Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 3; pp. 3594 - 3599
• Main Authors: Liu, Bin, Ni, Wei, Liu, Ren Ping, Guo, Y. Jay, Zhu, Hongbo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Energy consumption; Graph theory; Integer programming; joint goods delivery and in-situ sensing; Mixed integer; Optimization; Propulsion; Roads; Route planning; Route selection; Routing; Sensors; Task analysis; task selection; Unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3225269

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV's energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.