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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Cooperative operation of autonomous surface vehicles for maintaining formation in complex marine environment</title>
      <link>https://trid.trb.org/View/1685364</link>
      <description><![CDATA[A system for motion-planning, collision avoidance, guidance and control of an autonomous surface vehicles formation, navigating in complex marine environment is presented. The motion-planning unit, which is based in angle-guidance fast-marching square method, is specially developed for operation in dynamic and static environments. The collision avoidance unit is based in fuzzy-logic formulation, the guidance unit uses the vector-field guidance formulation and the control unit is composed by a PID heading controller and a speed controller. The leader-follower's configuration is used for cooperative operation. A set of numerical simulations are carried out for a team of three autonomous surface vehicles navigating in a complex maritime environment including static and dynamic obstacles and the results show good performance of the system.]]></description>
      <pubDate>Wed, 22 Apr 2020 12:25:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685364</guid>
    </item>
    <item>
      <title>Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment</title>
      <link>https://trid.trb.org/View/1686322</link>
      <description><![CDATA[Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstra's searching, coastline expanding and genetic algorithm (GA). The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also implemented to provide the baseline for comparison. The proposed algorithm generates the most energy efficient paths in ten USV missions, while keeping a configurable clearance from the coastlines. The practicability and scalability of this algorithm is also demonstrated by analysing the computational time in these ten missions.]]></description>
      <pubDate>Wed, 22 Apr 2020 12:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1686322</guid>
    </item>
    <item>
      <title>Robust dynamic positioning of autonomous surface vessels with tube-based model predictive control</title>
      <link>https://trid.trb.org/View/1685755</link>
      <description><![CDATA[This paper proposes two robust dynamic positioning (DP) approaches for autonomous surface vessels when full states are measurable and when only partial states are available with measurement errors. High fidelity nonlinear hydrodynamics are considered which are approximated as linear models in the local area of DP setpoint. The linearization errors are seen as bounded unmodeled dynamics and are accommodated in the tube-based model predictive control (MPC) together with environmental disturbances. The tube-based MPC controller contains all the possible uncertain trajectories in a tube that is based on a precomputed robust positive invariant set and a nominal trajectory solved online. The total controller consists of a feedforward part compensating the predicted environmental forces, a nominal part guiding the vessel towards and stabilizing it at the origin, and an affine feedback part bounding the uncertain vessel trajectory within the tube. Furthermore, an output feedback robust DP controller is proposed by utilizing a simple Luenberger observer to estimate the system states when full states are not available. The resultant estimation and measurement errors are also incorporated in the tube-based MPC. Simulation results show that the proposed full state and output feedback robust DP controllers can achieve the DP goals within system constraints.]]></description>
      <pubDate>Wed, 22 Apr 2020 12:24:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685755</guid>
    </item>
    <item>
      <title>The review unmanned surface vehicle path planning: Based on multi-modality constraint</title>
      <link>https://trid.trb.org/View/1687542</link>
      <description><![CDATA[The essence of the path planning problems is multi-modality constraint. However, most of the current literature has not mentioned this issue. This paper introduces the research progress of path planning based on the multi-modality constraint. The path planning of multi-modality constraint research can be classified into three stages in terms of its basic ingredients (such as shape, kinematics and dynamics et al.): Route Planning, Trajectory Planning and Motion Planning. It then reviews the research methods and classical algorithms, especially those applied to the Unmanned Surface Vehicle (USV) in every stage. Finally, the paper points out some existing problems in every stage and suggestions for future research.]]></description>
      <pubDate>Wed, 22 Apr 2020 12:24:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1687542</guid>
    </item>
    <item>
      <title>An improved integral line-of-sight guidance law for path following of unmanned surface vehicles</title>
      <link>https://trid.trb.org/View/1697947</link>
