Currently, humanoid robots face high dynamics that are difficult to adapt to human movements and complex interference problems in physical education teaching scenarios. Moreover, it is difficult to accurately match the localization requirements of humanoid robots' anthropomorphic motion trajectories, making it difficult to meet the diverse needs of physical education teaching. Therefore, this article proposes an optimization scheme for the positioning accuracy of humanoid robots driven by visual perception collaboration, with a clear focus on biomimetic perception fusion positioning adaptation as the core solution. Construct new principles and methods for positioning optimization to address the aforementioned scientific issues. Firstly, the system reviews the mainstream positioning algorithms and their current application status in the field of humanoid robots, with a focus on analyzing the LANDMARC algorithm and its adaptability in humanoid robot positioning. Research has found that simulating the multi-source collaborative characteristics of biological perception systems can compensate for the shortcomings of a single perception mode. The fixed weighting factor strategy of the traditional LANDMARC algorithm is unable to adapt to the dynamic positioning requirements of humanoid robot motion, which is a key bottleneck leading to positioning errors. Based on this, an improved LANDMARC algorithm is proposed that integrates biomimetic perception principles with dynamic weighted optimization. On the one hand, drawing on the multi sensory collaborative perception mechanism of biology, a collaborative fusion model of visual perception and biomimetic tactile perception is constructed. On the other hand, a dynamic weighting factor adaptive adjustment strategy is designed, and an SVM classifier is introduced to achieve multi round iterative optimization, forming a new method for biomimetic collaborative perception dynamic weighting iterative optimization. Simulation experiments and real physical education teaching scenarios have shown that this scheme significantly reduces the positioning error of humanoid robots in anthropomorphic motion states and complex teaching scenarios without increasing computational complexity and application costs, effectively solving the core scientific problem of positioning under dynamic motion postures. This study provides key technical support and theoretical reference for the development and application of intelligent assistive devices in physical education teaching.
Li et al. (Thu,) studied this question.