Robotic Additive Manufacturing
Background:
Additive Manufacturing (AM) is expected to revolutionize manufacturing. The current generation of AM technology has overcome many limitations of traditional manufacturing. However, the current AM technology still needs many improvements.
Our laboratory’s focus:
The goal of our research is to realize the next generation AM technologies through use of robotics. Articulated robot arms can significantly expand capabilities of AM processes by allowing material deposition using complex non-planar layers. Many composite parts have thin three-dimensional shell structures. Achieving the right fiber orientation is critical to the functioning of these parts. Printing them using conventional planar-layer AM processes leads to fibers being oriented in the plane of the layer. The capability to deposit the material along non-planar conformal layers can produce parts with the desired material properties.
We have demonstrated that using non-planar layers can significantly improve structural performance. We use artificial intelligence-based methods to generate robot trajectories to minimizing the build time. Furthermore, we have been working on conformal multi-resolution 3D printing. Robots can be used to perform multi-resolution printing that finds the best trade-off between build speed and surface finish. We are developing the three-nozzle extrusion system and implemented path planning algorithms using non-planar layers with different resolutions. Finally, AM is not expected to produce high-quality electronics (e.g., processor, sensors) in the near foreseeable future.
Our research also focuses on the use of robots for the insertion of externally fabricated components such as sensors, actuators, and energy harvesting components during the AM process.
Machining Tending with Semi-Autonomous Robots
Background:
Mobile manipulators can be used for machine tending and material handling tasks in small volume manufacturing applications. These applications usually have semi-structured work environments. Fixed automation with conveyor belts, AGVs, or large industrial manipulators, currently used in large volume manufacturing, is not feasible in these applications because of the expenses and inflexibility. Mobile manipulators offer the flexibility to perform a variety of tasks using a limited number of robots around the facility. However, using a fully autonomous mobile manipulator for such applications can be risky, as an inaccurate model of the workspace may result in damage to expensive equipment. On the other hand, the use of a fully teleoperated mobile manipulator may require a significant amount of operator time.
Our laboratory’s focus:
We believe that a hybrid operation mode will be beneficial, in which teleoperation and autonomous motions are combined. For this, we have developed a semi-autonomous, mobile manipulation system, called ADAMMS (Agile Dexterous Autonomous Mobile Manipulation System). It is equipped with cameras and other sensors, and is human-supervised to be used in machine tending operations. The human operator can remain at a remote location, use a user interface to provide a set of high-level instructions for each individual task. The robot autonomously plans motions for executing these tasks and shows a simulation of plan execution to the operator, who can then choose to execute or discard the motion plans. The remote operator can also monitor the motions being executed and stop if a collision is likely to occur. Furthermore, if the autonomous operation is infeasible, the operator can take complete control of the robot and still complete the task in teleoperation mode. As automation in manufacturing applications continues to increase, human operator support of the equipment is expected to become increasingly remote due to spatial, logistical, and safety constraints. We are developing a system to enable a single operator to support multiple platforms distributed throughout a facility through a semi-autonomous operation.
Robotic Composite Layup
Background:
Hand layup is a commonly used process for making composite structures from several plies of carbon-fiber prepreg. The process involves multiple human operators manipulating and conforming layers of prepreg to a mold. The manual layup process is ergonomically challenging, tedious, and limits throughput. Moreover, different operators may perform the process differently and hence introduce inconsistency. We are developing a smart robotic cell to automate the prepreg sheet layup process. The cell uses multiple robots to manipulate and drape sheets over a tool. A human expert provides a sequence to conform the ply and types of end-effectors to be used as input to the system. The system automatically generates trajectories for the robots that can achieve the specified layup. Planning algorithms are capable of:
- Generating plans to grasp and manipulate the ply
- Generating feasible robot trajectories.
Our laboratory’s focus:
Our system can generate plans in a computationally efficient manner for complex parts. We are also developing an approach for selecting and placing robots in the cell and description of tools and end effectors needed for utilizing the cell. We have demonstrated the automated layup by conducting physical experiments on an industry-inspired mold using the generated plans. Our system can perform sheet layup at speed comparable to human operators.
Human Robot Collaboration on Assembly Operations
Background:
Factories of the future are expected to produce increasingly complex products, demonstrate flexibility by rapidly accommodating changes in products or volumes, and remain cost competitive by controlling capital and operational costs. Humans and robots share complementary strengths in performing assembly tasks. Humans offer the capabilities of versatility, dexterity, performing in-process inspection, handling contingencies, and recovering from errors. However, they have limitations in terms of consistency, payload size/weight, and operational speed. In contrast, robots can perform tasks at high speeds, while maintaining precision and repeatability, operate for long periods of times, and can handle high payloads. However, currently robots require long programming times and have limited dexterity.
Our laboratory’s focus:
We are developing a framework to build assembly cells that support safe and efficient human-robot collaboration during assembly operations. Our approach allows asynchronous collaborations between human and robot. The human retrieves parts and places them in the robot’s workspace, while the robot picks up the placed parts and assembles them into the product. We are developing technologies for automated plan generation, system state monitoring, and contingency handling.