Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments

We present a deliberative trajectory planning method to avoid collisions with traffic vessels. It also plans traversal across wavefields generated by these vessels and minimizes the risk of failure. Our method searches over a state-space consisting of pose and time. And, it produces collision-free and minimum-risk trajectory. It uses a lookup table to account for motion uncertainty and failure risk. We also present speed-up techniques to increase performance. Our wave-aware planner produces plans that (1) have shorter execution times and safer when compared to previously developed reactive planning schemes and (2) comply with user-defined wave-traversal constraints and Collision Regulations (COLREGs)

[1]  Ivan R. Bertaska,et al.  Control of an Unmanned Surface Vehicle With Uncertain Displacement and Drag , 2017, IEEE Journal of Oceanic Engineering.

[2]  Irfan A. Essa,et al.  Gaussian process regression flow for analysis of motion trajectories , 2011, 2011 International Conference on Computer Vision.

[3]  Ashitey Trebi-Ollennu,et al.  Towards a Predictive Model of Mobile Robot Reliability , 2005 .

[4]  Satyandra K. Gupta,et al.  Dynamics-aware target following for an autonomous surface vehicle operating under COLREGs in civilian traffic , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Alonzo Kelly,et al.  Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control , 2003, Int. J. Robotics Res..

[6]  Giuseppe Oriolo,et al.  A Bayesian framework for optimal motion planning with uncertainty , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Jana Fuhrmann,et al.  Guidance And Control Of Ocean Vehicles , 2016 .

[8]  Eric Thornhill,et al.  Observations of predictive skill for real-time Deterministic Sea Waves from the WaMoS II , 2015, OCEANS 2015 - MTS/IEEE Washington.

[9]  Dominique Gruyer,et al.  A fast Monte Carlo algorithm for collision probability estimation , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[10]  Max Q.-H. Meng,et al.  Risk-DTRRT-Based Optimal Motion Planning Algorithm for Mobile Robots , 2019, IEEE Transactions on Automation Science and Engineering.

[11]  Satyandra K. Gupta,et al.  GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning , 2012, Robotics Auton. Syst..

[12]  Luis Moreno,et al.  Nonholonomic Motion Planning Using the Fast Marching Square Method , 2015 .

[13]  Jonathan P. How,et al.  Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns , 2013, Auton. Robots.

[14]  Lily Rachmawati,et al.  Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology , 2016, IEEE Transactions on Intelligent Transportation Systems.

[15]  Joel Veness,et al.  Monte-Carlo Planning in Large POMDPs , 2010, NIPS.

[16]  Nancy M. Amato,et al.  FIRM: Feedback controller-based information-state roadmap - A framework for motion planning under uncertainty , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Ariel Felner,et al.  Theta*: Any-Angle Path Planning on Grids , 2007, AAAI.

[18]  Steven Dubowsky,et al.  Hazard avoidance for high‐speed mobile robots in rough terrain , 2006, J. Field Robotics.

[19]  Satyandra K. Gupta,et al.  Wave-Aware Trajectory Planning for Unmanned Surface Vehicles Operating in Congested Environments , 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[20]  Vijay Kumar,et al.  Automated Self-Assembly of Large Maritime Structures by a Team of Robotic Boats , 2015, IEEE Transactions on Automation Science and Engineering.

[21]  Thierry Fraichard,et al.  Tiji, a generic trajectory generation tool for motion planning and control , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  David Hsu,et al.  DESPOT: Online POMDP Planning with Regularization , 2013, NIPS.

[23]  Nicholas Roy,et al.  Rapidly-exploring Random Belief Trees for motion planning under uncertainty , 2011, 2011 IEEE International Conference on Robotics and Automation.

[24]  Maxim Likhachev,et al.  Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles , 2008, Int. J. Robotics Res..

[25]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014 .

[26]  L. Dubins.  On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[27]  Håkan Jonsson,et al.  Planning Smooth and Obstacle-Avoiding B-Spline Paths for Autonomous Mining Vehicles , 2010, IEEE Transactions on Automation Science and Engineering.

[28]  Dinesh Manocha,et al.  Generalized velocity obstacles , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Alois Knoll,et al.  Motion planning under perception and control uncertainties with Space Exploration Guided Heuristic Search , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[30]  Satyandra K. Gupta,et al.  Trajectory planning with look-ahead for Unmanned Sea Surface Vehicles to handle environmental disturbances , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[31]  Gaurav S. Sukhatme,et al.  A Solution to Time-Varying Markov Decision Processes , 2016, IEEE Robotics and Automation Letters.

[32]  J. G. Kusters,et al.  FutureWaves™: A real-time Ship Motion Forecasting system employing advanced wave-sensing radar , 2016, OCEANS 2016 MTS/IEEE Monterey.

[33]  Yuanchang Liu,et al.  A practical path planning and navigation algorithm for an unmanned surface vehicle using the fast marching algorithm , 2015, OCEANS 2015 - Genova.

[34]  Karl D. von Ellenrieder,et al.  Station-keeping control of an unmanned surface vehicle exposed to current and wind disturbances , 2016, ArXiv.

[35]  Fredrik Heintz,et al.  Online sparse Gaussian process regression for trajectory modeling , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[36]  Satyandra K. Gupta,et al.  Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic , 2016, Auton. Robots.

[37]  Christian Laugier,et al.  Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian processes , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[38]  Alois Knoll,et al.  Heuristic search in belief space for motion planning under uncertainties , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[39]  Lars Linsen,et al.  Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction , 2014 .

[40]  Sebastian Thrun,et al.  Coastal Navigation with Mobile Robots , 1999, NIPS.

[41]  Hongbin Zha,et al.  A real-time motion planner with trajectory optimization for autonomous vehicles , 2012, 2012 IEEE International Conference on Robotics and Automation.

[42]  Masahiro Ono,et al.  Chance-Constrained Optimal Path Planning With Obstacles , 2011, IEEE Transactions on Robotics.

[43]  Vijay Kumar,et al.  Control and planning for vehicles with uncertainty in dynamics , 2010, 2010 IEEE International Conference on Robotics and Automation.

[44]  Franz S. Hover,et al.  Motion planning with an analytic risk cost for holonomic vehicles , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[45]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..