ABSTRACT
For multi-dimensional high-order nonlinear systems with unstable path quality in parameter and extension terms, we developed a new fast search random tree strategy. First, we established a high-order Lipschitz vector field dynamic system to adapt to high-order systems of multi-degree-of-freedom robots, with the complex obstacle function being one of its key components. Secondly, we designed a classification gap filtering network layer (Classification LSTM) to screen training data models and ensure the global stability of data in path design. Additionally, the visual sensors deployed in the unit area effectively implement the path marking backtracking strategy and dead zone path simplification. Finally, three examples are provided to verify the effectiveness of this design method.
ABSTRACT
Implementing grasping tasks under color and multi scene promotion conditions is a key technology. This study proposes a recognition and grasping technique based on the crystal butterfly algorithm and adaptive imitation synthesis. Firstly, inspired by the movement trajectory of butterflies, a dynamic node tracking method called "Butterfly Trajectory" was designed. It can complete dynamic trajectory tracking under geometric constraints and achieve route memory. The second color dynamic recognition technology (CDR) has been proposed. It can quickly extract brightness, transparency, and color saturation obtained from multiple angles. Improve the feature extraction speed of Region CNN (R-CNN) instead of traditional methods (HOG). In addition, an adaptive imitation synthesis technique (AISP) is used to achieve the multi scenario promotion of grasping technology. Finally, simulation and physical testing were provided to verify the effectiveness of the design scheme in this article.
ABSTRACT
To address the parameter instability issues in hazardous materials handling during multi-machine loading and unloading operations, we propose a Full-Scale Smart Parameter Optimization Control (FSPOC) system specifically designed for multi-machine coordination. This system leverages a novel fish scale prediction algorithm tailored for cooperative multi-machine environments. Initially, the fish scale prediction algorithm, inspired by bionic fish scales, is developed to predict future system behavior by analyzing historical data. Building on this algorithm, we introduce a disturbance cancellation control theorem and design a parameter optimization controller to enhance stability in high-dimensional nonlinear spaces. The FSPOC method is then applied to a multi-machine cooperative system, enabling online distributed parameter optimization for complex systems with multiple degrees of freedom. The effectiveness of the proposed method was validated through simulations, where it was compared with two other optimization techniques: Genetic Algorithm-based PID (GAPID) and Chaotic Atomic Search Algorithm-based PID (CHASO). The simulation results confirm the superiority of the FSPOC method.