Bao Pang

Shandong University

Author Statistics

Papers

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H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Yong Song

Shandong University

Xianfeng Yuan

Shandong University

Qingyang Xu

Shandong University

Yibin Li

Shandong University

Runtao Yang

Shandong University

Chengjin Zhang

Syngenta (United States)

Zong Quan Deng

Harbin Institute of Technology

Hongling Wang

East China University of Technology

Hongling Wang

Nanyang Technological University

Bing Hui Wu

Shanghai University of Electric Power

Cooperative Institutions

Shandong University

Harbin Institute of Technology

Chinese Academy of Sciences

Weihai Science and Technology Bureau

Northeastern University

Shanghai Jiao Tong University

Applied Materials (United States)

Huazhong University of Science and Technology

Ministry of Education of the People's Republic of China

Chinese PLA General Hospital

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

The Effect on Dynamic Properties of Reactive Powder Concrete under High Temperature Burnt and its Micro-Structure Analysis

Key engineering materials (2010)

Bao Pang L. W. Wang Z.Q. Yang Run Qiang

In this paper, the dynamic macroscopical properties of reactive powder concrete (RPC) are studied by using the split Hopkinson pressure bar (SHPB) system. The effect on dynamic properties of RPC material with different water binder ratio (W/B) and steel fiber influence under high temperature burnt is discussed. And scanning electron microscope (SEM) technology is also used to investigate the micro-structure change under high temperature burnt. The experiment result shows: without high temperature burnt, the higher water binder ratio, the lower dynamic compression strength of RPC material. With high temperature burnt, strength decreases exquisite. However, steel fiber can prevent micro-crack from generating, the toughness effect still alive after high temperature burnt. From SEM analysis: the inner part of RPC will have a series of physical chemistry changing, such as: micro-crack generation, C-S-H gel damage. This essential change is the basic reason for dynamic properties degrade.

Split Hopkinson Pressure Bar

Dynamic range compression

Bar (unit)

10.4028/www.scientific.net/kem.452-453.109

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Citations (4)

Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots

Applied Sciences (2023)

Bao Pang Ziqi Zhang Yong Song Xianfeng Yuan Qingyang Xu

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes.

Swarm Robotics

Obstacle avoidance

Stimulus (psychology)

10.3390/app13169107

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Citations (0)

An improved Bacterial Foraging Optimization algorithm using novel chemotaxis and swarming strategy

Bao Pang Yong Song Chengjin Zhang Hongling Wang Runtao Yang

This paper proposes an improved Bacterial foraging optimization (BFO) algorithm based on modified chemotaxis process and novel swarming strategy. Three improvements are presented in the proposed algorithm. First, during the chemotactic process, each bacterium selects one dimension for tumbling randomly to reduce the mutual interference among different dimensions. Second, each bacterium's movement-length on the selected dimension is determined by the stochastic flight lengths of the improved Levy flight which is composed of many short step-size combined with rarer longer step-size, with this pattern repeated across all scales; moreover, to achieve a better search result, the stochastic step-size is also reduced adaptively based on the evolutionary generations. Third, inspired by the social information term in particle swarm optimization (PSO), the global best solution is used to guild the swarming direction to increase the swarming performance. Experiments are carried out with the aim of studying the performance of the proposed LPBFO algorithm on six widely used functions. The experimental results and analysis demonstrate that the method obtains a marked improvement compared with other competitive algorithms.

Swarming (honey bee)

Lévy flight

Guild

10.1109/icinfa.2018.8812479

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Citations (0)

Dynamic Response Threshold Model for Self-organized Task Allocation in a Swarm of Foraging Robots

Bao Pang Ziqi Zhang Yong Song Xianfeng Yuan Qingyang Xu

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.

