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.

Managing robot workloads in human robot teams is critical for efficient team operation. If robots are overloaded with work, then they will miss deadlines and force humans to take on extra work. This paper presents a framework for a robot to assess its own workload based on an initial goal assignment. The robot does this by generating task and motion plans and computing the probability of missing deadlines due to the possibility of delays in task execution. A branch and bound based search is used to generate task and motion plans by minimizing task execution effort. The robot presents a diverse set of task and motion plans to the humans to offer multiple different options. Humans can either approve a plan or provide guidance to reduce the workload by either relaxing deadlines or removing goal(s) assigned to the robots.

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.

Composites are increasingly becoming a material of choice in the aerospace and automotive industries. Currently, many composite parts are produced by manually laying up sheets on complex molds. Composite sheet layup requires executing two main tasks: (1) grasping a sheet and (2) draping it on the mold. Automating the layup process requires automation of these two tasks. This paper is focused on the automation of the grasping task using robots. This requires an automated generation of grasp plans to enable robots to hold the sheet during the draping process. We present a simulation-based approach for determining robot grasp locations on the composite sheets. We also present an intervention controller that uses a real-time sheet tracking system during plan execution and can prevent failures. We demonstrate the performance of the developed system using a large complex part.

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.

We pose the problem of path-constrained trajectory generation for the synchronous motion of multi-robot systems as a non-linear optimization problem. Our method determines appropriate parametric representation for the configuration variables, generates an approximate solution as a starting point for the optimization method, and uses successive refinement techniques to solve the problem in a computationally efficient manner. We have demonstrated the effectiveness of the proposed method on challenging simulation and physical experiments with high degrees of freedom robotic systems.

Successfully completing a complex manufacturing task requires finding a feasible placement of the workpiece in the robot workspace. The workpiece placement should be such that the task surfaces on the workpiece are reachable by the robot, the robot can apply the required forces, and the end-effector/tool can move with the desired velocity. This paper formulates the problem of identifying a feasible placement as a non-linear optimization problem over the constraint violation functions. This is a computationally challenging problem. We show that this problem can be solved by successively searching for the solution by incrementally applying different constraints. We demonstrate the feasibility of our approach using several complex workpieces.