The ability to solubilize lignocellulose makes certain ionic liquids (ILs) very effective reagents for pretreating biomass prior to its saccharification for biofuel fermentation. However, residual IL in the aqueous sugar solution can inhibit the growth and function of biofuel-producing microorganisms. In E. coli this toxicity can be partially overcome by the heterologous expression of an IL efflux pump encoded by eilA from Enterobacter lignolyticus. In the present work, we used microarray analysis to identify native E. coli IL-inducible promoters and develop control systems for regulating eilA gene expression. Three candidate promoters, PmarR’, PydfO’, and PydfA’, were selected and compared to the IPTG-inducible PlacUV5 system for controlling expression of eilA. The PydfA’ and PmarR’ based systems are as effective as PlacUV5 in their ability to rescue E. coli from typically toxic levels of IL, thereby eliminating the need to use an IPTG-based system for such tolerance engineering. We present a mechanistic model indicating that inducible control systems reduce target gene expression when IL levels are low. Selected-reaction monitoring mass spectrometry analysis revealed that at high IL concentrations EilA protein levels were significantly elevated under the control of PydfA’ and PmarR’ in comparison to the other promoters. Further, in a pooled culture competition designed to determine fitness, the strain containing pPmarR’-eilA outcompeted strains with other promoter constructs, most significantly at IL concentrations above 150 mM. These results indicate that native promoters such as PmarR’ can provide effective systems for regulating the expression of heterologous genes in host engineering and simplify the development of industrially useful strains.

Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if there exists a perfect predictor that precisely predicts the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles to the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the obstacles’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have an uncertainty tolerance for closed-loop stability, and still avoid collision when the obstacles’ movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With this notion, we verify the performance of the proposed MPC with stability enhanced prediction through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and the closed-loop cost is reduced.

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Efficient and robust task planning for a human-robot collaboration (HRC) system remains challenging. The human-aware task planner needs to assign jobs to both robots and human workers so that they can work collaboratively to achieve better time efficiency. However, the complexity of the tasks and the stochastic nature of the human collaborators bring challenges to such task planning. To reduce the complexity of the planning problem, we utilize the hierarchical task model which explicitly captures the sequential and parallel relationships of the task. To account for human-induced uncertainties, we model human movements with the sigma-lognormal functions. A human action model adaptation scheme is applied during run-time and it provides a measure for modeling the human-induced uncertainties. We propose a sampling-based method to estimate the uncertainties in human job completion time. Next, we propose a robust task planner, which formulates the planning problem as a robust optimization problem by considering the task structure and the uncertainties. We conduct simulations of a robot arm collaborating with a human worker in an electronics assembly setting. The results show that our proposed planner, compared to the baseline planner, can reduce task completion time when human-induced uncertainties occur.

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