Large-scale metal additive manufacturing has become increasingly popular in aerospace and petroleum industries alike for sustainable fabrication of thin-shelled structural components. For example, wire-arc additive manufacturing (WAAM) offers high-deposition rates on large printing areas by robot-assisted welding of thick layers of material. However, WAAM technologies suffer from significant layer displacement and resultant part-scale distortion due to unstable high temperature deposition processes and lack of economically viable support structures. Therefore, geometric accuracy qualification at layer level is critical to process optimization and control. However, layer quality assessment relies on layer identification from large point clouds. Manual layer segmentation is experience-dependent and time-consuming due to high surface roughness, excessive layer remelting, and severe out-of-plane layer displacement. To enable automated layer segmentation for quality assessment, we computationally model a human operator’s intuition utilized in the process of finding layer boundaries, that is, locating nearby regions with a large number of possible boundary points, and finetuning boundaries by learning boundaries functions. In our proposed approach, geometrical features of the boundaries between printed layers are exploited to identify candidate boundary points. Cooperative multi-learning-agents efficiently process the large point clouds to locate sets of nearby regions with a high density of boundary points. Learning agents then sample promising boundary points. Gaussian process regression is employed to fine-tune layer boundaries through learning mean boundary functions and their uncertainties from the sampled points. Simulation studies demonstrate the accuracy and robustness of the procedure under severe surface roughness conditions. WAAM experimental studies illustrate the applicability of the methodology in practice.

Serial-link manipulators are required to execute trajectories that enable a robot end-effector or a tool to track a Cartesian path. Practical applications may not require constraining all six degrees of freedom (position and orientation) of the tool resulting in semi-constrained paths. Semi-constrained paths allow improved success rates and better quality trajectories as the robot has more freedom to meet the kinematic and dynamic constraints. Additionally, robotic applications will need to use multiple tool center points (TCPs) on the tool to generate feasible paths for the robot. We present an iterative graph construction method to find trajectories for semi-constrained Cartesian paths that also use multiple TCPs. Our graph-based method finds multiple inverse kinematic solutions for possible Cartesian poses that the robot can take and connects them to build a graph. The algorithm uses cues from the Cartesian space to prioritize poses that produce a better quality solution. A biasing scheme is also developed to selectively sample the starting Cartesian poses from available choices. Our method finds near-optimal solutions with significantly fewer nodes and edges in the graph. The algorithm’s performance results on complex industrial test cases are provided. Note to Practitioners—Industrial applications permit the relaxation of one or more degrees of freedom of the tool while following the Cartesian paths. The relaxation of constraints is introduced by defining tolerances between the tool and the workpiece. Multiple TCPs have to be employed for many tasks to use different surfaces of the tool. In this paper, we present a planning algorithm for semi-constrained Cartesian paths that can incorporate the use of multiple TCPs. The user can define discrete TCPs over the tool contact points or surfaces. The tolerances can be easily defined as angular limits on tool orientation along the Cartesian path. Our planning algorithm can work with others via point constraints in Cartesian space or joint space of the robot. Practitioners from the industry can use the method presented in this paper to develop automated robotic cells that do cutting, sanding, polishing, welding, painting, composite layup, additive manufacturing, and several other common applications.

Wire arc additive manufacturing is a metal additive manufacturing process in which the material is deposited using arc welding technology. It is gaining popularity due to high material deposition rates and faster build time. It is en-abled using robotic manipulators and can build relatively large-scale parts faster when compared with other metal additive manufacturing processes. However, the size of the large-scale parts is limited by the size of the industrial manipulator being used for the process. This limitation is overcome by using a fixed configuration multi-robot cell in which manipulators work cooperatively to build large-scale parts quickly. A fixed multi-robot cell with closely spaced industrial manipulators has high flexibility, but it restricts the part size that can be built. If the manipulators are spread out, the cell loses its flexibility but can build relatively larger parts. This issue can be avoided by using larger size manipulators, which are expensive, or by moving the modest size manipulators based on the part geometries. This paper presents a novel algorithm to generate multi-robot placements for different part geometries to be built using wire arc additive manufacturing. Furthermore, the algorithm hierarchically optimizes the build time and the inverse kinematics consistency in robot paths to improve the process efficiency and part quality. We compare the results with fixed multi-robot cells and provide insights to users to make an informed decision on whether to use a fixed or a flexible multi-robot cell for wire arc additive manufacturing.

