This paper presents a maritime path planning algorithm for autonomous surface vehicles (ASVs). A improved RRT algorithm for generating sample nodes is proposed. The HOCBF-RRT algorithm leverages high order control barrier functions (HOCBFs) to take into account the dynamics of the ASV and produce a path that prioritizes safety. In addition, to complete the path optimization, combined with control Lyapunov functions (CLFs), the expansion to the target point is accelerated and the planning time is reduced. In simulation experiments, it is observed that the HOCBF-RRT algorithm and HOCBF-CLF-RRT algorithm offer improvements over traditional RRT algorithms. Specifically, the HOCBF-RRT algorithm is found to enhance the smoothness of paths, while the HOCBF-CLF-RRT algorithm effectively optimizes the expansion of the random tree towards the target point.
In recent years, the conservation of water resources has attracted widespread attention. The development and application of water surface robots can achieve efficient cleaning of floating waste. However, limited to the small size of floating waste on the water surface, its detection remains a great challenge in the field of object detection. Existing object detection algorithms cannot perform well, such as YOLO (You Only Look Once), SSD (Single-Shot Detector), and Faster R-CNN. In the past two years, diffusion-based networks have shown powerful capabilities in object detection. In this paper, we decouple the position and size regressions of detection boxes, to propose a novel decoupled diffusion network for detecting the floating waste in images. To further promote the detection accuracy of floating waste, we design a new box renewal strategy to obtain desired boxes during the inference stage. To evaluate the performance of the proposed methods, we test the decoupled diffusion network on a public dataset and verify the superiority compared with other object detection methods.
Tanker ship is an important transportation equipment for petroleum. Due to the strong permeability and solubility of refined oil product, the high-quality inner wall special coating of tanker ships needs to be carried out regularly, which relies on manual operation with poor working conditions in confined space, high labor intensity, long cycle and fluctuating coating quality. This paper designs a lightweight rigid-flexible robot with a cable-driven parallel robot (CDPR) and a symmetric mechanical arm in series. The workspace analysis of the CDPR is completed based on kinematic and static modeling. The size of the symmetric mechanical arm and the terminal reachability are analyzed considering the interference. The results show that the proposed rigid-flexible cable robot can cover all the areas to be coated, which provides a new automatic solution for the inner wall special coating of tanker ships.
With the rapid development and wide application of industrial robots (IRs), it inevitably brings huge energy consumption (EC), which has become an important part of manufacturing EC. Therefore, the EC optimization of IRs has become the key to the green transformation and upgrading of the manufacturing industry, and it is of great significance for the realization of "carbon neutrality" and "carbon peaking". Therefore, this paper focuses on the energy evaluation and optimization of IR, and realizes the power and EC evaluation and motion parameter optimization of its trajectories based on data-driven method. First, the convolutional neural network (CNN) and Transformer models are combined to build the energy model of IR, and then accurate modeling of its power and EC is realized based on the deep learning algorithms. Based on the above energy model, the exhaustive method and genetic algorithm (GA) are used to find the optimal motion parameters and obtain the optimal trajectory with the least EC. Finally, the experimental results show that the proposed method can achieve more than 98% and 99% of the power and EC modeling of IR, respectively. The optimization of the trajectory motion parameters of IR is realized through exhaustive method and GA, and the maximum optimization potential on the test dataset can reach 52.77%, which verifies the effectiveness and accuracy of the proposed method.
In order to satisfy the demand for large planar workspace and lightweight in ship side painting, we designed a cable-driven parallel robot (CDPR) with parallel cables powered by a common drive unit considering easy deployment and structural simplicity. Different parallel cables and the effects of cable numbers are discussed. Based on the analysis, typical configurations of parallel cables are proposed, and the configuration of the CDPR for ship side painting is deduced, which has two translational degrees of freedom (DOF), and all three rotational DOFs are constrained. Kinematic and static modeling are executed, and dimensional optimization is carried out with the goal of maximizing the workspace. Workspace coverage rate of 88% is achieved with a 30m×20m vertical area.
