Abstract
This paper aims to find a reliable, collision-free path in a dynamic environment for highly maneuverable unmanned combat air vehicles (UCAVs). Given the real-time nature of the operational scenario, quick and adaptable reactions of UCAVs are necessary for updates in situational awareness. Therefore, we propose a three dimensional (3D) path planning approach based on the situational space to provide the tactical requirements of UCAVs for tracking targets and avoiding collisions. First, to ensure reliable nonlinear measurements, the interacting multiple model (IMM) algorithm based on a cubature Kalman filter (CKF) is chosen for the tracking and prediction algorithm. A constraint reference frame combining the kinematic model of constant acceleration (CA) is developed to solve the problem of arrival point generation. Second, by analyzing the relative motion between the UCAV and the moving objects, we define the situation space and give the corresponding calculation method. In tracking the moving target, the guidance vector contains the fusion information of displacement and velocity. At the same time, taking advantage of the one-step situation space as the judgment of the threat, we further plan the collision avoidance strategy. Third, as the safety in a practically reachable trajectory of the UCAV possesses the absolute priority, the collision avoidance acceleration accounts for this dominant factor in path planning. Simulations and experimental results prove that the proposed approach can plan a smooth and flyable path in 0.008 s under the premise of soft-landing target tracking.
Similar content being viewed by others
References
Biswas, K., Kar, I.: On reduction of oscillations in target tracking by artificial potential field method. In: International Conference on Industrial and Information Systems (2015)
Chen, X., Zhang, J.: The three-dimension path planning of UAV based on improved artificial potential field in dynamic environment. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 144–147 (2013)
Cho, N., Kim, Y.: Optimality of augmented ideal proportional navigation for maneuvering target interception. IEEE Trans. Aerosp. Electron. Syst. 52(2), 948–954 (2016)
Ding, F.G., Jiao, P., Bian, X.Q., Wang, H.J.: AUV local path planning based on virtual potential field. In: IEEE International Conference Mechatronics and Automation, 2005, vol. 4, pp. 1711–1716 (2005)
Dobson, A., Moustakides, G.V., Bekris, K.E.: Geometric probability results for bounding path quality in sampling-based roadmaps after finite computation. In: IEEE International Conference on Robotics and Automation, pp. 4180–4186 (2015)
Duan, H., Yu, Y., Zhang, X., Shao, S.: Three-dimension path planning for UCAV using hybrid meta-heuristic aco-de algorithm. Simul. Model. Pract. Theory 18(8), 1104–1115 (2010)
Fasano, G., Forlenza, L., Tirri, A.E., Accardo, D., Moccia, A.: Multi-sensor data fusion: a tool to enable UAS integration into civil airspace. In: Digital Avionics Systems Conference, pp. 1–28 (2011)
Fernandez, J., Sánchez, P S, Tinao, I., Porter, J., Ezquerro, J.M.: The CFVib experiment: control of fluids in microgravity with vibrations. Microgravity Sci. Technol. 29(5), 351–364 (2017)
Foo, J.L., Knutzon, J., Oliver, J., Winer, E.: Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization. In: Aiaa/issmo Multidisciplinary Analysis & Optimization Conference (2006)
Ge, S.S., Cui, Y.J.: Dynamic motion planning for mobile robots using potential field method. Auton. Robot. 13(3), 207–222 (2002)
Hanson, Richardson, Girard: Path planning of a Dubins vehicle for sequential target observation with ranged sensors, pp. 1698–1703 (2011)
Jeyaraman, S., Tsourdos, A., Zbikowski, R., White, B.: Formal techniques for the modelling and validation of a co-operating UAV team that uses dubins set for path planning. In: Proceedings of the American Control Conference, 2005, vol. 7, pp. 4690–4695 (2005)
Karimi, J., Pourtakdoust, S.H.: Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm. Aerosp. Sci. Technol. 26(1), 60–71 (2013)
Lu, J.Y., Dong, Z.N., Zhang, M.Y.: A fuzzy virtual force based approach to multiple UAVs collaborative path planning. In: Guidance, Navigation and Control Conference, pp. 1245–1251 (2015)
Nannicini, G., Delling, D., Liberti, L., Schultes, D.: Bidirectional a* search for time-dependent fast paths. J. Am. Chem. Soc. 136(24), 8626–8641 (2008)
Paul, T., Krogstad, T.R., Gravdahl, J.T.: Modelling of UAV formation flight using 3D potential field. Simul. Model. Pract. Theory 16(9), 1453–1462 (2008)
Peng, Wang, Zikang, Honglun: UAV Feasible path planning based on disturbed fluid and trajectory propagation. Chin. J. Aeronaut. 28(4), 1163–1177 (2015)
Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inf. 9(1), 132–141 (2012)
Saunders, J.B.: Obstacle avoidance, visual automatic target tracking, and task allocation for small unmanned air vehicles. Dissertations & Theses - Gradworks (2009)
Sullivan, J., Waydo, S., Campbell, M.: Using stream functions for complex behavior and path generation. In: Accepted: Aiaa Guidance, Navigation, and Control Conference (2013)
Wan, M., Li, P., Li, T.: Tracking maneuvering target with angle-only measurements using IMM algorithm based on CKF. In: International Conference on Communications and Mobile Computing, pp. 92–96 (2010)
White, B.A., Shin, H.S., Tsourdos, A.: UAV Obstacle avoidance using differential geometry concepts. IFAC Proceedings Volumes 44(1), 6325–6330 (2011)
Yang, H.I., Zhao, Y.J.: Trajectory planning for autonomous aerospace vehicles amid known obstacles and conflicts. J. Guid. Control Dynam. 27(6), 997–1008 (2004)
Yao, P., Wang, H., Su, Z.: Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment. Aerosp. Sci. Technol. 47, 269–279 (2015)
Zhang, B., Liu, W., Mao, Z., Liu, J., Shen, L.: Cooperative and geometric learning algorithm (CGLA) for path planning of uavs with limited information. Automatica 50(3), 809–820 (2014)
Zhang, D., Xian, Y., Li, J., Lei, G., Chang, Y.: Uav path planning based on chaos ant colony algorithm. In: International Conference on Computer Science and Mechanical Automation, pp. 81–85 (2015)
Zhu, L., Cheng, X., Yuan, F.G.: A 3D collision avoidance strategy for UAV with physical constraints. Measurement 77, 40–49 (2016)
Zucker, M., Kuffner, J., Branicky, M.: Multipartite RRTs for rapid replanning in dynamic environments. In: IEEE International Conference on Robotics and Automation, pp. 1603–1609 (2007)
Acknowledgements
Research reported in this article was supported by the Radar & Electronic Warfare Team of Harbin Engineering University. This work was also supported by the National Natural Science Foundation of China (61571146). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that helped improve the quality of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
You, S., Gao, L. & Diao, M. Real-Time Path Planning Based on the Situation Space of UCAVs in a Dynamic Environment. Microgravity Sci. Technol. 30, 899–910 (2018). https://doi.org/10.1007/s12217-018-9650-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12217-018-9650-5