Low-Complexity Path Planning Algorithm for Unmanned Aerial Vehicles in Complicated Scenarios

Existing algorithms on path planning with obstacles for unmanned aerial vehicles (UAVs) suffer from high computational complexity and unpredictability when the considered scenario is complicated. In this paper, we propose a novel path-planning algorithm for UAVs, which relies on continuously updating virtual regional field and its local gradients. The information of target regions and obstacles is incorporated in a virtual regional field. The algorithm circumvents the large number of variables to be optimized, and does not rely on any black boxes with unpredictable outputs. Real data show that the proposed algorithm can design a path with high coverage rate of the target region in a certain time duration, and guides the UAV to bypass the obstacles. The approach based on the regional field provides an option for low-cost hardwares, and reveals insights into the problem of path planning.

[1]  Youmin Zhang,et al.  Flatness-Based Trajectory Planning/Replanning for a Quadrotor Unmanned Aerial Vehicle , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Salah Sukkarieh,et al.  3D smooth path planning for a UAV in cluttered natural environments , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Alexander B. Miller,et al.  3D path planning in a threat environment , 2011, IEEE Conference on Decision and Control and European Control Conference.

[4]  Eliot Winer,et al.  Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization , 2009, J. Aerosp. Comput. Inf. Commun..

[5]  Howie Choset,et al.  Coverage for robotics – A survey of recent results , 2001, Annals of Mathematics and Artificial Intelligence.

[6]  Ernest L. Hall,et al.  Region filling operations for mobile robot using computer graphics , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[7]  Rahul Kala,et al.  Rapidly exploring random graphs: motion planning of multiple mobile robots , 2013, Adv. Robotics.

[8]  Sylvia C. Wong,et al.  Qualitative Topological Coverage of Unknown Environments by Mobile Robots , 2006 .

[9]  Giorgio Guglieri,et al.  Path Planning Strategies for UAVS in 3D Environments , 2011, Journal of Intelligent & Robotic Systems.

[10]  Jizhong Xiao,et al.  Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method , 2013, Int. J. Autom. Comput..

[11]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[12]  Ellips Masehian,et al.  Robot Path Planning in 3D Space Using Binary Integer Programming , 2007 .

[13]  Jianli Yu,et al.  3D path planning for mobile robots using annealing neural network , 2009, 2009 International Conference on Networking, Sensing and Control.

[14]  Oktay Baysal,et al.  Path planning for autonomous UAV via vibrational genetic algorithm , 2007 .

[15]  Kwangjin Yang,et al.  Real-time continuous curvature path planning of UAVS in cluttered environments , 2008, 2008 5th International Symposium on Mechatronics and Its Applications.

[16]  Sylvia C. Wong,et al.  A topological coverage algorithm for mobile robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[17]  Syeda Madiha Qamar,et al.  Potential guided directional-RRT* for accelerated motion planning in cluttered environments , 2013, 2013 IEEE International Conference on Mechatronics and Automation.

[18]  Howie Choset,et al.  Morse Decompositions for Coverage Tasks , 2002, Int. J. Robotics Res..

[19]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..