Robot systems are facing a growing demand for realtime performance of multiple computing tasks and autonomous decision-making. However, limited computing resources make efficient scheduling of computing tasks and optimizing resource utilization a key challenge. Virtualization of field-programmable gate arrays enables the dynamic sharing of hardware resources, while dynamic partial reconfiguration allows for flexible adjustment of hardware accelerators based on task requirements, making it suitable for complex and fluctuating computing loads. However, virtualization scheduling based on operating systems or microkernels usually introduces high software overhead. This paper proposes a hardware-level adaptive scheduling mechanism integrated with spatiotemporal scheduling to efficiently share reconfigurable computing resources among multiple users. This mechanism is transparent to users and can adaptively adjust the spatiotemporal allocation strategy according to the characteristics of tasks. In addition, the scheduling scheme introduces access rights management to strictly prevent unauthorized resource access. Hardware scheduling system achieves a speedup of over 1000 × compared to a software-based scheduling system when executing 100 hardware tasks, reducing execution time from seconds to milliseconds.