Systems designed for scale

Robot Base Placement Planning

The workspace of a serial manipulator is limited. Additionally, complexities exist due to the joint limit and self-collision-avoidance constraints. The robot workspace includes singularities, and it is generally preferred for a robot to carry out tasks away from singularities. Different locations in the robot workspace impose different constraints on achievable forces and velocities. Variations in achievable forces and velocities can be quite large. Moreover, there might not exist a continuous path between two points in the robot workspace that are far apart. Workspace characteristics can be quite complex for redundant manipulators, and unfortunately, there is no simple model to capture these. Many complex manufacturing tasks such as welding, robotic finishing, composite layup, painting, 3D printing, etc. use manipulators to follow continuous paths under constraints on the workpiece. It is important to find the right location and orientation for the workpiece with respect to the robot to meet all the task constraints. This problem is called a workpiece placement problem. We formulate the problem of identifying a feasible placement as a non-linear optimization problem over the constraint violation functions. This is a computationally challenging problem. Our approach includes successively searching for the solution by incrementally applying different constraints. We prove the gains in success rate of a gradient-based algorithm when constraints are applied successively by developing a theory based on attractor basins. An initial guess can be attracted to regions of infeasibility when all constraints are applied at once. Successively application prioritizes constraints and directs the consecutive iterates to explore feasible regions.

Sequence of Mobile Base Placements

Sensors are widely used in the industry to collect information about a physical object. Operational range of the sensor is limited and therefore the sensor needs to be moved around a large complex part in order to capture complete information. Robot arm or manipulators can provide the degrees of freedom needed to maneuver the sensor through the complex geometry. However, a robotic arm has a limited workspace as well and cannot cover large parts. Mobile base can enhance the capability of the robotic arm by adding mobility to the arm and carrying the arm around the part. Mobile base will need to relocate around the part during the process. Relocating the mobile base increases execution time and also introduces uncertainty in the localization as mobile base moves inaccurately. It is important to reduce the number of mobile base repositioning and reduce execution time and uncertainty. In this paper, we develop a motion planner that finds the minimum number of mobile base placements in order to find robotic arm trajectories that can cover a large complex part using a RGB-D camera sensor. The planning problem, also known as optimal base sequencing, is challenging due to the immensity of the search space. The computation costs involved in inverse kinematics calculations also adds to the search time. A branch and bound search algorithm is developed with efficient branch guiding and pruning heuristics that quickly explores the search space. A capability map based method is developed to improve the search space construction time. Output of our method is an optimal sequence of base placements for the mobile base that will lead to minimum number of placements and execution time required for the process.

Planning Algorithms for Acquiring High Fidelity PointClouds for Fast and Accurate 3D Reconstruction

Sensors are widely used to construct a 3D model of parts by collecting data. The accuracy of the collected data depends on sensor placement with respect to the part and sensor operational range. The operational range and limitations of the sensor need to be applied as constraints while planning for robot motions that move the sensor around the part to collect data. Overly conservative constraints on sensor placement will lead to high execution times. We present an approach where we develop a robot motion planning algorithm that takes into account the camera performance constraints and produces output with low error. An RGB-D camera is used to obtain the pointcloud of the part. An offline planning method improves point density in the regions having zero or low density. Our method guarantees a high point density across the surface of the part. Results are presented on six geometries with different complexity and surface properties. We also present results on how camera parameters influence the output of our method. Our results show that algorithmic advances reported in this paper enable us to use low-cost depth cameras for producing high accuracy uniform density scans of physical objects.

Robotic Supportless Additive Manufacturing

Extrusion-based additive manufacturing systems usually use three degrees of freedom extrusion tools to perform the deposition operation. This requires the use of support structures to deposit structures with overhang features. The use of support structures can be avoided by adding degrees of freedom to the build platform. The elimination of build structures can offer benefits in terms of reduction of build time and elimination of post-processing costs. The work demonstrated that the use of three degrees of freedom build platform enables printing of complex shapes without support structures. We presented computational foundations for generating paths and trajectories for synchronizing the motion of three degrees of freedom build platforms and three degrees of freedom extrusion tools. We reported results on six different test parts in terms of reduction in build time, accuracy, and surface roughness. The parts had an average accuracy of 0.193 mm and a maximum error of about 0.9 mm using a 0.8 mm extrusion tooltip for material deposition. Build time reduction of 88% was observed as compared to printing with support.

Human Robot Collaboration on Assembly Operations

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. 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.