Last-mile delivery is one of the most discussed problems of the last decade due to the growing importance of e-commerce and the development of Industry 4.0. In particular, this problem regards the delivery of parcels from the warehouse to the final customers. In order to bring efficiency and innovation, in this paper a hybrid delivery architecture is considered, which takes advantage of the combined use of a drone and a truck to perform a sequence of pick-ups and deliveries, and the problem of optimal control of the drones' missions is addressed. The reference scenario is the smart city where the drone of the hybrid delivery architecture is in charge of three different pick-up and delivery missions: truck to point (i.e., pick-up from the truck and delivery to the customer), point to point (i.e., delivery to a customer and pick-up from the subsequent customer), and point to truck (i.e., reentry from a customer to the truck). From the control point of view, the drone is optimally guided in all the operating modes, i.e., ascent and descent from/to truck mode, free flight mode with/without payload, and descent for pick-up/delivery mode, by a receding horizon linear quadratic regulator (LQR), which is able to manage the drone in the dynamic landing on a movable vehicle and to allow the changing in real time of the landing point on the truck. Simulation results of the truck-drone delivery architecture are presented and discussed in detail, proving the effectiveness of the proposed method.

Automatic Control of Drones' Missions in a Hybrid Truck-Drone Delivery System / Proia, S.; Cavone, G.; Tresca, G.; Carli, R.; Dotoli, M.. - (2023), pp. 1477-1482. (Intervento presentato al convegno 9th International Conference on Control, Decision and Information Technologies, CoDIT 2023 tenutosi a University of Rome La Sapienza, Facolta di lngegneria Civile e lndustriale, ita nel 2023) [10.1109/CoDIT58514.2023.10284110].

Automatic Control of Drones' Missions in a Hybrid Truck-Drone Delivery System

Proia S.;Tresca G.;Carli R.;Dotoli M.
2023-01-01

Abstract

Last-mile delivery is one of the most discussed problems of the last decade due to the growing importance of e-commerce and the development of Industry 4.0. In particular, this problem regards the delivery of parcels from the warehouse to the final customers. In order to bring efficiency and innovation, in this paper a hybrid delivery architecture is considered, which takes advantage of the combined use of a drone and a truck to perform a sequence of pick-ups and deliveries, and the problem of optimal control of the drones' missions is addressed. The reference scenario is the smart city where the drone of the hybrid delivery architecture is in charge of three different pick-up and delivery missions: truck to point (i.e., pick-up from the truck and delivery to the customer), point to point (i.e., delivery to a customer and pick-up from the subsequent customer), and point to truck (i.e., reentry from a customer to the truck). From the control point of view, the drone is optimally guided in all the operating modes, i.e., ascent and descent from/to truck mode, free flight mode with/without payload, and descent for pick-up/delivery mode, by a receding horizon linear quadratic regulator (LQR), which is able to manage the drone in the dynamic landing on a movable vehicle and to allow the changing in real time of the landing point on the truck. Simulation results of the truck-drone delivery architecture are presented and discussed in detail, proving the effectiveness of the proposed method.
2023
9th International Conference on Control, Decision and Information Technologies, CoDIT 2023
979-8-3503-1140-2
Automatic Control of Drones' Missions in a Hybrid Truck-Drone Delivery System / Proia, S.; Cavone, G.; Tresca, G.; Carli, R.; Dotoli, M.. - (2023), pp. 1477-1482. (Intervento presentato al convegno 9th International Conference on Control, Decision and Information Technologies, CoDIT 2023 tenutosi a University of Rome La Sapienza, Facolta di lngegneria Civile e lndustriale, ita nel 2023) [10.1109/CoDIT58514.2023.10284110].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/262801
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