FPGAbased Power Efficient Face Detection for Mobile Robots Autonomous mobile robots need perception module to understand nearby environments, avoid obstacles and, most importantly, interact with the humans in the dynamic environments. In order to perform human robot interaction for mobile robots, face detection is an inevitable and important function. In recent years, deep learning has emerged to be the most accurate approach for face detection problems. However, deep learning models have very high computational density, therefore cannot be executed in realtime on the embedded lowend processors that most of the robot systems equip with. Switching to highend CPU or GPU generally means much higher cost and power consumption. Meanwhile, FPGA is well suited for robot application because it provides high computing power with low cost and high power efficiency. In this paper we design an FPGA based system for the acceleration of MTCNN, which is one of the most accurate face detection neural networks. We evaluate the system using a Zynq 706 board and compare the performance with embedded CPU and popular GPU edge computing platform Jetson TX2. Our FPGA system has 40xc3x97 lower latency than Jetson TX2 with 2.5 xc3x97 higher power efficiency, making it a promising candidate for egde computing on mobile robot applications.

ZytleBot: FPGA Integrated Development Platform for ROS Based Autonomous Mobile Robot The FPT2019 FPGA Design Competition is a competition aimed at recommending innovations in utilizing FPGAs to realize level 5 autonomous driving vehicles. We have developed ZytleBot, an ROS based robot utilizing FPGA for the contest. ZytleBot calculates all processing necessary for autonomous driving in realtime within a programmable SoC. Therefore, it is possible to go around the course imitating an actual road, detect signals and obstacles and take appropriate behavior without any external operation. Robot development requires a wide range of knowledge and technology, but we have proceeded robot development efficiently by using ROS, a robot development middleware, and TurtleBot3, a robot development platform. As an application of FPGA, road surface image preprocessing and traffic signal detector using machine learning is implemented in the FPGA. The traffic signal detector uses HOG features and SVM classifiers, which runs over 270 times faster than running on the processor. We also provide ZytleBot as a platform for efficient development of FPGA integrated ROS robots.