Robotic manipulators are increasingly being used to perform additive manufacturing. The accuracy of a built part is dependent on the trajectory execution error of the manipulator. For articulated manipulators, the trajectory execution error and achievable build accuracy vary considerably over the workspace. Therefore, the build accuracy depends on where the part is placed in the manipulator workspace. If the part is small compared to the manipulator workspace, its placement can be optimized to improve the accuracy. This paper provides experimental evidence that the placement of the parts changes its build accuracy. We model the trajectory execution error of the manipulator for additive manufacturing. We validate these errors by comparing the predicted errors with the experimental errors. Finally, we present an algorithm to optimize the part placement for improving built part accuracy during robot-based additive manufacturing.

Multi-arm mobile manipulators can be represented as a combination of multiple robotic agents from the perspective of task-assignment and motion planning. Depending upon the task, agents might collaborate or work independently. Integrating motion planning with task-agent assignment is a computationally slow process as infeasible assignments can only be detected through expensive motion planning queries. We present three speed-up techniques for addressing this problem-(1) spatial constraint checking using conservative surrogates for motion planners, (2) instantiating symbolic conditions for pruning infeasible assignments, and (3) efficiently caching and reusing previously generated motion plans. We show that the developed method is useful for real-world operations that require complex interaction and coordination among high-DOF robotic agents.

High-performance composites are increasingly being used in the industry. Sheet layup is a process of manufacturing composite components using deformable sheets. We have developed a robotic cell to automate the layup process and overcome the limitations of the manual layup. Generating offline trajectories for robots and executing them without online refinement can introduce defects in the process due to uncertainties in the model of the sheet and environmental factors. Our system computes layup and grasping trajectories for the robots and refines them during the layup process based on the sensor data. We use an approach that augments physical experiments with simulations to train a Gaussian process regression model offline. The use of GPR enables us to quickly refine grasp plans and perform a defect-free layup without slowing down the layup process. We present experimental results on two components.

Extrusion-based additive manufacturing (AM), also known as fused deposition modeling (FDM) extrudes filaments through a heated nozzle and builds a part layer-by-layer. Using a smaller diameter nozzle can achieve better surface finish. However, there is a trade-off between surface finish and build times as using a small diameter nozzle leads to smaller layer thickness and long build times. Traditional FDM printers create a part with planar layers, and this restricts control over fiber orientations. This paper presents a robotic cell for multi-resolution AM. The cell consists of two 6 degrees of freedom (DOF) robot manipulators capable of printing non-planar and/or planar layers. We describe algorithms for decomposing parts into multi-resolution layers and generating collision-free trajectories for the robot manipulators. We validate our approach by printing five parts with multi-resolution.

Currently, automatically generated trajectories cannot be directly used on tasks that require high execution accuracies due to errors accused by inaccuracies in the robot model, actuator errors, and controller limitations. These trajectories often need manual refinement. This is not economically viable on low production volume applications. Unfortunately, execution errors are dependent on the nature of the trajectory and end-effector loads, and therefore devising a general purpose automated compensation scheme for reducing trajectory errors is not possible. This paper presents a method for analyzing the given trajectory, executing an exploratory physical run for a small portion of the given trajectory, and learning a compensation scheme based on the measured data. The learned compensation scheme is context-dependent and can be used to reduce the execution error. We have demonstrated the feasibility of this approach by conducting physical experiments.