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Smart city article details
| Title | Urban Air Logistics With Unmanned Aerial Vehicles (Uavs): Double-Chromosome Genetic Task Scheduling With Safe Route Planning |
|---|---|
| ID_Doc | 59845 |
| Authors | Rinaldi M.; Primatesta S.; Bugaj M.; Rostáš J.; Guglieri G. |
| Year | 2024 |
| Published | Smart Cities, 7, 5 |
| DOI | http://dx.doi.org/10.3390/smartcities7050110 |
| Abstract | Highlights: What are the main findings? Developed a combined task scheduling and path planning framework for enabling optimized and safe drone delivery services in an urban environment. Utilized a constrained optimization-based framework to allocate both parcel pick-up and delivery tasks and re-charge tasks to a fleet of UAVs in an urban context. The energy efficiency, tasks’ due dates, UAVs’ capabilities, and risks of the UAVs’ flyable paths are taken into account in the combined double-chromosome evolutionary-based task scheduling and path planning methodology. What are the implications of the main findings? The proposed approach combining task allocation and path planning offers both a scalable optimization solution to the NP-hard problem addressed in this work (i.e., the drone delivery problem) and a flexible tool adaptable to other scenarios and task types. Addressing the allocation of re-charge tasks along with the allocation of delivery tasks in the same framework represents a comprehensive resolution approach to the drone delivery problem; also, ensuring service persistency and, thanks to the risk-aware UAV route planner integrated to the evolutionary-based task scheduling algorithm, feasibility of deployment in smart city context. In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. © 2024 by the authors. |
| Author Keywords | aerial robotics; double-chromosome; drone delivery; drone delivery problem; drone operations; energy optimization; genetic algorithm; intelligent transportation system; safe path planning; smart cities; smart transportation; task scheduling; unmanned aerial vehicles; urban air mobility |
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