Track Planning Methods for Unmanned Aerial Vehicles Based on Double-Rolling Windows

As a fundamental technique for cruise of unmanned aerial vehicles, track planning is critical for these vehicles to reach their destinations safely without any collision and along tracks within the minimum time. In this paper, a track planning method based on double-rolling windows that integrates global and local planning is proposed for unmanned aerial vehicles under dynamic and uncertain environment. For global planning, roadmaps are modeled according to known global environmental information to search the optimal tracks. Concerning local planning, unmanned aerial vehicles fly along searched global track points, while their tracks are constantly corrected and optimized with fast algorithms according to detailed environmental information that is detected on a real-time basis. By making full use of global priori information and detected real-time information, the track planning method based on double-rolling windows may not only guarantee optimal tracks for unmanned aerial vehicles, but also adapts to dynamic changes to environment and tasks for the purpose of rolling optimization. After simulation and analysis, this method is proven to be effective and feasible for track planning under predetermined environment.

[1]  Kannan M. Moudgalya.  Model Predictive Control , 2007 .

[2]  Yan Wu,et al.  An Obstacle-Based Probabilistic Roadmap Method For Path Planning , 1996 .

[3]  Jason L. Speyer,et al.  Sensor Fusion Applied to Autonomous Aerial Refueling , 2009 .

[4]  Haibin Duan,et al.  A binocular vision-based measuring system for UAVs autonomous aerial refueling , 2016, 2016 12th IEEE International Conference on Control and Automation (ICCA).

[5]  Min Zhao,et al.  Unmanned aerial vehicle dynamic path planning in an uncertain environment , 2014, Robotica.

[6]  Pei Li,et al.  Chaotic differential evolution approach for 3D trajectory planning of unmanned aerial vehicle , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[7]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[8]  Daniel Hodouin,et al.  Unmanned Aerial Vehicle Optimal Cooperative Obstacle Avoidance in a Stochastic Dynamic Environment , 2011 .

[9]  James J. Winebrake,et al.  Dynamic Modeling of Environmental Systems , 1999, Modeling Dynamic Systems.

[10]  Honglun Wang,et al.  Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment , 2015 .

[11]  Xia Chen,et al.  Dynamic path planning based on improved boundary value problem for unmanned aerial vehicle , 2016, Cluster Computing.

[12]  Radhakant Padhi,et al.  Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles , 2011, J. Aerosp. Comput. Inf. Commun..