      <description><![CDATA[This paper proposes a path following control system for Unmanned Surface Vehicles (USVs) based on an improved integral Line-Of-Sight (LOS) guidance law. Unlike the conventional LOS guidance law, the look-ahead distance is designed as a function of the USV’s cruising speed and the cross tracking error to adapt to the different cruising speeds of USVs. Meanwhile, a reduced-order state observer is developed for online estimation of the time-varying sideslip angle caused by external disturbances such as wind, wave and current. Then, a heading controller is further designed using the dynamic surface control technique to track the desired heading angle. The guidance system and the reduced-order state observer subsystem are proved to be uniformly asymptotically stable and input-to-state stable respectively. The simulation results show that the path following control system designed in this paper can track the desired curved and straight line paths quickly and smoothly at different cruising speeds.]]></description>
      <pubDate>Wed, 22 Apr 2020 12:24:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1697947</guid>
    </item>
    <item>
      <title>Motion planning for an unmanned surface vehicle based on topological position maps</title>
      <link>https://trid.trb.org/View/1686330</link>
      <description><![CDATA[This paper investigates the motion-planning problem for an unmanned surface vehicle (USV), in which the goal is to find the shortest search time, the shortest path in navigational waters, all subject to collision avoidance and USV dynamics constraints. A new motion-planning method is proposed, based on topological position relationships (TPR), to achieve this solution. Firstly, the TPR of the obstacles and the USV are constructed, based on the spatial distribution of the obstacles. This gives an overall topological navigation map, which is different from the usual grid-based map. Secondly, a numerical model of unit decomposition is built to constrain the dynamics of the USV, so that the motion of the USV better fits the exact situation. Motion planning in this study is achieved by combining the topological navigation map and a numerical model of the USV. Finally, Numerical simulations and field tests verify the effectiveness of the authors' formulated model and proposed algorithm.]]></description>
      <pubDate>Tue, 31 Mar 2020 16:47:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1686330</guid>
    </item>
    <item>
      <title>Deep reinforcement learning-based controller for path following of an unmanned surface vehicle</title>
      <link>https://trid.trb.org/View/1685709</link>
      <description><![CDATA[In this paper, a deep reinforcement learning (DRL)-based controller for path following of an unmanned surface vehicle (USV) is proposed. The proposed controller can self-develop a vehicle’s path following capability by interacting with the nearby environment. A deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic-based reinforcement learning algorithm, was adapted to capture the USV’s experience during the path-following trials. A Markov decision process model, which includes the state, action, and reward formulation, specially designed for the USV path-following problem is suggested. The control policy was trained with repeated trials of path-following simulation. The proposed method’s path-following and self-learning capabilities were validated through USV simulation and a free-running test of the full-scale USV.]]></description>
      <pubDate>Fri, 27 Mar 2020 15:02:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685709</guid>
    </item>
    <item>
      <title>Distributed containment maneuvering of uncertain under-actuated unmanned surface vehicles guided by multiple virtual leaders with a formation</title>
      <link>https://trid.trb.org/View/1685879</link>
      <description><![CDATA[This paper is concerned with the distributed containment maneuvering for a fleet of under-actuated unmanned surface vehicles (USVs) guided by multiple virtual leaders moving along multiple parameterized paths with a formation. Each USV is subject to model uncertainties and ocean disturbances caused by wind, waves and ocean currents. Distributed containment maneuvering controllers are constructed for under-actuated USVs based on an auxiliary variable approach, an extended state observer, a linear tracking differentiator, and a path maneuvering design. The proposed controllers drive the vehicle fleet to converge to a convex combination of multiple virtual leaders regardless of the model uncertainties and ocean disturbances. The input-to-state stability of the closed-loop system is analyzed via Lyapunov theory and the containment maneuvering errors converge to a small neighborhood of the origin. Simulation results show that the feasibility and efficacy of the proposed distributed containment maneuvering controllers for the under-actuated USVs.]]></description>
      <pubDate>Fri, 20 Mar 2020 16:26:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685879</guid>
    </item>
    <item>
      <title>ELOS-based path following control for underactuated surface vehicles with actuator dynamics</title>
      <link>https://trid.trb.org/View/1685874</link>
