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 decimeterlevel precision in dynamic GPSattenuated urban waterways. Area and shape filters are proposed to crop water reflections and street obstacles from a pointcloud. Euclidean clustering and multiobject 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 fourcontrolinput 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.

Learningbased Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats This paper presents a Learningbased Nonlinear Model Predictive Control (LBNMPC) algorithm for reconfigurable autonomous vessels to facilitate highaccurate path tracking. Each vessel is designed to latch to a predefined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a realtime parameter estimator and a feedback control strategy, which is capable of ensuring highaccurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated online and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve pathtracking performance. Through experiments on different configurations of connectedvessels, we demonstrate stability of our proposed approach and its effectiveness in highaccuracy in path tracking.