D'Urso, G., Smith, S.L., Mettu, R., Oksanen, T. & Fitch, R. 2018, 'Multi-vehicle refill scheduling with queueing', Computers and Electronics in Agriculture, vol. 144, pp. 44-57.
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© 2017 We consider the problem of refill scheduling for a team of vehicles or robots that must contend for access to a single physical location for refilling. The objective is to minimise time spent in travelling to/from the refill station, and also time lost to queuing (waiting for access). In this paper, we present principled results for this problem in the context of agricultural operations. We first establish that the problem is NP-hard and prove that the maximum number of vehicles that can usefully work together is bounded. We then focus on the design of practical algorithms and present two solutions. The first is an exact algorithm based on dynamic programming that is suitable for small problem instances. The second is an approximate anytime algorithm based on the branch and bound approach that is suitable for large problem instances with many robots. We present simulated results of our algorithms for three classes of agricultural work that cover a range of operations: spot spraying, broadcast spraying and slurry application. We show that the algorithm is reasonably robust to inaccurate prediction of resource utilisation rate, which is difficult to estimate in cases such as spot application of herbicide for weed control, and validate its performance in simulation using realistic scenarios with up to 30 robots.
Song, J., Wang, J., Zhao, L., Huang, S. & Dissanayake, G. 2018, 'Dynamic Reconstruction of Deformable Soft-Tissue With Stereo Scope in Minimal Invasive Surgery', IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 155-162.
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To, W., paul, G. & liu, D. 2018, 'A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots', Robotics and Computer-Integrated Manufacturing.
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This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the lasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.