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Robotic manufacturing is increasingly moving towards one-off assembly tasks. Agile manufacturing systems must be able to adapt to changing parts and designs. The addition of a soft interface, between a picking tool and a part, can improve the performance and introduce new ways of picking and manipulating parts without increasing complexity. The introduced compliance also assists when mating parts. These soft interfaces can be applied in minimalist pick and place tasks with passive gripping. This work investigates the model behind gel-based soft interfaces, and demonstrates the application for manipulating fasteners in an agile assembly task. The model predicts the limit to the holding force is from a vacuum seal, the actual holding force can be lower due to the fluid properties of the gel in confined geometries. The soft interface is shown to give adaptability for a single tool to pick multiple different sizes of parts. This interface gives success rate > 90% for well aligned cross-head bolts, and much better performance for hex keys and bolts. © 2019 IEEE.

Prefabrication of structures is currently used in a limited capacity, due to the lack of flexibility, despite the potential cost and speed advantages. Autonomous flexible reassembly enables structures to be developed which can be continuously and iteratively dis-assembled and re-assembled providing far more flexibility in comparison to single shot pre-fabrication methods. Dis-assembly of structures should be considered when assembling, due to the asymmetry of assembly and dis-assembly processes, to ensure structures can be recycled and re-assembled. This allows for agile development, significantly reducing the time and resource usage during the build process. In this work, a framework for flexible re-assembly is developed and a robotic platform is developed to implement and test this framework with simple Lego bricks. The tradeoffs in terms of time, resource use and probability of success of this new assembly method can be understood by using a cost function to compare to alternative fabrication methods. © 2018 IEEE.

Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors’ knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 h to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task. © 2020, Springer Nature Switzerland AG.