Abstract
Robotics competitions stimulate the next generation of cutting edge robotics solutions and innovative technologies. The World Robot Summit (WRS) Industrial Assembly challenge posed a key research challenge: how to develop adaptive industrial assembly robots. The overall goal is to develop robots where minimal hardware or software changes are required to manufacture a new or altered product. This will minimise waste and allow the industry to move towards a far more flexible approach to manufacturing; this will provide exciting new technologies for the manufacturing industry and support many new business models and approaches. In this paper, we present an approach where general-purpose grippers and adaptive control approaches have been developed to move towards this research goal. These approaches enable highly flexible and adaptive assembly of a belt drive system. The abilities of this approach were demonstrated by taking part in the WRS Industrial Assembly Challenge. We achieved second place in the kitting challenge and second place in the adaptive manufacturing challenge and were presented with the Innovation Award.
Highlights
There is a need for industrial robots that can manufacture or assemble products which are bespoke or have a variable design and bill of materials [1]
We devise a new convolutional neural network, identical to the network designed in Sect. 3.7, but substitute the inception layer devised by a 5 × 5 convolution layer with 80 channels
This research has demonstrated how adaptive manufacturing systems can be developed. This has been achieved by developing adaptive grippers which can pick up a wide range of parts and tools, and accompanying control strategies which use force feedback to achieve resilience to changes in design
Summary
There is a need for industrial robots that can manufacture or assemble products which are bespoke or have a variable design and bill of materials [1]. Application areas include aerospace, the space industry, and industrial assembly [2,3], where it is useful to manufacture small runs of specific assemblies and rapidly adapt to changing or evolving designs. Developing adaptive manufacturing systems has the potential to reduce waste, increase the rate of assembly, and allow bespoke design. There has been much existing work in this area, in particular with a focus on how dual-arm systems can work collaboratively with humans to assemble systems [4,5,6,7,8]. It is necessary to advance this research to move towards fully autonomous, flexible, and adaptive assembly systems.
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