Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment

Abstract According to the tactical requirements of unmanned aerial vehicle (UAV) for tracking target and avoiding obstacle in complex dynamic environment, a three-dimensional (3D) real-time path planning method is proposed by combing the improved Lyapunov Guidance Vector Field (LGVF), the Interfered Fluid Dynamical System (IFDS) and the strategy of varying receding-horizon optimization from Model Predictive Control (MPC). First, in order to track the moving target in 3D environment, the LGVF method is improved by introducing flight height into the traditional Lyapunov function, and the generated velocity can guide UAV converge gradually to the limit cycle in horizontal plane and the optimal height in vertical plane. Then, the IFDS method imitating the phenomenon of fluid flow is utilized to plan the collision-free path. To achieve the mission of tracking moving target and avoid static or dynamic obstacle at the same time, the guidance vector field by LGVF is taken as the original fluid of IFDS. As the fluid system still remains stable under the influence of obstacles, the disturbed streamline from the interfered fluid can be regarded as the planned path. Third, as the quality of route is mainly influenced by the repulsive and tangential parameters of IFDS, the real-time suboptimal route can be planned by the varying receding-horizon optimization according to the predicted motion. The experimental results prove that the proposed hybrid method is applicable to various dynamic environments.

[1]  Haibin Duan,et al.  Chaotic predator–prey biogeography-based optimization approach for UCAV path planning , 2014 .

[2]  Honglun Wang,et al.  3-D dynamic path planning for UAV based on interfered fluid flow , 2014, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference.

[3]  X Gao,et al.  Developing an effective algorithm for dynamic UAV path planning with incomplete threat information , 2012 .

[4]  Shuzhi Sam Ge,et al.  Dynamic Motion Planning for Mobile Robots Using Potential Field Method , 2002, Auton. Robots.

[5]  Howie Choset,et al.  Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph , 2000, Int. J. Robotics Res..

[6]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[7]  Tal Shima,et al.  Unmanned Aerial Vehicles Cooperative Tracking of Moving Ground Target in Urban Environments , 2008 .

[8]  Wei Liu,et al.  A feedback based CRI approach to fuzzy reasoning , 2011, Appl. Soft Comput..

[9]  Eric W. Frew,et al.  Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control , 2008 .

[10]  Seid H. Pourtakdoust,et al.  Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm , 2013 .

[11]  Yiyuan Zhao,et al.  Trajectory Planning for Autonomous Aerospace Vehicles amid Known Obstacles and Conflicts , 2004 .

[12]  Kuo-Chu Chang,et al.  Tracking with UAV using tangent-plus-Lyapunov vector field guidance , 2009, 2009 12th International Conference on Information Fusion.

[13]  Mohammad A. Jaradat,et al.  Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field , 2012, Soft Comput..

[14]  Javaid Iqbal,et al.  On the Improvement of Multi-Legged Locomotion over Difficult Terrains Using a Balance Stabilization Method: , 2012 .

[15]  Kalyanmoy Deb,et al.  Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms , 2012, Soft Computing.

[16]  Demin Xu,et al.  Intelligent Online Path Planning for UAVs in Adversarial Environments , 2012 .

[17]  Luigi Chisci,et al.  Optimal UAV coordination for target tracking using dynamic programming , 2010, 49th IEEE Conference on Decision and Control (CDC).

[18]  C. Zong-ji,et al.  Study on UAV Path Planning Approach Based on Fuzzy Virtual Force , 2010 .

[19]  Jing Chen,et al.  Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control , 2013 .

[20]  Honglun Wang,et al.  UAV feasible path planning based on disturbed fluid and trajectory propagation , 2015 .

[21]  Edwin K. P. Chong,et al.  UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Edwin K. P. Chong,et al.  Dynamic UAV path planning for multitarget tracking , 2012, 2012 American Control Conference (ACC).

[23]  Hongda Chen,et al.  A dynamic path planning algorithm for UAV tracking , 2009, Defense + Commercial Sensing.

[24]  Myung Hwangbo,et al.  A stable target-tracking control for unicycle mobile robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[25]  Vincent Roberge,et al.  Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning , 2013, IEEE Transactions on Industrial Informatics.

[26]  Stephen Waydo,et al.  Using Stream Functions for Complex Behavior and Path Generation , 2003 .

[27]  Kuo-Chu Chang,et al.  UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[28]  Jonathan P. How,et al.  Real-Time Motion Planning With Applications to Autonomous Urban Driving , 2009, IEEE Transactions on Control Systems Technology.

[29]  Kai-Yuan Cai,et al.  Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval , 2013 .