SIMULASI ADAPTIVE PURSUIT ROUTE ALGORITHM (APRA) UNTUK ALGORITMA INTERSEPSI ADAPTIF BERBASIS PREDIKSI DINAMIS UNTUK PERENCANAAN JALUR TARGET BERGERAK DENGAN MANUVER TINGGI

Authors

  • Devanta Abraham Tarigan

DOI:

https://doi.org/10.54314/jssr.v9i3.6374

Keywords:

adaptive interception, path planning, moving target, dynamic prediction, pursuit guidance

Abstract

Intercepting a highly maneuvering moving target remains a difficult planning problem because the target may change heading, accelerate, halt, or reverse without warning, while the pursuer only receives noisy position observations. This study proposes the Adaptive Pursuit Route Algorithm (APRA), an interception path-planning method that combines short-horizon target-state estimation, time-to-go aim-point projection, dynamically weighted cost terms, and a bounded residual that is learned online from past prediction errors. APRA is evaluated against three classical pursuit laws-Pure Pursuit, Proportional Navigation (PN), and Augmented PN (APN)-on a physics-based synthetic dataset of 3,200 trajectories spanning eight motion regimes, yielding 12,800 engagements. APRA attains the best result on every metric: an interception rate of 90.91% (95% CI 0.899-0.919), the shortest mean interception time (9.65 s), the lowest mean prediction error (8.73 units), the highest path efficiency (0.798), and the lowest control energy (48,022). One-way ANOVA confirms that the differences in time, energy, and prediction error are statistically significant (p < 0.001), and a Welch t-test shows APRA is significantly faster than APN (t = -14.48, p < 0.001). The results indicate that disciplined filtering combined with bounded online adaptation is more robust against deceptive and abrupt maneuvers than raw lead-based guidance.

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References

R. C. Coulter, “Implementation of the pure pursuit path tracking algorithm,” Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA, Tech. Rep. CMU-RI-TR-92-01, 1992.

H. Wang, Y. Lu, X. Chen, and Z. Su, “An improved pure pursuit path tracking algorithm,” J. Adv. Comput. Intell. Intell. Inform., vol. 28, no. 4, pp. 1034–1042, 2024.

A. R. Cabrera et al., “Look-ahead distance optimization for pure pursuit using a salp swarm algorithm,” IEEE Access, vol. 8, pp. 168830–168841, 2020.

Y. Li et al., “Pure pursuit path tracking with sideslip compensation for high-speed autonomous vehicles,” in Proc. IEEE Intell. Veh. Symp. (IV), 2023, pp. 1–7.

S. Park and H. Kim, “Accurate path tracking by adjusting the look-ahead point,” Int. J. Automot. Technol., vol. 22, no. 1, pp. 119–131, 2021.

M. Quan et al., “Path following for mobile robots using deep reinforcement learning and pure pursuit,” Sensors, vol. 24, no. 10, 2024, Art. no. 3174.

P. Zhou and M. Tang, “On the capturability of augmented pure proportional navigation guidance,” J. Guid. Control Dyn., vol. 37, no. 5, pp. 1655–1660, 2014, doi: 10.2514/1.G000561.

J. Hu, L. Wang, T. Hu, C. Guo, and Y. Wang, “Interception guidance of maneuvering targets with deep reinforcement learning,” Int. J. Aerosp. Eng., vol. 2023, Art. no. 7924190, 2023.

B. Gaudet, R. Furfaro, and R. Linares, “Intercept strategy for maneuvering target based on deep reinforcement learning,” in Proc. IEEE Conf. Decis. Control (CDC), 2021, pp. 4571–4577.

Y. Chen et al., “Maneuvering target interception via deep reinforcement learning with line-of-sight rate,” Eng. Appl. Artif. Intell., vol. 142, 2025, Art. no. 109876.

Z. Li, Y. Xia, and C. Su, “Moving target interception considering dynamic environment,” arXiv:2205.07772, 2022.

J. Wang et al., “Predictive hierarchical reinforcement learning for path-efficient mapless navigation toward a moving target,” Neural Netw., vol. 165, pp. 605–619, 2023.

L. Guo, X. Yang, and H. Zhang, “Short-term maneuvering target trajectory prediction based on DTW-CNN-LSTM,” Int. J. Aerosp. Eng., vol. 2025, Art. no. 5512389, 2025.

C. Liu, Y. Zhao, and J. Sun, “UAV target prediction and tracking based on KCF and Kalman filter,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 2022, pp. 6712–6718.

M. Alshaer, T. Garouani, and S. Lecoeuche, “Vision-based UAV detection and tracking using deep learning and Kalman filter,” Comput. Intell., vol. 41, no. 1, 2025.

R. Kumar and A. Singh, “Advanced algorithms for UAV tracking under start-stop and irregular motion,” Sci. Rep., vol. 15, Art. no. 11342, 2025.

S. Lee and J. Park, “Deep recurrent reinforcement learning for intercept guidance under partial observability,” Appl. Artif. Intell., vol. 38, no. 1, 2024.

H. Zhang, K. Wang, and L. Li, “UAV trajectory tracking via RNN-aided interacting multiple model Kalman filter,” arXiv:2312.15721, 2023.

B. Gaudet and R. Furfaro, “Reinforcement learning for angle-only intercept guidance of maneuvering targets,” arXiv:1906.02113, 2019.

D. A. Tarigan, M. Zarlis, and R. W. Sembiring, “Reverse tracking graph based on dynamic path planning,” InfoTekJar: J. Nas. Inform. dan Teknol. Jaringan, vol. 6, no. 1, 2021, doi: 10.30743/infotekjar.v6i1.4355.

P. Zarchan, Tactical and Strategic Missile Guidance, 6th ed. Reston, VA, USA: AIAA, 2012.

M. Guelman, “A qualitative study of proportional navigation,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-7, no. 4, pp. 637–643, 1971.

N. A. Shneydor, Missile Guidance and Pursuit: Kinematics, Dynamics and Control. Chichester, U.K.: Horwood, 1998.

P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern., vol. 4, no. 2, pp. 100–107, 1968.

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math., vol. 1, pp. 269–271, 1959.

S. M. LaValle, Planning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2006.

S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA, USA: MIT Press, 2005.

V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, pp. 529–533, 2015.

T. P. Lillicrap et al., “Continuous control with deep reinforcement learning,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2016.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Pr

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Published

2026-06-06

Issue

Vol. 9 No. 3 (2026): June 2026

Section

Artikel

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Copyright (c) 2026 Devanta Abraham Tarigan

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How to Cite

SIMULASI ADAPTIVE PURSUIT ROUTE ALGORITHM (APRA) UNTUK ALGORITMA INTERSEPSI ADAPTIF BERBASIS PREDIKSI DINAMIS UNTUK PERENCANAAN JALUR TARGET BERGERAK DENGAN MANUVER TINGGI. (2026). JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 9(3), 3104-3113. https://doi.org/10.54314/jssr.v9i3.6374