Unsupervised Learning-Based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time

In remote data collection from sampling stations, a vehicle must be within sufficient distance from a particular station for a predefined minimal time to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensing site and ensure the vehicle is within a reliable communication distance from the station for a sufficient period. We propose to formulate the planning problem as a variant of the Close Enough Dubins Traveling Salesman Problem with Time Constraints (CEDTSP-TC) that is heuristically solved by unsupervised learning of the Growing Self-Organizing Array (GSOA) modified to address the constrained minimal data retrieving time. The proposed method is compared with a baseline based on a sampling-based decoupled approach, and the results support the feasibility of both proposed solvers in random instances.