Roboat: An Autonomous Surface Vehicle for Urban Waterways

Unmanned surface vehicles (USVs) are typically designed for open area marine applications. In this paper, we present a new autonomy system (Roboat) for urban waterways which requires robust localization, perception, planning, and control. A novel localization system, based on the extended Kalman filter (EKF), is proposed for USVs, which utilizes LiDAR, camera, and IMU to provide a decimeter-level precision in dynamic GPS-attenuated urban waterways. Area and shape filters are proposed to crop water reflections and street obstacles from a pointcloud. Euclidean clustering and multi-object contour tracking are then introduced to detect and track the static and moving objects reliably in urban waters. An efficient path planner is tailored to calculate optimal trajectories to avoid these static and dynamic obstacles. Lastly, a nonlinear model predictive control (NMPC) scheme with full state integration is formulated for the four-control-input robot to accurately track the trajectory from the planner in rough water. Extensive experiments show that the robot is able to autonomously navigate in both the indoor waterway and the cluttered outdoor waterway in the presence of static and dynamic obstacles, implying that Roboat could have a great impact on the future of transportation in many coastal and riverside cities.

[1]  J. Curcio,et al.  SCOUT - a low cost autonomous surface platform for research in cooperative autonomy , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[2]  Shinpei Kato,et al.  An Open Approach to Autonomous Vehicles , 2015, IEEE Micro.

[3]  Jules-Raymond Tapamo,et al.  Detection and tracking of moving objects in a maritime environment using level set with shape priors , 2013, EURASIP J. Image Video Process..

[4]  Shinpei Kato,et al.  Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments , 2017, J. Robotics Mechatronics.

[5]  David John Thompson,et al.  Maritime Object Detection, Tracking, and Classification Using Lidar and Vision-Based Sensor Fusion , 2017 .

[6]  Sajad Saeedi,et al.  AUV Navigation and Localization: A Review , 2014, IEEE Journal of Oceanic Engineering.

[7]  Forrest Voight,et al.  Team NaviGator AMS 1 of 11 University of Florida : Team NaviGator AMS , 2016 .

[8]  Roger Skjetne,et al.  A Nonlinear Ship Manoeuvering Model: Identification and adaptive control with experiments for a model ship , 2004 .

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

[10]  Rita Cunha,et al.  Trajectory Tracking Nonlinear Model Predictive Control for Autonomous Surface Craft , 2009, IEEE Transactions on Control Systems Technology.

[11]  Wei Wang,et al.  Design. Modeling, and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Han Wang,et al.  Vision based long range object detection and tracking for unmanned surface vehicle , 2015, 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[13]  Christopher Assad,et al.  360‐degree visual detection and target tracking on an autonomous surface vehicle , 2010, J. Field Robotics.

[14]  John J. Leonard,et al.  Navigation of unmanned marine vehicles in accordance with the rules of the road , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[15]  Guangming Xie,et al.  Coordination of Multiple Robotic Fish With Applications to Underwater Robot Competition , 2016, IEEE Transactions on Industrial Electronics.

[16]  M. Z. Md. Zain,et al.  Review of Course Keeping Control System for Unmanned Surface Vehicle , 2015 .

[17]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[18]  Carrick Detweiler,et al.  Complete SE3 underwater robot control with arbitrary thruster configurations , 2010, 2010 IEEE International Conference on Robotics and Automation.

[19]  Peter I. Corke,et al.  Experiments with Underwater Robot Localization and Tracking , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

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

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

[22]  J.E. Manley,et al.  Unmanned surface vehicles, 15 years of development , 2008, OCEANS 2008.

[23]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[24]  Kenneth R. Muske,et al.  Sliding-Mode Tracking Control of Surface Vessels , 2008, IEEE Transactions on Industrial Electronics.

[25]  Gaurav S. Sukhatme,et al.  Experiments in robotic boat localization , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Zhixiang Liu,et al.  Unmanned surface vehicles: An overview of developments and challenges , 2016, Annu. Rev. Control..

[27]  George W. Irwin,et al.  A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres , 2012, Annu. Rev. Control..

[28]  Radu Bogdan Rusu,et al.  Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments , 2010, KI - Künstliche Intelligenz.

[29]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[30]  Gaurav S. Sukhatme,et al.  Obstacle detection and avoidance for an Autonomous Surface Vehicle using a profiling sonar , 2011, 2011 IEEE International Conference on Robotics and Automation.