Spatial drone path planning: A systematic review of parameters and algorithms

Abstract

After the outbreak of COVID-19 pandemic and the increase in online shopping and e-commerce, the use of drones for logistics has sharply increased. Such an increase raises two questions: (1) What spatial parameters were used to optimize drone paths? (2) How do the algorithms used for drone path planning differ in their input information, type of vehicles and outputs? Seeking answers to these questions, this study systematically reviews the 72 studies on path planning of logistic drones. We identify seven types of strategic design factors – i.e. spatial parameters of drone path optimisation: (i) demand, (ii) climate, (iii) infrastructure, (iv) regulations, (v) safety, (vi) public acceptance and (vii) drone technology. We also identified three properties differentiating algorithms used for spatial allocation of drone paths, i.e. tactical design factors: (i) input information types – i.e. static vs. dynamic; (ii) vehicle type – i.e. drone-only vs. drone-vehicle models; (iii) solution types - i.e. single solution vs multiple solutions. Lastly, the implications of these findings are discussed in light of expected technological developments in AI and battery endurance, and conclusions on future spatial planning systems embracing drone-based logistics are made.