      <description><![CDATA[This paper presents an improved extended state observer-based line-of-sight (ELOS) guidance law for robust path following of underactuated surface vehicles without linear velocity measurement. First, two extended state observers are resorted to separately estimate the surge and sway linear velocities, such that the unmeasured side-slip angle can be indirectly calculated and accurately compensated in the line-of-sight guidance law. Especially, in this improved ELOS design no assumptions on constant or small side-slip angle are made. Second, a more high-order sliding mode surface is defined for the input of third-order heading tracking subsystem due to actuator dynamics. Subsequently, an adaptive fuzzy sliding mode control (AFSMC)-based tracking law is designed to robustly track the heading guidance angle without a prior on the vehicle’s complex hydrodynamics parameters. Finally, comparative simulation results are provided to show the performance of the designed path following controller, composed of ELOS and AFSMC for underactuated surface vehicles with actuator dynamics.]]></description>
      <pubDate>Fri, 20 Mar 2020 16:26:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685874</guid>
    </item>
    <item>
      <title>Constrained coordinated path-following control for underactuated surface vessels with the disturbance rejection mechanism</title>
      <link>https://trid.trb.org/View/1686232</link>
      <description><![CDATA[This note proposed a practical coordinated path-following algorithm for a group of underactuated surface vessels to achieve and maintain the desired formation pattern. The developed scheme consists of two envelopes. One is to stabilize the individual vessel to the virtual vessel moving along with the predefined path. The other is to synchronize the surge speed of virtual vehicles to guarantee the desired geometric formation. Within the design framework, a nonlinear disturbance observer (DOB) is constructed to estimate the lumped disturbance which includes the model uncertainty and the unknown disturbance. By virtue of the auxiliary system and the novel tan-type barrier Lyapunov function (BLF), the observer based control law is designed to eliminate the effect of the input and velocity constraints. Furthermore, on the basis of the undirected graph, the speed agreement is presented to ensure the motion synchronization of networked underactuated vessels. Compared with the existing results, the proposed algorithm is more applicable to the marine practice, e.g. the collaborative search and rescue scene. Through the Lyapunov analysis, it is proved that all the variables of the closed-loop system are with the uniform ultimate bounded stability. Finally, the scaling formation example and comparison simulation are conducted to illustrate the effective of the proposed algorithm.]]></description>
      <pubDate>Fri, 20 Mar 2020 16:26:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1686232</guid>
    </item>
    <item>
      <title>Collision avoidance for an unmanned surface vehicle using deep reinforcement learning</title>
      <link>https://trid.trb.org/View/1685537</link>
      <description><![CDATA[In this paper, a deep reinforcement learning (DRL)-based collision avoidance method is proposed for an unmanned surface vehicle (USV). This approach is applicable to the decision-making stage of collision avoidance, which determines whether the avoidance is necessary, and if so, determines the direction of the avoidance maneuver. To utilize the visual recognition capability of deep neural networks as a tool for analyzing the complex and ambiguous situations that are typically encountered, a grid map representation of the ship encounter situation was suggested. For the composition of the DRL network, the authors proposed a neural network architecture and semi-Markov decision process model that was specially designed for the USV collision avoidance problem. The proposed DRL network was trained through repeated simulations of collision avoidance. After the training process, the DRL network was implemented in collision avoidance experiments and simulations to evaluate its situation recognition and collision avoidance capability.]]></description>
      <pubDate>Fri, 20 Mar 2020 16:26:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685537</guid>
    </item>
    <item>
      <title>Adaptive modeling of maritime autonomous surface ships with uncertainty using a weighted LS-SVR robust to outliers</title>
      <link>https://trid.trb.org/View/1685202</link>
      <description><![CDATA[Maritime Autonomous Surface Ships (MASS) have become increasingly interesting for the commercial maritime sectors as an alternative to conventional ships. For the purpose of development of MASS operations such as motion control (e.g. collision avoidance, trajectory tracking), the ship dynamic model is of significant importance for technique tests, i.e. verification and validation. The ship dynamic model should be suitable for cases which means the sufficient balance between the complexity and the accuracy of the model. This contribution is aiming to develop a robust ship dynamics modeling approach by making efforts on determining a common ship dynamic model and designing a robust identification method. A response model, i.e. the first-order nonlinear Nomoto model widely-used in ship autopilot design is selected. A robust optimal identification method named optimal DW-LSSVR is proposed by taking advantages of least square support vector regression algorithm (LS-SVR), robust 3α principle (D), adaptive weight technique (W), and artificial bee colony algorithm (ABC for optimization). The robust 3α principle has the function of outlier detection. Adaptive weight technique can adaptively weight support vectors to improve the sparsity of LS-SVR. Furthermore, ABC is served as the structural parameters optimizer for