Visual Navigation With Multiple Goals Based on Deep Reinforcement Learning.

Learning to adapt to a series of different goals in visual navigation is challenging. In this work, we present a model-embedded actor-critic architecture for the multigoal visual navigation task. To enhance the task cooperation in multigoal learning, we introduce two new designs to the reinforcement learning scheme: inverse dynamics model (InvDM) and multigoal colearning (MgCl). Specifically, InvDM is proposed to capture the navigation-relevant association between state and goal and provide additional training signals to relieve the sparse reward issue. MgCl aims at improving the sample efficiency and supports the agent to learn from unintentional positive experiences. Besides, to further improve the scene generalization capability of the agent, we present an enhanced navigation model that consists of two self-supervised auxiliary task modules. The first module, which is named path closed-loop detection, helps to understand whether the state has been experienced. The second one, namely the state-target matching module, tries to figure out the difference between state and goal. Extensive results on the interactive platform AI2-THOR demonstrate that the agent trained with the proposed method converges faster than state-of-the-art methods while owning good generalization capability. The video demonstration is available at https://vsislab.github.io/mgvn.

Broadband Gerchberg-Saxton algorithm for freeform diffractive spectral filter design.

A multi-wavelength expansion of the Gerchberg-Saxton (GS) algorithm is developed to design and optimize a surface relief Diffractive Optical Element (DOE). The DOE simultaneously diffracts distinct wavelength bands into separate target regions. A description of the algorithm is provided, and parameters that affect filter performance are examined. Performance is based on the spectral power collected within specified regions on a receiver plane. The modified GS algorithm is used to design spectrum splitting optics for CdSe and Si photovoltaic (PV) cells. The DOE has average optical efficiency of 87.5% over the spectral bands of interest (400-710 nm and 710-1100 nm). Simulated PV conversion efficiency is 37.7%, which is 29.3% higher than the efficiency of the better performing PV cell without spectrum splitting optics.

Spectrum splitting metrics and effect of filter characteristics on photovoltaic system performance.

During the past few years there has been a significant interest in spectrum splitting systems to increase the overall efficiency of photovoltaic solar energy systems. However, methods for comparing the performance of spectrum splitting systems and the effects of optical spectral filter design on system performance are not well developed. This paper addresses these two areas. The system conversion efficiency is examined in detail and the role of optical spectral filters with respect to the efficiency is developed. A new metric termed the Improvement over Best Bandgap is defined which expresses the efficiency gain of the spectrum splitting system with respect to a similar system that contains the highest constituent single bandgap photovoltaic cell. This parameter indicates the benefit of using the more complex spectrum splitting system with respect to a single bandgap photovoltaic system. Metrics are also provided to assess the performance of experimental spectral filters in different spectrum splitting configurations. The paper concludes by using the methodology to evaluate spectrum splitting systems with different filter configurations and indicates the overall efficiency improvement that is possible with ideal and experimental designs.