Complete automation of trajectory planning tools for material deposition/removal applications has become increasingly necessary to reduce the ldquoconcept-to-consumerrdquo timeline for rapid product introduction in industries such as the automotive industry. The work in this paper is specifically motivated by automotive spray painting. Prior developments in automated trajectory planning tools promise to reduce the time required to program the robots; however, these approaches are limited to surfaces that are either approximately planar or topologically simple (i.e., with no holes). To extend the applicability of these planning tools to nonplanar and topologically complex surfaces, currently the user has to manually segment a complex surface into simpler subsets, i.e., subsets that are approximately extruded surfaces and contain no holes. However, the complex nature of the relationships between surface segmentation and resulting output characteristics such as material deposition uniformity, process cycle time, and material waste makes the task of manually segmenting the surface difficult. In this paper, we develop a hierarchical procedure that automatically segments a surface based on surface geometry, surface topology, and path geometry to obtain topologically simple subsets that are approximately extruded surfaces. Finally, we compare the effectiveness of our segmentation with the state of the art on a few automotive surfaces in simulation.

Complete automation of trajectory planning tools for material deposition/removal applications has become increasingly necessary to reduce the “concept-to-consumer” timeline in product development. Segmentation of a complex automotive surfaces into topologically simple surfaces remains a barrier in the automation of trajectory generation. In this paper, we develop a novel hierarchical procedure to segment a complex automotive surface into geometrically as well as topologically simple components.

This research is focused on developing trajectory planning tools for the automotive painting industry. The geometric complexity of automotive surfaces and the complexity of the spray patterns produced by modern paint atomizers combine to make this a challenging and interesting problem. This paper documents our efforts to develop computationally tractable analytic deposition models for electrostatic rotating bell (ESRB) atomizers, which have recently become widely used in the automotive painting industry. The models presented in this paper account for both the effects of surface curvature as well as the deposition pattern of ESRB atomizers in a computationally tractable form, enabling the development of automated trajectory generation tools. We present experimental results used to develop and validate the models, and verify the interaction between the deposition pattern, the atomizer trajectory, and the surface curvature. Limitations of the deposition model with respect to predictions of paint deposition on highly curved surfaces are discussed. Note to Practitioners-The empirical paint deposition models developed herein, which are fit to experimental data, offer a significant improvement over models that are typically used in industrial robot simulations. The improved simulation results come without the computational cost and complexity of finite element methods. The models could be incorporated, as is, into existing industrial simulation tools, provided the users are cognizant of the model limitations with respect to highly curved surfaces. Although the models are based on readily available information, incorporating the models into existing robot simulation software would likely require support from the software vendor.

For automated spray painting robots, the choice of a start curve from which all subsequent paths will be determined, plays a critical role in ensuring uniform coverage of the target surface. In this paper, we ultimately propose a method to determine the start curve for two different procedures of coverage path construction. For the first procedure, we average the target surface normal to optimize the average error between desired and resultant deposition with the added goal of minimizing cycle time. For the second technique, we make recourse to the Gauss-Bonnet theorem to minimize the non-uniformity of paint deposition and cycle time for the entire coverage path.

As part of an ongoing collaborative effort with the Ford Motor Company, our research aims to develop practical and efficient trajectory planning tools for automotive painting. This paper documents our efforts to develop analytic deposition models for electrostatic rotating bell (ESRB) atomizers, which have recently become widely used in the automotive painting industry. Conventional deposition models, used in earlier automatic trajectory planning tools, fail to capture the complexity of deposition patterns generated by ESRB atomizers. The models presented here take into account both the surface curvature and the deposition pattern of ESRB atomizers, enabling planning tools to optimize trajectories to meet several measures of quality, such as coating uniformity. In addition to the development of our models, we present experimental results used to evaluate our models, and verify the interaction between the deposition pattern, trajectory, and surface curvature.

We address the task of covering a closed orientable surface embedded in /spl Rfr//sup 3/ without any prior information about the surface. For applications such as paint deposition, the effector (the paint atomizer) does not explicitly cover the target surface, but instead covers an offset surface-a surface that is a fixed distance away from the target surface. Just as Canny and others use critical points to look for changes in connectivity of the free space to ensure completeness of their roadmap algorithms, we use critical points to identify changes in the connectivity of the offset surface to ensure full surface coverage. The main contribution of this work is a method to construct unknown offset surfaces using a procedure, also developed in this paper, to detect critical points.

Sensor-based coverage uses sensor information to determine a path that passes a detector over all points in an unknown space. Our prior work in coverage prescribed a path for a circular robot of radius r to pass over all points in unknown spaces; in this case we set the detector range /spl delta/ to be equal to the robot's radius, r. Our prior work in Voronoi diagrams prescribed a path for a circular robot with infinite detector range (/spl delta/=/spl infin/) to pass its detector over all points in an unknown bounded space. This work combines these, results to consider "efficient" coverage with a finite-range detector with r</spl delta/