Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals

This paper addresses the motion planning problem for a team of aerial agents under high level goals. We propose a hybrid control strategy that guarantees the accomplishment of each agent's local goal specification, which is given as a temporal logic formula, while guaranteeing inter-agent collision avoidance. In particular, by defining 3-D spheres that bound the agents' volume, we extend previous work on decentralized navigation functions and propose control laws that navigate the agents among predefined regions of interest of the workspace while avoiding collision with each other. This allows us to abstract the motion of the agents as finite transition systems and, by employing standard formal verification techniques, to derive a high-level control algorithm that satisfies the agents' specifications. Simulation and experimental results with quadrotors verify the validity of the proposed method.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 201

Molecular dynamics simulation of graphene sinking during chemical vapor deposition growth on semi-molten Cu substrate

Copper foil is the most promising catalyst for the synthesis of large-area, high-quality monolayer graphene. Experimentally, it has been found that the Cu substrate is semi-molten at graphene growth temperatures. In this study, based on a self-developed C-Cu empirical potential and density functional theory (DFT) methods, we performed systematic molecular dynamics simulations to explore the stability of graphene nanostructures, i.e., carbon nanoclusters and graphene nanoribbons, on semi-molten Cu substrates. Many atomic details observed in the classical MD simulations agree well with those seen in DFT-MD simulations, confirming the high accuracy of the C-Cu potential. Depending on the size of the graphene island, two different sunken-modes are observed: (i) graphene island sinks into the first layer of the metal substrate and (ii) many metal atoms surround the graphene island. Further study reveals that the sinking graphene leads to the unidirectional alignment and seamless stitching of the graphene islands, which explains the growth of large single-crystal graphene on Cu foil. This study deepens our physical insights into the CVD growth of graphene on semi-molten Cu substrate with multiple experimental mysteries well explained and provides theoretic references for the controlled synthesis of large-area single-crystalline monolayer graphene

Theoretical analysis of a membrane-based cross-flow liquid desiccant system

Liquid desiccant air dehumidification has become one of the most widely used dehumidification technologies with advantages of high efficiency, no liquid condensate droplets and capability of energy storage. In this paper a cross-flow mathematical model is developed for a single layer membrane unit. The governing equations are solved iteratively by finite difference method. The performance analysis is carried out for a small-scale membrane-based dehumidification module consisting of 8 air channels and 8 solution channels. The influences of main design parameters on system effectiveness are evaluated. These include air flow rate (NTU), solution to air mass flow rate ration (m*) and solution inlet temperature and concentration. It is revealed that higher sensible and latent effectiveness can be achieved with larger NTU and m*. Increasing solution concentration can also improve the dehumidification effect

Theoretical analysis of a membrane-based cross-flow liquid desiccant system

Liquid desiccant air dehumidification has become one of the most widely used dehumidification technologies with advantages of high efficiency, no liquid condensate droplets and capability of energy storage. In this paper a cross-flow mathematical model is developed for a single layer membrane unit. The governing equations are solved iteratively by finite difference method. The performance analysis is carried out for a small-scale membrane-based dehumidification module consisting of 8 air channels and 8 solution channels. The influences of main design parameters on system effectiveness are evaluated. These include air flow rate (NTU), solution to air mass flow rate ration (m*) and solution inlet temperature and concentration. It is revealed that higher sensible and latent effectiveness can be achieved with larger NTU and m*. Increasing solution concentration can also improve the dehumidification effect

Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis

In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resulting in performance drop due to the significant quantization loss. On the contrary, we search the optimal subset of convolution operation that activates the sparse convolution at various locations for quantization error alleviation, and the performance gap between real-valued and binary sparse convolutional networks is closed without complexity overhead. Specifically, we first present the shifted sparse convolution that fuses the information in the receptive field for the active sites that match the pre-defined positions. Then we employ the differentiable search strategies to discover the optimal opsitions for active site matching in the shifted sparse convolution, and the quantization errors are significantly alleviated for efficient point cloud analysis. For fair evaluation of the proposed method, we empirically select the recently advances that are beneficial for sparse convolution network binarization to construct a strong baseline. The experimental results on Scan-Net and NYU Depth v2 show that our BSC-Net achieves significant improvement upon our srtong baseline and outperforms the state-of-the-art network binarization methods by a remarkable margin without additional computation overhead for binarizing sparse convolutional networks.Comment: Accepted to CVPR202