LS-SVR. The ships studied in this work are considered as more realistic objects with uncertain parameters induced by unmodeled terms, variation of the loading condition as well as environmental disturbances, and measurement noises. In identifying the widely-used response model for ships, the optimal DW-LSSVR method is verified and validated on both experimental measurements and simulated data. In simulation tests, the effectiveness of the proposed method in identifying the response model has been demonstrated with the use of simulated data including ones with uncertainties which are generated by activating a response model with predefined parameter values by means of step inputs in Monte Carlo simulations. Besides the simulation tests, the experimental investigation on a real Unmanned Surface Vessel (USV) using experimental zigzag maneuvers also indicates the consistency of the proposed approach.]]></description>
      <pubDate>Fri, 20 Mar 2020 16:26:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1685202</guid>
    </item>
    <item>
      <title>Utilization of Unmanned System Technology in Transportation Engineering: A Case Study</title>
      <link>https://trid.trb.org/View/1647979</link>
      <description><![CDATA[Unmanned system technology is an emerging field in the world of civil engineering. While there are many possible applications for using unmanned systems for data collection there are currently many restrictions. This paper provides insight to a cost effective utilization of unmanned systems to complete data collection for transportation projects and discusses in detail the laws currently limiting unmanned system use in the United States.]]></description>
      <pubDate>Thu, 19 Mar 2020 10:22:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1647979</guid>
    </item>
    <item>
      <title>High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-Based adaptive gains and time delay estimation</title>
      <link>https://trid.trb.org/View/1676862</link>
      <description><![CDATA[This research addresses two kinds of problems related to optimal trajectory tracking of a Maritime Autonomous Surface Ship (MASS): those caused by the time-varying external disturbances including winds, waves and ocean currents as well as those resulting from inherent dynamical uncertainties. As the paper shows, an accurate and robust optimal controller can successfully deal with both issues. An improved Optimal Adaptive Super-Twisting Sliding Mode Control (OAST-SMC) algorithm is proposed here as a robust optimal adaptive strategy. In this strategy, in order to improve performance of the standard super-twisting approach, the authors apply an Approximate Dynamic Programming (ADP)-based optimal tuning of gains and an underlying concept based on Time Delay Estimation (TDE). An ADP algorithm is implemented using an actor-critic neural network to deal with the curse of dimensionality in Hamilton–Jacobi–Bellman (HJB) equation. The critical role of TDE part in this algorithm is estimating the impact of disturbances and uncertainties on the MASS model. The results have shown that OAST-TDE significantly outperforms the ST-TDE and AST-TDE algorithm in terms of the optimal control efforts. Also, compared with a Nonlinear Model Predictive Control (NMPC), proposed controller meets the optimal control efforts and accurate tracking concurrently.]]></description>
      <pubDate>Wed, 29 Jan 2020 14:30:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1676862</guid>
    </item>
    <item>
      <title>Distributed Model Predictive Control for cooperative floating object transport with multi-vessel systems</title>
      <link>https://trid.trb.org/View/1676861</link>
      <description><![CDATA[Compared to an individual Autonomous Surface Vessel (ASV), greater efficiency and operational capability can be realized by a team of cooperative ASVs for certain operations, such as search and rescue, hydrographic survey and navigation assistance. This paper focuses on cooperative floating object transport, i.e., a group of ASVs coordinate their actions to transport floating objects. The authors propose a multi-layer distributed control structure for the object transport system. The object transport problem is formulated as the combination of several sub-problems: trajectory tracking of the object, control allocation, and formation tracking of the ASVs. The sub-problems are integrated by a nonlinear towline model that describe the transformation of forces considering the mass and elasticity of the towline. A controller based on Model Predictive Control (MPC) is designed to control the motion of each ASV. A negotiation framework based on the Alternating Direction of Multipliers Method (ADMM) is then proposed to achieve consensus among the ASVs. Numerical simulations of utilizing the proposed cooperative system to move a large vessel sailing inbound the Port of Rotterdam are carried out to show the effectiveness of the authors' method. Besides transporting barges and off-shore platforms, the proposed cooperative object transport system could also be a solution to coordinate non-autonomous vessels and ASVs in future autonomous ports where both human-operated and autonomous vessels exist.]]></description>
      <pubDate>Wed, 29 Jan 2020 14:30:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1676861</guid>
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