DYNAMIC TRAJECTORY PLANNING METHODS FOR PAINTING UNEVEN SURFACES WITH ROBOTIC MANIPULATORS

Abstract

This research focuses on developing and implementing dynamic trajectory planning methods for robotic manipulators tasked with painting uneven surfaces. The proposed system integrates surface mapping, real-time trajectory generation, and adaptive control algorithms to address challenges associated with uneven surface topographies. By leveraging advanced mathematical models and real-time sensor feedback, the system ensures uniform paint application, reduces wastage, and improves operational efficiency. Experimental results validate the effectiveness of the proposed methods, demonstrating significant improvements in trajectory accuracy, paint coverage uniformity, and computational efficiency compared to static methods. This work has broad implications for industrial automation, particularly in automotive, aerospace, and construction sectors.