For mobile robots, mobile manipulators, and autonomous vehicles to safely navigate around populous places such as streets and warehouses, human observers must be able to understand their navigation intent. One way to enable such understanding is by visualizing this intent through projections onto the surrounding environment. But despite the demonstrated effectiveness of such projections, no open codebase with an integrated hardware setup exists. In this work, we detail the empirical evidence for the effectiveness of such directional projections, and share a robot-agnostic implementation of such projections, coded in C++ using the widely-used Robot Operating System (ROS) and rviz. Additionally, we demonstrate a hardware configuration for deploying this software, using a Fetch robot, and briefly summarize a full-scale user study that motivates this configuration. The code, configuration files (roslaunch and rviz files), and documentation are freely available on GitHub at https://github.com/umhan35/arrow_projection.
翻译:对于移动机器人、移动操纵器和自主飞行器,在街道和仓库等人口众多的地方安全航行,人类观察者必须能够理解他们的导航意图。这种理解的一个方法是通过对周围环境的预测来直观地看待这一意图。但是,尽管这些预测已经证明是有效的,但没有具有集成硬件设置的开放代码库。在这项工作中,我们详细说明了这种定向预测的有效性的经验证据,并分享了这种预测的机器人-不可知性执行,这些预测是使用广泛使用的机器人操作系统(ROS)和rviz在C++中编码的。此外,我们展示了部署这一软件的硬件配置,使用一个抓取机器人,并简要总结了激励这一配置的全面用户研究。代码、配置文件(ROs 发射和rviz文件)和文件可在GitHub网站https://github.com/umhan35/row_projection上免费查阅。