De Guzman, CJP and Chua, Alvin and Chu, Timothy and Secco, Emanuele Lindo (2023) Evolutionary algorithm-based energy-aware path planning with a quadrotor for warehouse inventory management. HighTech and Innovation Journal, 4 (4). ISSN 2723-9535
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Abstract
Quadrotors are a key technology to introduce smart automation in warehouse processes, thanks to their ability to extract information from the products that can be stored at high altitudes. Current studies investigating quadrotor viability in warehouse automation rely on using a single quadrotor, and its limited endurance can clearly constrain the performance of this approach in large-scale warehouses. Having multiple quadrotors can compensate for their low battery life, effectively increasing their productivity. There is a need to design a system where the quadrotor with the most energy-efficient path to the product is chosen in a quick manner. This paper designs and implements an energy-aware path planning algorithm for multiple quadrotors based on an evolutionary algorithm. Objectives and constraints regarding quadrotor yaw were added to the algorithm to allow the quadrotor to face the product to be scanned. The proposed design was applied to a commercial device, namely a Crazyflie quadrotor. The energy recovered by charging the Crazyflie quadrotor was obtained after performing a set of maneuvers to characterize its energy consumption model. A set of experiments was performed: results showed that the model properly minimized the energy consumption along the quadrotor’s paths as generated by the algorithm in a warehouse environment. The designed hybrid path planning algorithm could generate more optimal paths based on the obtained parameters, such as energy consumption and violations against constraints, while only having slightly higher computational times. The algorithm’s feasibility is further strengthened with simulation results incurring a maximum energy consumption difference of 0.6%.
| Item Type: | Article |
|---|---|
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Emanuele Secco |
| Date Deposited: | 04 Jan 2024 14:30 |
| Last Modified: | 30 May 2024 11:42 |
| URI: | https://hira.hope.ac.uk/id/eprint/4106 |
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