Evolutionary Algorithm-Based Energy-Aware Path Planning with a Quadrotor for Warehouse Inventory Management

C. J. P. De Guzman, A. Y. Chua, T. S. Chu, E. L. Secco


Quadrotors have been vital for automating warehouse processes. However, a significant gap in recent studies is that they use a single quadrotor with limited battery life, considering that their objective involves navigation in a large-scale environment such as a warehouse. Using an energy consumption model to enable more efficient navigation can be explored. Conventional data-driven energy models and path planning algorithms are insufficient for describing the various motions that a quadrotor can perform in warehouse operations, such as changes in yaw. This study aims to design a novel exhaustive data-driven energy consumption model and evolutionary algorithm-based path planning algorithm to consider various quadrotor movements involved in warehouse operations. The quadrotor is tasked with performing a set of movements to each be represented as a power equation in terms of their velocity. The obtained equations were subsequently used as the primary optimization objective for the path planning algorithm, which included yaw angle objectives and constraints. A set of experiments was performed with Crazyflie quadrotors to verify the model and the algorithm. The results showcased the accuracy of the energy consumption model, which was kept at a maximum difference of 0.6%. The designed path planning algorithm obtained greater energy efficiency in the generated paths compared to other state-of-the-art evolutionary algorithms with similar objectives and constraints.


Doi: 10.28991/HIJ-2023-04-04-012

Full Text: PDF


Evolutionary Algorithms; Inventory Management; Path Planning; Quadrotor; Warehouse.


Fernández-Caramés, T. M., Blanco-Novoa, O., Froiz-Míguez, I., & Fraga-Lamas, P. (2019). Towards an Autonomous Industry 4.0 Warehouse: A UAV and Blockchain-Based System for Inventory and Traceability Applications in Big Data-Driven Supply Chain Management. Sensors (Basel, Switzerland), 19(10), 2394. doi:10.3390/s19102394.

Malang, C., Charoenkwan, P., & Wudhikarn, R. (2023). Implementation and Critical Factors of Unmanned Aerial Vehicle (UAV) in Warehouse Management: A Systematic Literature Review. Drones, 7(2), 80. doi:10.3390/drones7020080.

Kalinov, I., Petrovsky, A., Ilin, V., Pristanskiy, E., Kurenkov, M., Ramzhaev, V., Idrisov, I., & Tsetserukou, D. (2020). WareVision: CNN Barcode Detection-Based UAV Trajectory Optimization for Autonomous Warehouse Stocktaking. IEEE Robotics and Automation Letters, 5(4), 6647–6653. doi:10.1109/LRA.2020.3010733.

Kwon, W., Park, J. H., Lee, M., Her, J., Kim, S. H., & Seo, J. W. (2020). Robust Autonomous Navigation of Unmanned Aerial Vehicles (UAVs) for Warehouses’ Inventory Application. IEEE Robotics and Automation Letters, 5(1), 243–249. doi:10.1109/LRA.2019.2955003.

Campbell, J., Corberán, Á., Plana, I., Sanchis, J. M., & Segura, P. (2023). The multi-purpose K-drones general routing problem. Networks, 82(4), 437–458. doi:10.1002/net.22176.

Alajami, A. A., Moreno, G., & Pous, R. (2022). Design of a UAV for Autonomous RFID-Based Dynamic Inventories Using Stigmergy for Mapless Indoor Environments. Drones, 6(8), 208. doi:10.3390/drones6080208.

Yang, S. Y., Jan, H. C., Chen, C. Y., & Wang, M. S. (2023). CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle. Sensors, 23(10), 4707. doi:10.3390/s23104707.

Di Franco, C., & Buttazzo, G. (2016). Coverage Path Planning for UAVs Photogrammetry with Energy and Resolution Constraints. Journal of Intelligent and Robotic Systems: Theory and Applications, 83(3–4), 445–462. doi:10.1007/s10846-016-0348-x.

Yan, X., Chen, R., & Jiang, Z. (2023). UAV Cluster Mission Planning Strategy for Area Coverage Tasks. Sensors, 23(22), 9122. doi:10.3390/s23229122.

Na, Y., Li, Y., Chen, D., Yao, Y., Li, T., Liu, H., & Wang, K. (2023). Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization. Sustainability (Switzerland), 15(16), 12101. doi:10.3390/su151612101.

Liu, H., Chen, Q., Pan, N., Sun, Y., An, Y., & Pan, D. (2022). UAV Stocktaking Task-Planning for Industrial Warehouses Based on the Improved Hybrid Differential Evolution Algorithm. IEEE Transactions on Industrial Informatics, 18(1), 582–591. doi:10.1109/TII.2021.3054172.

Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 149, 270–299. doi:10.1016/j.comcom.2019.10.014.

Shao, S., Peng, Y., He, C., & Du, Y. (2020). Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Transactions, 97, 415–430. doi:10.1016/j.isatra.2019.08.018.

Xu, L., Cao, X., Du, W., & Li, Y. (2023). Cooperative path planning optimization for multiple UAVs with communication constraints. Knowledge-Based Systems, 260, 110164. doi:10.1016/j.knosys.2022.110164.

Bitcraze (2022). Crazyflie 2.1. Available online: https://www.bitcraze.io/products/crazyflie-2-1/ (accessed on June 2023).

Full Text: PDF

DOI: 10.28991/HIJ-2023-04-04-012


  • There are currently no refbacks.

Copyright (c) 2023 Timothy Scott C Chu