Additive manufacturing (AM) technologies have been widely used to fabricate three-dimensional (3D) objects quickly and cost-effectively. However, building parts consisting of complex geometries with curvatures can be a challenging process for the traditional AM system whose capability is restricted to planar layered printing. Using six degrees-of-freedom (DOF) industrial robots for AM overcomes this limitation by allowing the material deposition to take place on nonplanar surfaces. In this paper, we present trajectory planning algorithms for 3D printing using nonplanar material deposition. Trajectory parameters are selected to avoid collision with printing surface and satisfy robot constraints. We have implemented our approach by using a 6DOF robot arm. The complex 3D structures with various curvatures were successfully fabricated with a good surface finish.

Robotic bin picking requires using a perception system to estimate the posture of parts in the bin. The selected singulation plan should be robust with respect to perception uncertainties. If the estimated posture is significantly different from the actual posture, then the singulation plan may fail during execution. In such cases, the singulation process will need to be repeated. We are interested in selecting singulation plans that minimize the expected task completion time. In order to estimate the expected task completion time for a proposed singulation plan, we need to estimate the probability of success and the plan execution time. Robotic bin picking needs to be done in real-time. Therefore, candidate singulation plans need to be generated and evaluated in real-time. This paper presents an approach for utilizing computationally efficient simulations for generating singulation plans. Results from physical experiments match well with the predictions obtained from simulations.