Predictable multi-core implementation of multi-rate sensor fusion for high-precision positioning systems

The high-precision and high-speed positioning systems require position feedback with high accuracy at a higher frequency. As reported in recent literature, high accuracy and high operating frequency can be achieved by fusing multiple position sensor data, e.g., the linear encoder (less robust/accurate, but fast) and object detection using camera images (accurate, but slow due to heavy processing load). Typically, image-based object detection incurs a significant computational delay due to computationally intensive processes and is the main performance bottleneck. Moreover, the computation delay varies when implemented on industrial platforms and degrades the performance of the closed-loop control system. In this paper, we present scheduling techniques such as parallelism and pipelining considering predictable multi-core platforms for such a multi-sensor positioning system. On the one hand, the predictable platform nearly removes the variation in execution time, making the delay constant. On the other hand, the parallel and pipeline schedules reduce the computation delay, translating to a shorter sampling period and better closed-loop performance. Furthermore, we perform a design space exploration on various parameters and control performance considering an industrial case study of semiconductor die-bonding equipment.