The process of Industry 4.0 is profoundly affecting the industrial structure of warehousing and intralogistics. However, it is unrealistic for many companies to build fully-automated warehouses by one-time investment due to the limitation of funds and/or restrictions on land registration, or, simply for some companies, Industry 5.0, which emphasises human participation, is more in line with their vision. Although existing solutions explicitly premise the coexistence of autonomous mobile robots (AMRs) and human workers, their main assumptions still include that humans must move carefully, and that the robot's current observations are able to match its priors about the work environment. Consequently, current robotic solutions usually have limited deployment space and high operation-maintenance costs. Therefore, there is a need to research and develop next-generation, more reliable and intelligent robotic navigation methods to enable large-scale deployment of affordable warehousing and intralogistics automation solutions. NavWare proposes to use data-driven deep learning methods to directly intervene in the AMR's navigation layers for fast and reliable local obstacle avoidance as well as generalizable global path planning, and ultimately generate safe worker-collaborative robot navigation. Compared with existing methods, robotic warehouse navigation based on NavWare may be less expensive to deploy and maintain, while the system performance may be better.