This research considers overall scheduling of a vacuum cleaning robot that includes multi cleaning cycles. Even though there are research studies for generating paths for a device, the paths in each cycle tend to be similar from the fact that the motion planning is based on one tour of a target space. This paper suggests a new and effective simulation based optimization (SO) framework for generating an overall schedule and an effective path for each cycle. In the simulation stage, a dust prediction model is generated using absorbed dust data and floor information. This process uses a multi-modal Gaussian mixture model as a basic model. The generated prediction model provides the needed constraints for different mathematical programming models in the optimization stage. The proposed framework is considered as an efficient scheduling method in terms of minimizing redundant paths while maintaining tolerable dust levels during multi cleaning cycles.
['Hyunsoo Lee', 'Amarnath Banerjee']
Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization