Smoothed A* algorithm for practical unmanned surface vehicle path planning

Abstract An effective path planning or route planning algorithm is essential for guiding unmanned surface vehicles (USVs) between way points or along a trajectory. The A* algorithm is one of the most efficient algorithms for calculating a safe route with the shortest distance cost. However, the route generated by the conventional A* algorithm is constrained by the resolution of the map and it may not be compatible with the non-holonomic constraint of the USV. In this paper an improved A* algorithm has been proposed and applied to the Springer USV. A new path smoothing process with three path smoothers has been developed to improve the performance of the generated route, reducing unnecessary ‘jags’, having no redundant waypoints and offering a more continuous route. Both simulation and experimental results show that the smoothed A* algorithm outperforms the conventional algorithm in both sparse and cluttered environments that have been uniformly rasterised. It has been demonstrated that the proposed improved A* route planning algorithm can be applied to the Springer USV providing promising results when tracking trajectories.

[1]  George W. Irwin,et al.  COLREGs-based collision avoidance strategies for unmanned surface vehicles , 2012 .

[2]  Wenwen Liu,et al.  An interval Kalman filter–based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle , 2016 .

[3]  Thor I. Fossen,et al.  Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles , 2002 .

[4]  Robert Sutton,et al.  Modelling and control of an unmanned surface vehicle for environmental monitoring , 2006 .

[5]  Tao Xu.  An intelligent navigation system for an unmanned surface vehicle , 2007 .

[6]  Andrew V. Goldberg,et al.  Computing the shortest path: A search meets graph theory , 2005, SODA '05.

[7]  Amit Motwani.  Interval Kalman filtering techniques for unmanned surface vehicle navigation , 2015 .

[8]  Giuseppe Casalino,et al.  A three-layered architecture for real time path planning and obstacle avoidance for surveillance USVs operating in harbour fields , 2009, OCEANS 2009-EUROPE.

[9]  RaphaelBertram,et al.  Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths" , 1972 .

[10]  Robert Sutton,et al.  A genetic algorithm based nonlinear guidance and control system for an uninhabited surface vehicle , 2013 .

[11]  Man Zhu,et al.  A Global Path Planning Algorithm of Unmanned Vessel in Inland Waterway , 2013 .

[12]  G. Oriolo,et al.  On-line map building and navigation for autonomous mobile robots , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[13]  J Chudley,et al.  A FUZZY LOGIC BASED MULTI-SENSOR NAVIGATION SYSTEM FOR AN UNMANNED SURFACE VEHICLE , 2006 .

[14]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[15]  Phil F. Culverhouse,et al.  Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control , 2013 .

[16]  Chia Hsun Chiang,et al.  A comparative study of implementing Fast Marching Method and A* SEARCH for mobile robot path planning in grid environment: Effect of map resolution , 2007, 2007 IEEE Workshop on Advanced Robotics and Its Social Impacts.

[17]  Ming-Cheng Tsou,et al.  THE STUDY OF SHIP COLLISION AVOIDANCE ROUTE PLANNING BY ANT COLONY ALGORITHM , 2010 .

[18]  Anupam Shukla,et al.  Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning , 2010, Artificial Intelligence Review.

[19]  J. Chudley,et al.  Soft Computing Design of a Linear Quadratic Gaussian Controller for an Unmanned Surface Vehicle , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[20]  Sebastian Thrun,et al.  Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments , 2010, Int. J. Robotics Res..

[21]  Roman Smierzchalski.  Evolutionary trajectory planning of ships in navigation traffic areas , 1999 .

[22]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[23]  Gábor Bohács,et al.  Development of an Intelligent Path Planning Method for Materials Handling Machinery at Construction Sites , 2016 .

[24]  David Clelland,et al.  Automatic simulation of ship navigation , 2011 .

[25]  Yanlong Wang,et al.  Design and Implementation of Global Path Planning System for Unmanned Surface Vehicle among Multiple Task Points , 2018, ArXiv.

[26]  Richard Bucknall,et al.  Cooperative path planning algorithm for marine surface vessels , 2013 .

[27]  Hyun Myung,et al.  Curvature Path Planning with High Resolution Graph for Unmanned Surface Vehicle , 2012, RiTA.

[28]  Phil F. Culverhouse,et al.  Interval Kalman filtering in navigation system design for an uninhabited surface vehicle , 2013 .

[29]  R Bucknall,et al.  Towards the development of an autonomous navigation system for unmanned vessels , 2015 .

[30]  P. S. Tseng,et al.  Path planning on satellite images for unmanned surface vehicles , 2015 .

[31]  Richard Bucknall,et al.  Path-planning algorithm for ships in close-range encounters , 2010 .

[32]  Robert Sutton,et al.  An integrated multi-sensor data fusion algorithm and autopilot implementation in an uninhabited surface craft , 2012 .

[33]  Yang Song,et al.  A Grid-Based Approach to Formation Reconfiguration for a Class of Robots with Non-Holonomic Constraints , 2012, Eur. J. Control.