As autonomous driving research has actively pro-gressed, software for autonomous vehicles and embedded systems such as Apollo and AutoWare have also been developed, providing a complete set of self-driving modules, including perception, localization and mapping, path planning, prediction, decision making, and control. Most of the researchers currently use these software programs, but many researchers have also studied autonomous driving based on the middleware software termed robot operating system (ROS) before such software was released, especially in academia. Accordingly, we intend to develop ROS-based unmanned RC car equipped with autonomous driving sensors such as LiDAR, radar, VIS/IR cameras, GPS, and IMUs that can provide ROS-based datasets to researchers studying self-driving cars and robots using ROS. In addition, unlike conventional datasets, we acquire dataset not only on road but also pedestrian paths that can be used in both vehicles and robots and provides extreme environmental datasets such as snowfall environments. In this sense, the ROS dataset we created will be helpful to researchers studying autonomous vehicles and robots by using ROS.

Collision avoidance in emergency situations is a crucial and challenging task in motion planning for autonomous vehicles. Especially in the field of optimization-based planning using nonlinear model predictive control, many efforts to achieve real-time performance are still ongoing. Among various approaches, the iterative linear quadratic regulator (iLQR) is known as an efficient means of nonlinear optimization. Additionally, parallel computing architectures, such as GPUs, are more widely applied in autonomous vehicles. In this paper, we propose 1) a parallel computing framework for iLQR with input constraints considering the characteristics of the problem and 2) a proper environmental formulation that can be covered with single-precision floating-point computation of the GPU. The GPU-accelerated framework was tested on a real-time simulation-in-the-loop system using CarMaker and ROS at a 20 Hz sampling rate on a low-performance mobile computer and was compared against the same framework realized with a CPU.

Input-output transmission ratio shifting mechanisms provide a variable transmission ratio, which effectively expands a speed-force operating range of actuators. Although it is the most effective solution to increase the performance of robotic systems, its application to compact robotic systems still remains a challenging issue due to its complexity and massive structure. In this paper, we introduce a twisted string actuation-based transmission module for compact robotic systems. The twisted string mechanism provides a simplified transmission design and a compact form factor of the transmission module. An automatic transmission shifting algorithm is proposed for effective and autonomous control strategies. The developed prototype is integrated into a robotic gripper/hand, and its performance is verified with grasping demonstrations.