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arXiv · 2503.07340

Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning

Yiting Mao·Dajun Tao·Shengyuan Zhang·Tian Qi·Keqin Li
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Abstract

In the field of robot target recognition research driven by artificial intelligence (AI), factors such as the disordered distribution of targets, the complexity of the environment, the massive scale of data, and noise interference have significantly restricted the improvement of target recognition accuracy. Against the backdrop of the continuous iteration and upgrading of current AI technologies, to meet the demand for accurate recognition of disordered targets by intelligent robots in complex and changeable scenarios, this study innovatively proposes an AI - based intelligent robot disordered target recognition method using reinforcement learning. This method processes the collected target images with the bilateral filtering algorithm, decomposing them into low - illumination images and reflection images. Subsequently, it adopts differentiated AI strategies, compressing the illumination images and enhancing the reflection images respectively, and then fuses the two parts of images to generate a new image. On this basis, this study deeply integrates deep learning, a core AI technology, with the reinforcement learning algorithm. The enhanced target images are input into a deep reinforcement learning model for training, ultimately enabling the AI - based intelligent robot to efficiently recognize disordered targets. Experimental results show that the proposed method can not only significantly improve the quality of target images but also enable the AI - based intelligent robot to complete the recognition task of disordered targets with higher efficiency and accuracy, demonstrating extremely high application value and broad development prospects in the field of AI robots.

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Yiting Mao, Dajun Tao, Shengyuan Zhang, Tian Qi, Keqin Li. 2025-03-10. Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning. https://arxiv.org/abs/2503.07340

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