinspecting robots on a simplified surface of an in-orbit infrastructure. The robots look for points of mechanical failure and inspectthe surface by assessing propagating vibration signals. In particular, they measure the magnitude of acceleration they sense ateach location on the surface. Our choice for sensing and analyzing vibration signals is supported by the established position ofvibration analysis methods in industrial infrastructure health assessment. We perform simulation studies in Webots, a physicsbased robotic simulator, and present a distributed inspection algorithm based on bio-inspired particle swarm optimization andevolutionary algorithm niching techniques to collectively localize an a priori unknown number of mechanical failure points. Toperform the vibration analysis and obtain realistic acceleration data, we use the ANSYS multi-physics simulation software andmodel mechanical failure points as vibration sources on the surface. We deploy a robot swarm comprising eight robots of 10-cmsize that use a bio-inspired inchworming locomotion pattern. The swarm is deployed on 2.5D (that is curved 2D) cylindricalsurfaces with and without obstacles to investigate the robustness of the algorithm in environments with varying geometric complexity. We study three performance metrics: (1) proximity of the localized sources to their ground truth locations, (2) time tolocalize each source, and (3) time to finish the inspection task given an 80% surface coverage threshold. Our results show thatthe robots accurately localize all the failure sources and reach the coverage threshold required to complete the inspection. Thiswork demonstrates the viability of deploying robot swarms for inspection of potentially complex 3D environments.<br/
