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

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.

Automated summary

This paper introduces a new application of UAV for joint goods delivery and in-situ sensing, and proposes an algorithm that optimizes route and sensing task selection to minimize energy consumption, maximize sensing reward, and ensure timely goods delivery. The problem is challenging due to mixed integer programming. The key idea is to interpret UAV's waypoints as location-dependent tasks, and a new task-time graph is constructed to describe the process. Validated by a real-world case study, our approach outperforms alternatives by over 18% in task reward.