Socially Appropriate Robot Planning in Dynamic, Unseen Construction Environments

With the advancement of robotic technologies, an increasing number of robots are envisioned to be deployed in various human-occupied environments such as construction job sites. Although navigation in a static environment is well studied, navigation in such a dynamic environment is still a challenging problem. In particular, construction environments present many challenges as they are often densely populated with both static and dynamic objects (e.g., stocked materials, workers, and equipment). Thus, robots in the construction environment should be capable of adapting to frequent layout changes and dynamic human movements and producing efficient paths in unknown, dynamic environments accordingly. Given the safety-critical nature of the construction environment, it is also critical not to collide, distract, or interfere with workers when robots maneuver around human workers in the workplace. To accommodate this need, this study proposes a Reinforcement Learning (RL)-based navigation model that enables robots to adhere to appropriate safety proxemic considerations aligned with the social work convention of construction workplaces while following a globally planned trajectory. The result showed that our model could secure the respective minimum distance in contrast to the baseline model, which suffered in complex environments. These findings will contribute to building future construction mobile robots with social intelligence that can produce socially compliant and safe behaviors.