Safe Human-UAS Collaboration from High-Level Planning to Low-Level Tracking

This paper studies the problem of safe human-uncrewed aerial system (UAS) collaboration in a shared work environment. By considering human and UAS as co-workers, we use Petri Nets to abstractly model evolution of shared tasks assigned to human and UAS co-workers. Particularly, the Petri Nets' 'places' represent work stations; and therefore, the Petri Nets' transitions can formally specify displacements between the work stations. The paper's first objective is to incorporate uncertainty regarding the intentions of human co-workers into motion planning for UAS, when UAS closely interacts with human co-workers. To this end, the proposed Petri Nets model uses 'conflict' constructs to represent situations at which UAS deals with incomplete knowledge about human co-worker intention. The paper's second objective is then to plan the motion of the UAS in a resilient and safe manner, in the presence of non-cooperative human co-workers. In order to achieve this objective, UAS equipped with onboard perception and decision-making capabilities are able to, through real-time processing of in-situ observation, predict human intention, quantify human distraction, and apply a non-stationary Markov Decision Process (MDP) model to safely plan UAS motion in the presence of uncertainty. Given the current and next UAS waypoints, assigned by the MDP planner, the paper applies Potryagin's minimal principle to plan the desired trajectory of the UAS and uses a feedback linearaztion trajectory control to enable UAS with stable tracking of the desired trajectory. Note to Practitioners - Despite advances in aerial robotics, there are still significant barriers for their integration and application into human-centered jobs. Safety-related concerns, potential hazards to labor, and limited mission duration are some major barriers for using such a helpful technology. The main goal of this paper is to come up with a reliable solution for long-term collaboration between humans and unmanned aerial system (UAS) for safe and efficient accomplishment of a human-centered work. To this end, we propose to use Petri Nets to abstractly model and specify human-robot collaboration and effectively plan the tasks assigned to UAS and human co-workers. In this context, human co-workers can do some tasks that are difficult and somehow unsafe to be carried out by UAS. On the other hand, UAS can be deployed to monitor the work environment, recognize human wellness, or carry some small payloads. UAS co-workers enabled with on-board perception capabilities can also predict human coworker intention and quantify human co-worker distraction in real time through online processing of in-situ observations. Furthermore, UAS can safely plan its motion without human intervention or supervision, by combining Markov Decision Process, optimal control, and trajectory tracking control models.