![](/images/paper-icn.png)
Unmanned Target Vehicle Navigation and Path Planning Using Improved Ant Colony Optimization Algorithm Combined with GPS/BDS
With the rise of technology, mobile unmanned target vehicles have become common in military training. These vehicles carry shooting targets, offering troops mobile targets for shooting practice and enhancing simulated training effectiveness. However, in complex terrains, the movement path of unmanned vehicles falls short of achieving desired results. Path planning for unmanned target vehicles has therefore gained importance. To optimize their movement and enhance military training, a robust adaptive positioning algorithm based on Global Positioning System/Bei Dou System (GPS/BDS) technology is proposed. This algorithm ensures precise positioning and trajectory prediction. Additionally, the Ant Colony Optimization (ACO) algorithm is improved by considering heuristic factors and defining restricted regions, optimizing the trajectory. Simulation experiments demonstrate high-precision navigation and positioning, reducing signal propagation time and improving smoothness and directionality. The path length approximates the optimal path with minimal error. Comparative experiments confirm the algorithm's accuracy and efficiency in planning paths with speed, fewer turns, and improved smoothness. This optimization helps unmanned target vehicles enhance their trajectories and improve military training effectiveness.
[1] Abdalla M., Mokhtar H., and Elgamal N., “HarmonyMoves: A Unified Prediction Approach for Moving Object Future Path,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 1, pp. 637-644, 2020. https://thesai.org/Downloads/Volume11No1/Paper_78- HarmonyMoves_A_Unified_Prediction_Approach.pdf
[2] Bao H., Du T., and Sun L., “Adaptive Attitude Determination of Bionic Polarization Integrated Navigation System Based on Reinforcement Learning Strategy,” Mathematical Foundations of Computing, vol. 6, no. 2, pp. 161-177, 2023. Doi: 10.3934/mfc.2022014
[3] Boyineni S., Kavitha K., and Sreenivasulu M., “Mobile Sink-based Data Collection in Event- Driven Wireless Sensor Networks Using a Modified Ant Colony Optimization,” Physical Communication, vol. 52, no. 6, pp. 10-23, 2022. https://doi.org/10.1016/j.phycom.2022.101600
[4] Che G., Liu L., and Yu Z., “An Improved Ant Colony Optimization Algorithm Based on Particle Swarm Optimization Algorithm for Path Planning of Autonomous Underwater Vehicle,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 8, pp. 3349-3354, 2020. https://doi.org/10.1007/s12652-019-01531-8
[5] Chen H. and Zhang Y., “Dynamic Path Optimization in Sharing Mode to Relieve Urban Traffic Congestion,” Discrete Dynamics Nature and Society, vol. 2021, no. 7, pp. 1-16, 2021. https://doi.org/10.1155/2021/8874957
[6] Cheng H., Liao W., Yang M., Rosenhahn B., and Sester M., “AMENet: Attentive Maps Encoder Network for Trajectory Prediction” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 172, no. 3, pp. 253-266, 2021. https://doi.org/10.1016/j.isprsjprs.2020.12.004
[7] Cui Y., Ren J., and Zhang Y., “Path Planning Algorithm for Unmanned Surface Vehicle Based on Optimized Ant Colony Algorithm,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 17, no. 7, pp. 1027-1037, 2022. DOI: 10.1002/tee.23592
[8] Dey M., Das A., Banerjee A., Kamila U., and Chattopadhyay S., “Construction of Efficient Wireless Sensor Networks for Energy Minimization Using a Modified ACO Algorithm,” International Journal of Sensors Wireless Communications and Control, vol. 11, no. 9, pp. 928-950, 2021. https://doi.org/10.2174/221032791166621012012261 0
[9] Fang Y., Zhao W., Lin F., Wang D., and Wang J., “An AMC-Based Liquid Sensor Optimized by Particle-Ant Colony Optimization Algorithms,” IEEE Sensors Journal, vol. 22, no. 3, pp. 2083- 2090, 2022. DOI: 10.1109/JSEN.2021.3133688
[10] Fraser C. and Ulrich S., “Adaptive Extended Kalman Filtering Strategies for Spacecraft Formation Relative Navigation,” Acta Astronautica, vol. 178, no. 1, pp. 700-721, 2021. https://doi.org/10.1016/j.actaastro.2020.10.016
[11] Gutmann S., Tstensen C., Bttcher I., Dietzel J., and Loderstedt S., “Clinical Use of a New Frameless Optical Neuronavigation System for Brain Biopsies: 10 Cases (2013–2020),” Journal of Small Animal Practice, vol. 63, no. 6, pp. 468- 481, 2022. DOI: 10.1111/jsap.13482
