As automotive manufacturing advances toward the industrial 5.0 era, traditional rigid automation production models are transitioning toward the embodied intelligence paradigm. Confronted with mass customization, diverse products, and small-batch production, the environment of automotive manufacturing exhibits high dynamism and unstructured characteristics. Different from traditional industrial intelligence based on static, hard-coded logic, robots enhance their cognitive abilities through closed-loop interaction with dynamic environments, inspired by bionic neural mechanisms, this shift enables robots to perform flexible and reliable operations in complex production scenarios. This paper analyzes the core role and key technologies of neural intelligence algorithms in reshaping perception, decision, and execution of industrial robot, while providing a systematic review of industrial robot evolution within the automotive industry, and provides a reliable path for future development.
Zhang et al. (Tue,) studied this question.