GOALI: Computer-Aided Design (CAD) Guided Sensor Planning for Surface Inspection in Manufacturing

Michigan State University, East Lansing MI

Investigators

Abstract

The objective of this Grant Opportunities for Academic Liaison with Industry (GOALI) research is to develop a general Computer-Aided Design (CAD)-based method for constraint-satisfying robot motion planning in manufacturing. In particular, the research will focus on CAD-based inspection planning, namely, the automatic planning of the inspection sensor, or camera carried by a robotic device, based on given CAD model of the inspected parts. First, a CAD-based sensor planning approach will be developed that utilizes the part geometric information from the CAD model and the sensor model to generate constraint-satisfying sensor configurations. Second, to improve the efficiency and the kinematics performance of the inspection system, two optimization problems are formulated: one is to find the minimum set of viewpoints, while the other is the optimal kinematics to observe the viewpoints. By using a discretization scheme, the former is rendered as a set-partitioning problem, and the later as a weighted set-covering problem. New algorithms will be developed to solve these problems. Finally, the robot motion planning problem in the part inspection will be investigated. This is formulated as a Clustered Traveling Salesman Problem (CTSP) and a new hierarchical algorithm will be developed to obtain suboptimal solutions. In addition, the theoretical results will be implemented and experimentally tested. The potential contributions of this effort include a new methodology for optimal sensor planning for inspecting large areas of part surfaces. It can be used to easily extract the global geometric information on the part surfaces. The methodologies developed in this research could also benefit many other CAD-based planning problems such as spray painting and CNC part programming. This research will contribute to the development of technologies and human resources in the area of manufacturing automation.