Structural design, modeling and simulation analysis of a cage broiler inspection robot

Published: 7 April 2025
Abstract Views: 205
PDF: 21
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

When designing an inspection robot for cage-reared broiler chickens, it is imperative to meticulously contemplate both the performance of the robot within the designated workspace and its energy efficiency. The paper optimizes the structure and energy consumption of the robot by analyzing its working environment and the power usage associated with its lifting and lowering functions. Inspection robots designed for cage-reared broiler chickens are required to operate within densely populated chicken coops, underscoring the critical importance of the structure and maneuverability these machines. This research utilizes a four-wheel skid-steering drive mechanism to facilitate swift and precise turns, empowering the robot to adeptly navigate the confined spaces within the chicken coops. The mathematical description of the robot is based on a static kinematic model to ensure efficient navigation within the enclosed environment. The mechanical framework of the robot comprises a four-wheel drive system crafted from hollow rectangular low-carbon steel bars. This design provides the necessary strength and durability while maintaining a lightweight profile. Additionally, the incorporation of a five-axis mechanical arm, integrated with sensors and a gimbal lifting algorithm, ensures adaptability to intricate inspection spaces, with a focus on energy efficiency. Simulation analysis based on the developed model demonstrates the suitability of this structure for the application of inspection robot for cage-reared broiler chickens, ensuring stable operation within the chicken coops. Furthermore, in an effort to boost the energy efficiency of the robot, an analysis of the power consumption linked to its lifting and lowering functions is undertaken. By integrating energy-efficient design principles and intelligent control strategies, the lifting and lowering functions of the system can reduce energy consumption. This ensures the completion of tasks, prolongs battery life, and ultimately enhances the work efficiency and sustainability of the robot.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Almasri, E., Uyguroglu, M. K. 2021. Modeling and trajectory planning optimization for the symmetrical multiwheeled omnidirectional mobile robot. Symmetry (Basel) 13:1033. DOI: https://doi.org/10.3390/sym13061033
Cai, J., Deng, J., Zhang, W., Zhao, W. 2021. Modeling method of autonomous robot manipulator based on D-H algorithm. Mob. Inf. Syst. 2021:4448648. DOI: https://doi.org/10.1155/2021/4448648
Catanoso, D., Chakrabarty, A., Fugate, J., Naal, U., Welsh, T.M., Edwards, L.J. 2021. OceanWATERS Lander robotic arm operation. Proc. IEEE Aerospace Conf. (50100), Big Sky. pp. 1-11. DOI: https://doi.org/10.1109/AERO50100.2021.9438473
Chen, J., Qiang, H., Wu, J., Xu, G., Wang, Z. 2021. Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point hough transform. Comput. Electron. Agr. 180:105911. DOI: https://doi.org/10.1016/j.compag.2020.105911
Cheng, M., Xiang, D. 2020. The design and application of a track-type autonomous inspection robot for electrical distribution room. Robotica 38:185-206. DOI: https://doi.org/10.1017/S0263574719000559
Karpyshev, P., Ilin, V., Kalinov, I., Petrovsky, A., Tsetserukou, D. 2021. Autonomous mobile robot for apple plant disease detection based on CNN and multi-spectral vision system. Proc. IEEE/SICE Int. Symp. System Integration (SII), Fukushima. pp. 157-162. DOI: https://doi.org/10.1109/IEEECONF49454.2021.9382649
Le, A.V., Hayat, A.A., Elara, M.R., Nhan, N.H.K., Prathap, K. 2019. Reconfigurable pavement sweeping robot and pedestrian cohabitant framework by vision techniques. IEEE Access 7:159402-159414. DOI: https://doi.org/10.1109/ACCESS.2019.2950675
Liu, Z., Lv, Z., Zheng, W., Wang, X. 2022. Trajectory control of two-degree-of-freedom sweet potato transplanting robot arm. IEEE Access 10: 26294-26306. DOI: https://doi.org/10.1109/ACCESS.2022.3157600
Mashayekhi, R., Idris, M.Y.I., Anisi, M.H., Ahmedy, I., Ali, I. 2020. Informed RRT*-connect: an asymptotically optimal single-query path planning method. IEEE Access 8:19842-19852. DOI: https://doi.org/10.1109/ACCESS.2020.2969316
Mishra, G., Ahluwalia, U., Praharaj, K., Prasad, S. 2019. RF and RFID based object identification and navigation system for the visually impaired. Proc. 32nd Int. Conf. on VLSI Design (VLSID)/18th Int. Conf. on Embedded Systems (ES), New Delhi. pp. 533-534. DOI: https://doi.org/10.1109/VLSID.2019.00122
