Analysis and optimisation of unmanned aerial vehicle swarms in logistics: An intelligent delivery platform

Abstract

Deploying Unmanned Aerial Vehicle (UAV) swarms in delivery systems is still in its infancy with regards to the technology, safety, aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects- e.g., increasing efficacy, reducing the air traffic resulting in safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This manuscript analyses several delivery schemes within a platform, such as delivery with and without using air highways, and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium and FIFO) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional space” (dMAiMD) and several other new techniques along with Hungarian and Cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method and constraints. Several scenarios are tested on the platform using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect
the decision on choosing the optimal delivery scheme and method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs.