Robots are widely used in manufacturing industries to perform various assembly operations given by a planner and such planner should be capable of performing necessary predicate testing to generate a feasible solution. Geometric feasibility (GF) is a pre-requisite and most essential assembly predicate that identify collision free paths to perform robotic assembly actions. However, the automated GF testing is limited to principal axes whereas it require human intervention to test in other possible directions. In this research, a novel automated method to generate Oblique-directional interference matrix (ODIM) is proposed to solve the problem. The developed method is tested over the real-time products and confirmed the applicability to identify the collision free paths. It is integrated with an existed assembly planner to test associativity for other predicates and observed the effectiveness by comparing with different methods. The results are indicating that the developed method is generating an optimal solution at minimum computational effort.

机器人在制造业中被广泛使用，以执行计划者提供的各种组装操作，并且这种计划者应该能够执行必要的谓词测试以生成可行的解决方案。几何可行性（GF）是识别无碰撞路径以执行机器人组装动作的先决条件和最重要的组装谓词。但是，自动GF测试仅限于主轴，而它需要人工干预才能在其他可能的方向上进行测试。在这项研究中，提出了一种新颖的自动方法来生成斜向干涉矩阵（ODIM）以解决该问题。所开发的方法在实时产品上进行了测试，并确认了识别无碰撞路径的适用性。它与现有的组装计划器集成在一起，以测试与其他谓词的关联性，并通过与不同方法进行比较来观察其有效性。结果表明，所开发的方法以最小的计算量即可生成最佳解决方案。