Human-Automation Collaborative RRT for UAV Mission Path Planning

摘要

Future envisioned Unmanned Aerial Vehicle (UAV) missions will be carried out in dynamic and complex environments. Human-automation collaboration will be required in order to distribute the increased mission workload that will naturally arise from these interactions. One of the areas of interest in these missions is the supervision of multiple UAVs by a single operator, and it is critical to understand how individual operators will be able to supervise a team of vehicles performing semi-autonomous path planning while avoiding no-fly zones and replanning on the fly. Unfortunately, real time planning and replanning can be a computationally burdensome task, particularly in the high density obstacle environments that are envisioned in future urban applications. Recent work has proposed the use of a randomized algorithm known as the Rapidly exploring Random Tree (RRT) algorithm for path planning. While capable of finding feasible solutions quickly, it is unclear how well a human operator will be able to supervise a team of UAVs that are planning based on such a randomized algorithm, particularly due to the unpredictable nature of the generated paths. This thesis presents the results of an experiment that tested a modification of the RRT algorithm for use in human supervisory control of UAV missions. The experiment tested how human operators behaved and performed when given different ways of interacting with an RRT to supervise UAV missions in environments with dynamic obstacle fields of different densities. The experimental results demonstrated that some variants of the RRT increase subjective workload, but did not provide conclusive evidence for whether using an RRT algorithm for path planning is better than manual path planning in terms of overall mission times. Analysis of the data and behavioral observations hint at directions for possible future work.