In view of enhancement of environmental awareness in recent years, the global energy conservation has become a topic of common concern. Especially in the field of power conservation, how to use the electricity more efficiently on robots without redundancy and also meet the fundamental requirements is an important issue. The purpose of this thesis is to develop a dynamic power scheduling and real-time monitoring system to make power management more structural and convenient so that the robots need not worry about the problem of excessive power consumption, and then complete the assigned tasks. First of all, in the aspect of dynamic power scheduling, a neighborhood search method which can search for different states to satisfy all constraints of the optimal scheduling will be developed, such as simulated annealing, genetic algorithm and Tabu search, etc. All the methodologies mentioned can be applied to handle most of the scheduling problems. Secondly, for real-time monitoring, in addition to the general real-time monitoring of the battery power status, we propose a Multi-Phase Principal Component Analysis (PCA) based Statistical Process Control (SPC) methods to monitor the process of optimal scheduling. The abnormal behaviors can be observed by a few of control charts, a main function of real-time monitoring system which will notify the remote monitoring personnel to take immediate action through the constructed GMPP system. Finally, in addition to promptly notify the remote monitoring personnel, the proposed method of dynamic power scheduling will also take corresponding measures to do rescheduling automatically to minimize the damage if the real-time monitoring system detect abnormal behaviors in the monitoring process. In this thesis, the cooperation of the dynamic power scheduling and real-time monitoring system which enable the power supply on robots to reach high reliability and good stability will be implemented practically in the future.