Self-Optimization of Robot Design for Navigating in Ceiling Systems

Suspended ceiling systems are complex and heterogeneous due to the combination of different components. Therefore, it is hard for robots to navigate in these environments without expert designed robot morphology. However, the cost of robot design is high since ceiling systems vary from building to building. Currently, some studies demonstrate that robots can evolve like creatures so that they can adapt themselves to different environments through evolutionary strategies. Inspired by the assembly of LEGO bricks, we applied graph grammar methods to optimize robot design in suspended ceiling systems. The basic idea is that the robot can find the optimized structures assembled from elementary components for themselves without any human intervention. Robots were trained in four common ceiling environments reflecting the influence of typical terrain and obstructions (e.g., ducts). Results show that different suspended ceiling system significantly affects the configuration of robots and robots can successfully evolve specific shapes to improve their acclimatization. This paper marks the first attempt at performing robot evolution in the context of facilities management and is expected to evoke future discussions in robot design in more civil engineering tasks.