HERO: Accelerating Autonomous Robotic Tasks with FPGA

Abstract

The Heterogeneous Extensible Robot Open (HERO) platform is designed for autonomous robotic research. While bringing in the flexible computational capacities by CPU and FPGA, it addresses the challenges of heterogeneous computing by embracing OpenCL programming. We propose heterogeneous computing approaches for three fundamental robotic tasks: simultaneous localization and mapping (SLAM), motion planning and convolutional neural network (CNN) inference. With FPGA acceleration, the SLAM and motion planning tasks are performed 2–4 times faster on the HERO platform against fine-tuned software implementation. For CNN inference, it can process 20–30 images per second with the network of VGG-16 or ResNet-50. We expect the open platform and the developing experiences shared in this paper can facilitate future robotic research, especially for those compute intensive tasks of perception, movement and manipulation.