Swarm Robotics

Stimulus (psychology)

Obstacle avoidance

10.20944/preprints202307.0955.v1

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Citations (1)

A Modified Artificial Bee Colony Algorithm Based on the Self-Learning Mechanism

Algorithms (2018)

Bao Pang Yong Song Chengjin Zhang Hongling Wang Runtao Yang

Artificial bee colony (ABC) algorithm, a novel category of bionic intelligent optimization algorithm, was achieved for solving complex nonlinear optimization problems. Previous studies have shown that ABC algorithm is competitive to other biological-inspired optimization algorithms, but there still exist several insufficiencies due to the inefficient solution search equation (SSE), which does well in exploration but poorly in exploitation. To improve accuracy of the solutions, this paper proposes a modified ABC algorithm based on the self-learning mechanism (SLABC) with five SSEs as the candidate operator pool; among them, one is good at exploration and two of them are good at exploitation; another SSE intends to balance exploration and exploitation; moreover, the last SSE with Lévy flight step-size which can generate smaller step-size with high frequency and bigger step-size occasionally not only can balance exploration and exploitation but also possesses the ability to escape from the local optimum. This paper proposes a simple self-learning mechanism, wherein the SSE is selected according to the previous success ratio in generating promising solutions at each iteration. Experiments on a set of 9 benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. The experimental results illustrated that the SLABC algorithm achieves significant improvement compared with other competitive algorithms.

Benchmark (surveying)

Operator (biology)

10.3390/a11060078

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Citations (8)

Modeling and Simulation of Flexible Band Based on the Muti-Body Dynamics

Key engineering materials (2010)

Bing Hui Wu Bao Pang Zong Quan Deng

To provide theory and technique support for the research on dynamic properties of flexible lock-unlock technology, dynamic equation of band is built up based on the muti-body dynamics. Simulation based on modal neutral file and mixed model with rigid and flexible properties are accomplished in the software of ADAMS. The result reveals that the later one is much closed to the actual movement.

Lock (firearm)

Dynamics

Dynamic simulation

10.4028/www.scientific.net/kem.458.200

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Citations (0)

Module optimization modeling with discrete event simulation

Robert Bachrach M.A. Pool Raja Sunkara Bao Pang

Module optimization modeling with discrete event simulation is described. The models allow the organization of tools and chambers and visualization of the modules in different configurations as well as the analysis of the expected performance relative to parameters. Representative results for a Dep/Etch/Dep module are presented.

Discrete-Event Simulation

Discrete optimization

Simulation Modeling

Solid modeling

10.1109/issm.2000.993684

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Citations (1)

STABILITY ANALYSIS FOR DELAYED NEURAL NETWORKS BASED ON A NEW DELAY PARTITIONING APPROACH

ICIC express letters. Part B, Applications (2013)

Liangdong Guo Bao Pang Xiqin He

Source

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Spatial Transform Soft Actor-Critic for Robot Grasping Skill Learning

2021 China Automation Congress (CAC) (2023)

Zihao Sun Xianfeng Yuan Yong Song Xiaolong Xu Bao Pang

In recent years, manipulation skill learning has become an important research direction in the robotics field. As one of the most basic robot skills, grasping skill learning has also received a lot of attention. Deep reinforcement learning (DRL) has been widely used in skill learning because of its strong perception and decision ability. However, the existing grasping methods based on DRL usually have low learning efficiency and poor skill adaptability. Aiming at this problem, this paper proposes an improved DRL method called spatial transform soft actor-critic (ST -SAC) for robot grasping skill learning. ST -SAC adopts a spatial transform convolution neural network (CNN) to perceive the environment state and employs the soft actor-critic method to make grasping skill decisions to improve model learning efficiency and adaptability. During training, a curriculum learning strategy is used to enhance the generalization and convergence of the model. The effectiveness of the proposed ST -SAC is verified by object grasping experiments, which is superior to other methods in grasping skill adaptability and learning efficiency.

Soft Robotics

10.1109/cac59555.2023.10451703

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Citations (1)

Autonomous Task Allocation in a Swarm of Foraging Robots: An Approach Based on Response Threshold Sigmoid Model

International Journal of Control Automation and Systems (2019)

Bao Pang Yong Song Chengjin Zhang Hongling Wang Runtao Yang

Obstacle avoidance

Swarm Robotics

10.1007/s12555-017-0585-1

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Citations (15)