Robotic computing has reached a tipping point, with a myriad of robots (eg, drones, self-
driving cars, logistic robots) being widely applied in diverse scenarios. The continuous …

We introduce RobotPerf, a vendor-agnostic bench-marking suite designed to evaluate
robotics computing performance across a diverse range of hardware platforms using ROS 2 …

Hardware acceleration can revolutionize robotics, enabling new applications by speeding
up robot response times while remaining power-efficient. However, the diversity of …

Autonomous drones are facing a tough efficiency challenge due to limitations on the utilized
processing hardware units. Among these limitations is the tradeoff between fast computing …

Robotics applications process large amounts of data in real time and require compute
platforms that provide high performance and energy efficiency. FPGAs are well suited for …

Traditional software development in robotics is about programming functionality in the CPU
of a given robot with a pre-defined architecture and constraints. With adaptive computing …

In this paper, we present an approach to integrate reconfigurable SoCs into the well known
Robot Operating System (ROS) called ReconfROS. Our method allows to implement …

The ROS2 software framework is increasingly prevalent in component-based applications
for robots and other autonomous systems. Recently added ROS2 features to support zero …

This paper introduces our autonomous driving system equipped with recognition processing
units from a camera image for hazard object/human-doll detection and drive lane detection …

Reinforcement Learning (RL) is a technique where an agent learns to accomplish an
assigned task on the basis of reward phenomenon. RL algorithm when implemented with …