Model predictive control (MPC) has been widely employed in autonomous driving control, offering advantages including rolling optimization. However, the performance of MPC can be significantly compromised due to the inaccurate system model of the vehicle. In response, a novel MPC model optimization algorithm based on BiLSTM has been proposed to address the issue of inaccurate system models. Firstly, MPC-based pathtracking data is collected under varying speeds, paths, and wheelbases. A BiLSTM network is then trained using this dataset and integrated with a MPC controller. This integration allows for the update of the wheelbase parameters of the kinematic model, thereby improving the accuracy of the vehicle prediction model. The proposed method is validated through simulation experiments. The results demonstrate that the path tracking accuracy of the MPC combined with a BiLSTM method is higher than that of MPC and linear quadratic regulator (LQR). Concurrently, this approach yields a considerable enhancement in path tracking precision, with an improvement of 37.53%.
Map fusion is a key challenge in multi-robot SLAM systems, with feature-based methods often leading to mismatches when handling maps of different resolutions. This paper introduces a density clustering-based map fusion method that reduces mismatches by clustering features before matching them. Experiments on public datasets demonstrate that the proposed method effectively merges grid maps of varying resolutions, offering improved efficiency and accuracy compared to traditional feature-based methods.
The path planning of Unmanned Underwater Vehicle (UUV) is a crucial aspect of their operation in underwater environments, Meta-heuristic algorithms are extensively utilized for addressing UUV path planning problems. To address the limitations of the traditional dung beetle optimization algorithm (DBO), including inadequate convergence speed and precision in two-dimensional UUV path planning, and its propensity for local optima, an improved dung beetle optimization algorithm (IDBO) is introduced which employing a suite of refinement strategies. Furthermore, the solution capability of the IDBO is validated through the CEC2017 test suite and two-dimensional raster maps that replicate actual underwater environments. The simulation results demonstrate the robust problem-solving capacity of the IDBO, applicable to both benchmark functions and real-world scenarios, affirming the efficacy of the enhancement strategies in practical applications.
Mobile robot machining system offers an effective solution for the integrated machining of large-scale components. However, the lower absolute positioning accuracy of industrial robots poses a significant challenge to achieving high-precision manufacturing of such components. Identifying robot kinematic parameters and compensating for motion errors represent effective approaches to enhancing the absolute positioning accuracy of robots. In this paper, we propose a two-step method for identifying robot kinematic parameters, which combines the LASSO algorithm with the IPSO algorithm, aiming to improve the absolute positioning accuracy of robotic machining systems for large-scale components. Initially, values of robot kinematic parameter deviations are obtained by applying the LASSO algorithm. These values then serve as initial particles for the IPSO algorithm, enabling more efficient and accurate kinematic parameter identification. Subsequently, the kinematic parameters of the robot machining system are efficiently calibrated. Experimental results demonstrate that the maximum absolute positioning error is improved by over 90% before calibration, demonstrating the validity of the proposed method in enhancing the absolute positioning accuracy of industrial robots.
By analyzing some disadvantages of existing spraying robot configuration, such as the lack of space and the destruction of the coating in the complex cavity surface, aiming at the special needs of the complex cavity surface spray, starting from the efficiency and spray quality of industrial production, a new type of six-degree-of-freedom robot is proposed. By combining the Modified Denavit-Hartenberg method and the anti -view angle algorithm, the robot’s kinematics model was established, and the kinematics solution and workspace were performed to verify the performance of the robot space. Taking the aircraft intake as an example, by simulating the machining trajectory of the aircraft intake, robotics simulation is performed to obtain the dynamic parameters of the axis joints, which provides a basis for subsequent motor selection and strength check. Through workspace solution and kinematics simulation verification, it proves that the agency not only can meet the high precision and high efficiency requirements of the complex cavity surface, but also shows the potential of robotic technology in improving production efficiency, ensuring the quality of spraying and achieving automated production. It provides new ideas and solutions for the development of spraying robot technology and its promotion in industrial applications.
