Mobile medical robots have been widely used in various structured scenarios, such as hospital drug delivery, public area disinfection, and medical examinations. Considering the challenge of environment modeling and controller design, how to achieve the information from the human demonstration in a structured environment directly arouse our interests. Learning skills is a powerful way that can reduce the complexity of algorithm in searching space. This is especially true when naturally acquiring new skills, as mobile medical robot must learn from the interaction with a human being or the environment with limited programming effort. In this paper, a learning scheme with nonlinear model predictive control (NMPC) is proposed for mobile robot path tracking. The learning-by-imitation system consists of two levels of hierarchy: in the first level, a multi-virtual spring-dampers system is presented for imitation of the mobile robot's trajectories; and in the second level, the NMPC method is used in the motion control system. The NMPC strategy utilizes a varying-parameter one-layer projection neural network to solve an online quadratic programming optimization via iteration over a limited receding horizon. The proposed algorithm is evaluated on a mobile medical robot with an emulated trajectory in simulation and three scenarios used in the experiment.

Customers’ choice decisions often involve two stages during which customers first use noncompensatory rules to form a consideration set and then make the final choice through careful compensatory tradeoffs. In this work, we propose a two-stage network-based modeling approach to study customers’ consideration and choice behaviors in a separate but integrated manner. The first stage models customer preferences in forming a consideration set of multiple alternatives, and the second stage models customers’ choice preference given individuals’ consideration sets. Specifically, bipartite exponential random graph (ERG) models are used in both stages to capture customers’ interdependent choices. For comparison, we also model customers’ choice decisions when consideration set information is not available. Using data from the 2013 China auto market, our results suggest that exogenous attributes (i.e., car attributes, customer demographics, and perceived satisfaction ratings) and the endogenous network structural factor (i.e., vehicle popularity) significantly influence customers’ decisions. Moreover, our results highlight the differences between customer preferences in the consideration stage and the purchase stage. To the authors’ knowledge, this is the first attempt of developing a two-stage network-based approach to analytically model customers’ consideration and purchase decisions, respectively. Second, this work further demonstrates the benefits of the network approach versus traditional logistic regressions for modeling customer preferences. In particular, network approaches are effective for modeling the inherent interdependencies underlying customers’ decision-making processes. The insights drawn from this study have general implications for the choice modeling in engineering design.

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We present the first results of a tracking system prototype using the artificial retina algorithm for fast track finding. The system is based on extensive parallelization and interconnectivity, and allows real-time tracking with offline-like quality with a latency < 1 μs. The artificial retina algorithm has been implemented on a novel custom data acquisition board, based on commercial FPGAs, that we have designed and constructed. The retina architecture is organized in three main blocks: a switch for the parallel distribution of the hits, a pool of engines for the digital processing of the hits, and a block for track parameter calculations. In the switch module memory buffers store the hits information according to a hold logic that gets activated when downstream modules are busy. A real-time tracking prototype has been built and tested successfully on beam at the CERN SPS with input track rate of about 300 kHz. It consists of a telescope with 8 planes of single-sided strip detectors readout using custom ASICs and providing hit position to the tracking processor. The tracking performance of the system are compatible with offline results and the system response is in agreement with simulations