[12] Hong J., Tu R., Zhang R., Fan L., and Zhang P., “Contribution Analysis of QZSS to Single- Unmanned Target Vehicle Navigation and Path Planning Using Improved Ant Colony ... 613 Frequency PPP of GPS/BDS/GLONASS/Galileo,” Advances in Space Research., vol. 65, no. 7, pp. 1803-1837, 2020. https://doi.org/10.1016/j.asr.2020.01.003
[13] Jao C. and Shkel A., “A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient Far in Foot-Mounted Inertial Navigation System,” IEEE Sensors Journal, vol. 22, no. 1, pp. 695-706, 2022. doi:10.1109/JSEN.2021
[14] Lin P., Yang J., Quan Y., and Chung C., “Potential Field-based Path Planning for Emergency Collision Avoidance with a Clothoid Curve in Waypoint Tracking,” Asian Journal of Control, vol. 24, no. 3, pp. 1074-1087, 2022. https://doi.org/10.1002/asjc.2778
[15] Mahfoud S., Derouich A., Iqbal A., and Ouanjli N., “ANT-Colony Optimization-Direct Torque Control for a Doubly Fed Induction Motor: An Experimental Validation,” Energy Reports, vol. 8, no. 10, pp. 81-98, 2022. https://doi.org/10.1016/j.egyr.2021.11.239
[16] Qian N., Gao J., Li Z., Fang C., and Pan C., “GPS/BDS Triple-Frequency Cycle Slip Detection and Repair Algorithm Based on Adaptive Detection Threshold and FNN-Derived Ionospheric Delay Compensation,” Acta Geodynamica et Geomaterialia, vol. 17, no. 2, pp. 141-156, 2020. DOI: 10.13168/AGG.2020.0010
[17] Sathiya V., Chinnadurai M., and Ramabalan S., “Mobile Robot Path Planning Using Fuzzy Enhanced Improved Multi-Objective Particle Swarm Optimization,” Expert Systems with Applications, vol. 198, no. 7, pp. 12-24, 2022. https://doi.org/10.1016/j.eswa.2022.116875
[18] Shi Y., Li J., Wang G., Bai S., and Bian G., “Establish Basic Reseach Funding Navigation System, Promote Technical Innovation to Underpin Development of National Modern Industrial System,” Bulletin of Chinese Academy of Sciences, vol. 35, no. 5, pp. 573-579, 2022. https://doi.org/10.16418/j.issn.1000- 3045.20210407007
[19] Tao L., Wu J., Ge D., Chen L., and Sun M., “Risk- Informed Based Comprehensive Path-Planning Method for Radioactive Materials Road Transportation,” Reliability Engineering and System Safety, vol. 219, no. 3, pp. 6-12, 2022. https://doi.org/10.1016/j.ress.2021.108228
[20] Tian Y., Yuan L., Huang D., Zhou L., and Chen X., “Analysis of the Code and Phase Between- Receiver Inter-System Biases of the Overlapping Frequencies for GPS/Galileo/BDS,” Advances in Space Research, vol. 65, no. 4, pp. 1196-1209, 2020. https://doi.org/10.1016/j.asr.2019.11.022
[21] Tu R., Zhang R., Liu Z., Fan L., and Han J., “Real- Time Detection of BDS Orbit Manoeuvres Based on the Combination of GPS and BDS Observations,” IET Radar, Sonar and Navigation, vol. 14, no. 10, pp. 1603-1609, 2020. https://doi.org/10.1049/iet-rsn.2020.0171
[22] Wang E., Jia C., Qu P., Huang Y., and Jiang Y., “BDS/GPS Integrated Navigation Satellite Selection Algorithm Based on Chaos Particle Swarm Optimization,” Journal of Beijing University of Aeronautics and Astronautics, vol. 45, no. 2, pp. 259-265, 2019. doi: 10.13700/j.bh.1001-5965.2018.0281
[23] Wu D., Zhu Z., Hu D., and Mansour R., “Optimizing Fresh Logistics Distribution Route Based on Improved Ant Colony Algorithm,” Computers, Materials and Continua, vol. 000, no. 10, pp. 2079-2095, 2022. DOI:10.32604/cmc.2022.027794
[24] Xu S., “Research on Anomaly Detection Method for Hybrid Big Data Subarea Based on Ant Colony Algorithm,” International Journal of Information and Communication Technology, vol. 17, no. 2, pp. 164-177, 2020. https://doi.org/10.1504/IJICT.2020.108965
[25] Xu T., Zhou H., Tan S., Li Z., and Ju X., “Mechanical Arm Obstacle Avoidance Path Planning Based on Improved Artificial Potential Field Method,” Industrial Robot, vol. 49, no. 2, pp. 271-279, 2022. https://doi.org/10.1108/IR-06- 2021-0120
[26] Zhang P., Tu R., Han J., Gao Y., Zhang R., and Lu X., “Characterization of Biases Between BDS-3 and BDS-2, GPS, Galileo and GLONASS Observations and Their Effect on Precise Time and Frequency Transfer,” Measurement Science and Technology, vol. 32, no. 3, pp. 2-13, 2021. DOI:10.1088/1361-6501/abc963