Virtual Fixture Generation for Task Planning with Complex Geometries

Many robotic processes require the system to maintain a tool's orientation and distance from a surface. To do so, researchers often use Virtual Fixtures (VFs) to either guide the robot along a path or forbid it from leaving the workspace. Previous efforts relied on volumetric primitives (planes, cylinders, etc.) or raw sensor data to define VFs. However, those approaches only work for a small subset of real-world objects. Extending this approach is complicated not only by VF generation but also generalizing user traversal of the VF to command a robot trajectory remotely. In this work, we present the concept of Task VFs, which convert layers of point cloud based Guidance VF into a bidirectional graph structure and pair it with a Forbidden Region VF. These VFs are hardware-agnostic and can be generated from virtually any source data, including from parametric objects (superellipsoids, supertoroids, etc.), meshes (including from CAD), and real-time sensor data for open-world scenarios. We address surface convexity and concavity since these and distance to the task surface determine the size and resolution of VF layers. This paper then presents the Manipulator-to-Task Transform Tool for Task VF visualization and to limit human-robot interaction ambiguities. Testing confirmed generation success, and users performed spatially discrete experiments to evaluate Task VF usability complex geometries, which showed their interpretability. The Manipulator-to-Task Transform Tool applies many robotic applications, including collision avoidance, process design, training, task definition, etc. for virtually any geometry.