A Review on AI-Enhanced Adaptive Grasping for Industrial Robots

Abstract

The evolution of intelligent automation in industrial domains has catalysed the necessity for advanced robotic systems capable of exhibiting human-like dexterity and situational awareness. This study delineates an AI-enhanced adaptive grasping framework meticulously engineered for industrial robots operating in complex and unstructured environments. The proposed system synergises deep convolutional neural networks for precise object detection and pose estimation with reinforcement learning algorithms that iteratively refine grasping policies through experiential feedback. Furthermore, the integration of multimodal sensory data—encompassing visual, tactile, and proprioceptive inputs—enables the robot to dynamically modulate its grasping strategy in accordance with fluctuating object properties, such as geometry, texture, and weight distribution. Empirical evaluations conducted in a simulated industrial milieu reveal a substantial enhancement in grasp reliability, manipulation efficiency, and task generalisability when juxtaposed with conventional methodologies. The research underscores the transformative potential of artificial intelligence in augmenting the autonomy, flexibility, and operational robustness of robotic manipulators, thereby paving the way for more resilient and adaptive manufacturing ecosystems.