Demanding for warehousing automation and intelligence is critical with the rapid development of information and big data teleology. In most storehouses, cargos are mainly carried by Automatic Guided Vehicles (AGVs) or even manpower. Most AGVs follow predefined paths that are normally paved by some marks detected by AGVs. It largely limits the application of AGVs. Some AGVs relies on highly accurate Light Laser Detection and Ranging (LiDAR) devices for navigation. The paper presents a low-cost localization approach for indoor AGVs with the localization accuracy up to centimetres. The proposed method not only allows an AGV to move precisely without any predefined paths, but also reduces the cost largely. The Ultra Wide Band (UWB) technology is used in our approach. In the paper, a gradient decent method cooperated with a least square method is developed to deal with the nonlinearity of UWB ranging data. An optimal localization result is achieved within a small amount of iterations. The positioning results are also compared to a high-accuracy Real-Time Kinematic (RTK) satellite system. Meanwhile, the approach is able to diagnose the original UWB data and will discard any data corrupted by non-ignorable noises. Thus, the robustness is guaranteed.

Compared to RYHJ, HPC is a safer and more efficient method to treat benign strictures of hilar biliary ducts. It preserves the sphincter of Oddi and normal biliary duct pressure, thus avoiding bile reflux into the bile duct.

The basic cognitive architecture of our brain is still unknown. However, scientists have found evidence for existence of distinct behavioral control systems shared by humans and nonhumans. Inspired by the problem solving systems of the behavioral control in the primate brain, a hierarchical computational model is presented. We focus on the integrative performance of brain substructures, each of which is represented by a problem solver that is further modeled by a certain algorithm. Different levels of brain substructures, as well as the corresponding algorithms, are hierarchically organized both in structure and in function, including how and when higher-order solvers control lower-order ones. Different problem solvers share a same slice of working memory. This novelty is claimed since most of existing brain models emphasize on the neural network structure even though the neuron dynamics of brain is still very controversial. And we compare its performance to three other computational models in the face of a challenging foraging problem. Agents are examined in foraging environment with different sizes, and/or transparent barriers. The experimental results show that our model performed the best outright in most scenarios. Further, the results discover that the virtues of our primate brain lie not only in the heights of thinking it can reach, but also in its range and versatility. INDEX TERMS cognitive hierarchical architecture, computational model, reinforcement learning, local and global planning

The need for automating cargo management in a manufacture environment is urgent. Recent advance in big data and artificial intelligence areas has made it possible. As we known, most cargoes are transported using autonomously guided vehicles or manpower. Many autonomously guided vehicles follow paths that are predefined and usually marked by labels that can be easily found by vehicles. The use of predefined path for vehicle navigation greatly limits the deployment of autonomously guided vehicles. A few vehicles use high accuracy Light Laser Detection and Ranging for sensing and guidance. In this paper, a low-cost and precise method for mobile robots has been developed. Considering the high nonlinearity of UWB ranging data, a least square method is used to find optimal locations and a gradient decent approach is applied for fast convergence to the optimal results. The proposed method is also able to diagnose the ultra-wide band data and will throw away any data corrupted by noise. Therefore, the robustness is obtained.

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