When utilizing the traditional path planning method for unmanned surface vehicles (USVs), ‘planning-failure’ is a common phenomenon caused by the inflection points of large curvatures in the planned path, which exceed the performances of USVs. This paper presents a second path planning method (SPP), which is an initial planning path optimization method based on the geometric relationship of the three-point path. First, to describe the motion performance of a USV in conjunction with the limited test data, a method of integral nonlinear least squares identification is proposed to rapidly obtain the motion constraint of the USV merely by employing a zig zag test. It is different from maneuverability identification, which is performed in combination with various tests. Second, the curvature of the planned path is limited according to the motion performance of the USV based on the traditional path planning, and SPP is proposed to make the maximum curvature radius of the optimized path smaller than the rotation curvature radius of the USV. Finally, based on the ‘Dolphin 1’ prototype USV, comparative simulation experiments were carried out. In the experiment, the path directly obtained by the initial path planning and the path optimized by the SPP method were considered as the tracking target path. The artificial potential field method was used as an example for the initial path planning. The experimental results demonstrate that the tracking accuracy of the USV significantly improved after the path optimization using the SPP method.
This paper presents an adaptive null-space-based behavioral (NSB) method to deal with the problems of saturation planning and lack of adaptability when the traditional NSB method is applied to the formation control of multiple unmanned surface vehicles (MUSVs). First, the NSB method is analyzed, and the matrix theory is introduced to propose a behavior priority theory determination method based on a vector graph. Second, consider the maneuverability of the unmanned surface vehicle (USV), variable coefficients with physical significances are introduced to redefine the behavioral motion model, making the speed limit solved in each working condition within the maneuvering range of the USV and effectively improving the formation ability of MUSVs. Third, a logical priority collision avoidance strategy between the MUSVs is proposed, aiming at the problem that when the USVs judge each other as obstacles, both of them adopt the obstacle avoidance behavior resulting in two vehicles' courses deviating from the direction of the target point. Finally, a simulation platform for the formation control of MUSVs was established by taking the Dolphin-I prototype USV as the experimental object, and the feasibility of the proposed method was verified by a simulation test. INDEX TERMS Multiple unmanned surface vehicles (MUSVs), adaptability formation control, null-spacebased behavior (NSB), behavior priority, cooperative collision avoidance.
Based on model-free adaptive control (MFAC) theory, this paper presents a variable output constraint MFAC (VOC-MFAC) algorithm to enhance the robustness of an unmanned surface vehicle's (USV's) heading subsystem. The contributions of this paper are as follows. First, a controller output constraint function is proposed to solve the system's control performance sensitivity to the redefined output gain when the redefined compact format model free adaptive control (RO-CFDL-MFAC) method is used to control an unmanned surface vehicle's heading. Second, the compact format dynamic linearization data models for a USV's angular velocity subsystem and heading subsystem are established, and the convergence of the closed-loop system under environmental disturbances is proven through rigorous theoretical analysis. Finally, the control algorithm proposed in this paper is simulated and tested in the field using the ''Dolphin IB'' unmanned surface vehicle platform developed by our research group, and the effectiveness of the VOC-MFAC algorithm is verified by the experimental results. INDEX TERMS Unmanned surface vehicle, heading control, model-free adaptive control, output constraint function, field trial.
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