This paper proposes a multi-objective cooperative control method for a ship-towing system in congested water traffic environments. The control objectives are to coordinate multiple autonomous tugboats for transporting a ship to: (i) follow the waypoints, (ii) adjust the heading, (iii) track the speed profile, and (iv) resolve collisions. The problem is tackled by the design of multiple control agents distributed in two control layers. Based on the strategy of model predictive control (MPC), the supervisory controller in the higher layer calculates the towing angles and forces of the ship, the tug controller in the lower layer computes the tug thruster forces and moment. The consensus between the lower and higher layer control is achieved by using the altering direction method of multipliers (ADMM) that makes the predicted tug position and heading approach to the desired tug trajectory. Simulation experiments indicate that the proposed method coordinates multiple autonomous tugboats to transport a ship smoothly and effectively and succeeds in multiple control objectives, in the meantime, the avoidance operation complies with COLREGS rules.

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Among the promising application of autonomous surface vessels (ASVs) is the utilization of multiple autonomous tugs for manipulating a floating object such as an oil platform, a broken ship, or a ship in port areas. Considering the real conditions and operations of maritime practice, this paper proposes a multi-agent control algorithm to manipulate a ship to a desired position with a desired heading and velocity under the environmental disturbances. The control architecture consists of a supervisory controller in the higher layer and tug controllers in the lower layer. The supervisory controller allocates the towing forces and angles between the tugs and the ship by minimizing the error in the position and velocity of the ship. The weight coefficients in the cost function are designed to be adaptive to guarantee that the towing system functions well under environmental disturbances, and to enhance the efficiency of the towing system. The tug controller provides the forces to tow the ship and tracks the reference trajectory that is computed online based on the towing angles calculated by the supervisory controller. Simulation results show that the proposed algorithm can make the two autonomous tugs cooperatively tow a ship to a desired position with a desired heading and velocity under the (even harsh) environmental disturbances.

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Autonomous surface vehicles (ASVs) are seeing a significant development over the last decade. In recent years, their commercial applications are attracting the attention of many companies. One of the promising subjects is to develop autonomous tugs for ship berthing. This paper focuses on the cooperative control of autonomous tugs for ship towing in a berthing scenario. We propose a multi-layer optimal control strategy for a two-tug towing system to guarantee a ship reaching a desired position with a desired heading and velocity. In the higher layer supervisory control, an optimal control method is used to allocate towing forces and determine towing angles. With the help of these results and geometry relationships, the reference trajectories of the two autonomous tugs can be calculated online. Based on the reference trajectories, the trajectory tracking, which is in the lower layer, is addressed. Simulation results indicate that two autonomous tugs can cooperatively tow the unpowered ship to a desired position with a desired heading and velocity.

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Regarding aiming at multiple mobile objects in a complex water traffic- navigation environment, the chapter puts forward a route-planning method based on the complexity map. First, a Complexity Map was established according to the theory of complexity measurement. Then, combined with the A* algorithm, the actual cost was modified by making use of the distribution of the complexity values. Meanwhile, to reduce the distance of the whole voyage and avoid the local minimum in the process of path-finding, the Euclidean distance from the current point to the target was set to estimate heuristic cost. In addition, by using the standardization method, the value of complexity and distance became dimensionless. Finally, considering the ship dimensions, a channel-boundary constraint was added. The experimental results show that in the case of satisfying the ship dimensions, the best path is close to the shortest route and avoids all the high complex areas.@en