Robotic milling posture adjustment under composite constraints: A weight-sequence identification and optimization strategy

Industrial robots are widely used for milling complex parts in restricted spaces owing to their multiple degrees of freedom and flexible postures. To plan posture trajectory for robot machining with high precision under multiple constraints, this study establishes composite constraint models with constraint boundary solutions. An improved gray relation analysis model is adopted to identify the weight-sequences among the composite constraints. The correlation degrees of the postures of the robot can be dynamically quantified between arbitrary cutter locations by applying weight sequence identification, which is conducive to fulfilling attractive orientations in artificial potential fields. In addition, this study proposes an initial posture determination strategy based on the optimization principle of minimizing the rotated energy in global postures. Consequently, an artificial potential planning model is applied to the implement posture adjustment of the robot end effector. During simulation and experimental validation, the proposed posture adjustment strategies with optimized initial postures and identified weight-sequences achieve a significant improvement in both the six-joint motion performance and machining precision quality in robotic milling.