During manufacturing, minor flaws in the surface of commercial airplane interior panels are often corrected using hand sanding, sometimes leading to repetitive stress injuries. Robotic sanding is an attractive option to mitigate these injuries. However, in preprogrammed automated sanding, both the sander path and the speed along that path are predetermined. Such fixed automation has limited effectiveness due to the part-to-part variability of surface condition. In addition, in typical fixed automation, a constant contact force and path speed are used to maintain constant material removal depth. Teleoperated robotic sanding allows a skilled operator to monitor the process and the condition of the surface in real time to correct the individual flaws. However, during teleoperated sanding, the path and speed along the path are inherently both time-varying and unknown a priori. The principal contribution of this work is to facilitate precision teleoperated sanding by developing a process model and control strategy that ensures constant material removal depth along the sanding path. Experimental results, with and without the proposed contact-force adjustments, for the same variable speed motion of the sander, shows 65% improvement in spatial variation of material removal with the proposed approach.

Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human–robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an open-source kitchen dataset, we collected a new dataset comprising 20 individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87%–94%) F1 overlap scores among the ground truth and predicted frame labels for videos lasting over 2 min and consisting of a large number of actions.

Devine et al., (2019). StateMint: A Set of Tools for Determining Symbolic Dynamic System Models Using Linear Graph Methods. Journal of Open Source Education, 2(14), 44, https://doi.org/10.21105/jose.00044

Owan et al., (2018). CoreRobotics: An object-oriented C++ library with cross-language wrappers for cross-platform robot control. Journal of Open Source Software, 3(22), 489, https://doi.org/10.21105/joss.00489

We report the development of an instrumentation and control system instantiated on a microprocessor-field programmable gate array (FPGA) device for a harmonic oscillator comprising a portion of a magnetic resonance force microscope. The specific advantages of the system are that it minimizes computation, increases maintainability, and reduces the technical barrier required to enter the experimental field of magnetic resonance force microscopy. Heterodyne digital control and measurement yields computational advantages. A single microprocessor-FPGA device improves system maintainability by using a single programming language. The system presented requires significantly less technical expertise to instantiate than the instrumentation of previous systems, yet integrity of performance is retained and demonstrated with experimental data.