Nguyen, V.L., Lin, C.-Y., Kuo, C.-H. 2020. Gravity Compensation design of planar articulated robotic arms using the gear-spring modules. J. Mechanisms Robotics 12:031014. DOI: https://doi.org/10.1115/1.4045650
Paradkar, V., Raheman, H., Rahul, K. 2021. Development of a metering mechanism with serial robotic arm for handling paper pot seedlings in a vegetable transplanter. Artif. Intell. Agric. 5:52-63. DOI: https://doi.org/10.1016/j.aiia.2021.02.001
Quaglia, G., Visconte, C., Scimmi, L.S., Melchiorre, M., Cavallone, P., Pastorelli, S. 2019. Robot arm and control architecture integration on a UGV for precision agriculture. In: Uhl T. (ed.), Advances in Mechanism and Machine Science. Cham, Springer. pp. 2339-2348. DOI: https://doi.org/10.1007/978-3-030-20131-9_231
Raikwar, S., Fehrmann, J., Herlitzius, T. 2022. Navigation and control development for a four-wheel-steered mobile orchard robot using model-based design. Comput. Electron. Agr. 202:107410. DOI: https://doi.org/10.1016/j.compag.2022.107410
Razak, A., Abdullah, K., Kamarudin, K., Saad, F.S.A., Shukor, S.A., Mustafa, H., Bakar, M.A.A. 2016. Mobile robot structure design, modeling and simulation for confined space application. Proc. 2nd IEEE Int. Symp. on Robotics and Manufacturing Automation (ROMA), Ipoh. pp. 1-5. DOI: https://doi.org/10.1109/ROMA.2016.7847808
Rea, P., Ottaviano, E. 2018. Design and development of an inspection robotic system for indoor applications. Robot. Cim.-Int. Manuf. 49:143-151. DOI: https://doi.org/10.1016/j.rcim.2017.06.005
Ren, G., Lin, T., Ying, Y., Chowdhary, G., Ting, K.C. 2020. Agricultural robotics research applicable to poultry production: a review. Comput. Electron. Agr. 169:105216. DOI: https://doi.org/10.1016/j.compag.2020.105216
Saeedi, B., Sadedel, M. 2021. Implementation of behavior-based navigation algorithm on four-wheel steering mobile robot. J. Computat. Appl. Mech. 52:619-641.
Sun, Y., Guan, L., Chang, Z., Li, C., Gao, Y. 2019. Design of a low-cost indoor navigation system for food delivery robot based on multi-sensor information fusion. Sensors (Basel) 19:4980. DOI: https://doi.org/10.3390/s19224980
Tzitzis, A., Megalou, S., Siachalou, S., Yioultsis, T., Kehagias, A., Tsardoulias, E., et al. 2019. Phase ReLock - Localization of RFID tags by a moving robot. Proc. 13th Eur. Conf. on Antennas and Propagation (EuCAP), Krakow. pp. 1-5.
Xiao, L., Ding, K., Gao, Y., Rao, X. 2019. Behavior-induced health condition monitoring of caged chickens using binocular vision. Comput. Electron. Agr. 156: 254-262. DOI: https://doi.org/10.1016/j.compag.2018.11.022
Xie, D., Chen, L., Liu, L., Chen L., Wang, H. 2022. Actuators and sensors for application in agricultural robots: a review. Machines 10: 13. DOI: https://doi.org/10.3390/machines10100913
Xu, Q., Li, H., Wang, Q., Wang ,C. 2021. Wheel deflection control of agricultural vehicles with four-wheel independent omnidirectional steering. Actuators 10:334. DOI: https://doi.org/10.3390/act10120334
Xue, H., Li, L., Wen, P., Zhang M,. 2023. A machine learning-based positioning method for poultry in cage environments. Comput. Electron. Agr. 208:107764. DOI: https://doi.org/10.1016/j.compag.2023.107764
Yang, H., Chen, L., Ma, Z.B., Chen, M., Zhang, Y., Deng, F., Li, M. 2021. Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator. Comput. Electron. Agr. 181:105946. DOI: https://doi.org/10.1016/j.compag.2020.105946
Yoo, J., Huh, H. 2020. Optimization of three-wheel vehicle roof structures against rollover accidents. Int. J. Automot. Technol. 21:795-804. DOI: https://doi.org/10.1007/s12239-020-0077-9
Yuan, C., Zhang, W., Liu, G., Liu, G., Pan, X. Liu, X. 2019. A heuristic rapidly-exploring random trees method for manipulator motion planning. IEEE Access 8:900-910. DOI: https://doi.org/10.1109/ACCESS.2019.2958876
Zhang, D., Han, X. 2020. Kinematic reliability analysis of robotic manipulator. J. Mech. Design 142:044502. DOI: https://doi.org/10.1115/1.4044436
Zhang, Y., Zhang, M., Li, M. 2022. Agricultural internet of things. In: Ma, S, Lin, T., Mao, E., Song, Z., Ting, G-H. (eds.), Sensing, data managing, and control technologies for agricultural systems.Cham, Springer. pp. 17-40. DOI: https://doi.org/10.1007/978-3-031-03834-1_2
Zhao, J., Wu, C.C., Yang, G.L., Chen, C.Y., Chen, S., Xiang, C.Y., Zhang, C. 2022. Kinematics analysis and workspace optimization for a 4-DOF 3T1R parallel manipulator. Mech. Mach. Theory 167:104484. DOI: https://doi.org/10.1016/j.mechmachtheory.2021.104484

How to Cite

Guo, Y. (2025) “Structural design, modeling and simulation analysis of a cage broiler inspection robot”, Journal of Agricultural Engineering. doi: 10.4081/jae.2025.1806.

Similar Articles

<< < 39 40 41 42 43 44 45 46 47 48 > >> 

You may also start an advanced similarity search for this article.