Multi-Layer Coverage Path Planner for Autonomous Structural Inspection of High-Rise Structures

In this paper, a novel 3D coverage path planning method, which is efficient and practical for inspection of high-rise structures such as buildings or towers, using an unmanned aerial vehicle (UAV)is presented. Our approach basically focuses on developing a model-based path planner for structural inspection with a prior map, which is opposite to a non-model based exploration. The proposed method uses a volumetric map which is made before the path planning. With the map, the whole structure is divided into several layers for efficient path planning. Firstly, in each layer, a set of the normal vectors of the center point of every voxel is calculated, and then the opposing vectors become viewpoints. Due to too many viewpoints and an overlapped inspection surface, we down-sample them with a voxel grid filter. Then, the shortest tour connecting the reduced viewpoints must be computed with the Traveling Salesman Problem (TSP)solver. Lastly, all the paths in each layer are combined to form the complete path. The results are verified using simulations with a rotary wing UAV and compared with other state-of-the-art algorithm. It is proven that our method performs much better for structural inspection with respect to computation time as well as the coverage completeness,

[1]  Franz S. Hover,et al.  Advanced perception, navigation and planning for autonomous in-water ship hull inspection , 2012, Int. J. Robotics Res..

[2]  Roland Siegwart,et al.  Structural inspection path planning via iterative viewpoint resampling with application to aerial robotics , 2015, ICRA 2015.

[3]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[4]  Pere Ridao,et al.  Coverage path planning with realtime replanning for inspection of 3D underwater structures , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Roland Siegwart,et al.  Uniform coverage structural inspection path–planning for micro aerial vehicles , 2015, 2015 IEEE International Symposium on Intelligent Control (ISIC).

[6]  Lakmal Seneviratne,et al.  A survey on inspecting structures using robotic systems , 2016 .

[7]  Hyun Myung,et al.  Graph-based SLAM (Simultaneous Localization And Mapping) for Bridge Inspection Using UAV (Unmanned Aerial Vehicle) , 2017 .

[8]  Oskar von Stryk,et al.  Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo , 2012, SIMPAR.

[9]  Vijay Kumar,et al.  Inspection of Penstocks and Featureless Tunnel-like Environments Using Micro UAVs , 2013, FSR.

[10]  Hyun Myung,et al.  Mechanism and system design of MAV(Micro Aerial Vehicle)-type wall-climbing robot for inspection of wind blades and non-flat surfaces , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[11]  C. Ian Connolly,et al.  The determination of next best views , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[12]  Hyun Myung,et al.  High‐speed 6‐DOF structural displacement monitoring by fusing ViSP (Visually Servoed Paired structured light system) and IMU with extended Kalman filter , 2017 .

[13]  G. Roth,et al.  View planning for automated three-dimensional object reconstruction and inspection , 2003, CSUR.

[14]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[15]  Adi Rosén,et al.  Semi-Streaming Set Cover , 2014, ACM Trans. Algorithms.

[16]  Sungho Jo,et al.  Online inspection path planning for autonomous 3D modeling using a micro-aerial vehicle , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Vijay Kumar,et al.  Time-optimal UAV trajectory planning for 3D urban structure coverage , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Karl Henrik Johansson,et al.  Robust area coverage using hybrid control , 2004 .

[19]  Roland Siegwart,et al.  Receding Horizon "Next-Best-View" Planner for 3D Exploration , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[21]  Keld Helsgaun,et al.  An effective implementation of the Lin-Kernighan traveling salesman heuristic , 2000, Eur. J. Oper. Res..