Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture

Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers\u27 path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques

SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping

Soil erosion assessment by RUSLE with improved P factor and its validation: Case study on mountainous and hilly areas of Hubei Province, China

The Revised Universal Soil Loss Equation (RUSLE) is widely used to estimate regional soil erosion. However, quantitative impacts of soil and water conservation (SWC) measures on conservation practice factor (P) of the RUSLE remain largely unclear, especially for the mountainous and hilly areas. In this study, we improved the RUSLE by considering quantitative impacts of different SWC measures on the P factor value. The improved RUSLE was validated against the long-term (2000–2015) soil erosion monitoring data obtained from 96 runoff plots (15–35°) in mountainous and hilly areas of Hubei Province, China; the result presented a high accuracy with the determination coefficient of 0.89. Based on the erosion monitoring data of 2018 and 2019, the Root Mean Square Error of the result by the improved RUSLE was 28.0% smaller than that by the original RUSLE with decrement of 19.6%–24.0% in the average P factor values, indicating that the soil erosion modelling accuracy was significantly enhanced by the improved RUSLE. Relatively low P factor values appeared for farmlands with tillage measures (P < 0.53), grasslands with engineering measures (P < 0.23), woodlands with biological measures (P < 0.28), and other land use types with biological measures (P < 0.51). The soil erosion modulus showed a downward trend with the corresponding values of 1681.21, 1673.14, 1594.70, 1482.40 and 1437.50 t km−2 a−1 in 2000, 2005, 2010, 2015 and 2019, respectively. The applicability of the improved RUSLE was verified by the measurements in typical mountainous and hilly areas of Hubei Province, China, and arrangements of SWC measures of this area were proposed

Electrocatalytic Activity and Design Principles of Heteroatom-Doped Graphene Catalysts for Oxygen-Reduction Reaction

Heteroatom-doped graphene materials have emerged as highly efficient and inexpensive and variations of graphene doping structures; however, there is still a lack of fundamental understanding of the trend and mechanisms in their ORR activity, which greatly hinders the development of highly active graphene-based catalysts. Here we use density-functional calculations to study the ORR activity and mechanism of nonmetal-element doped graphene catalysts with different doping configurations. Our results demonstrate that binding energies of ORR intermediates (i.e., *OH) on the catalysts can serve as a good descriptor for the ORR activity, attaining the optimal value at the vicinity of ∼2.6 eV. The analysis of electronic structures indicates that the ORR activity of doped graphene catalysts depends on the abundance of electronic states at the Fermi level, which dominates the charge transfer between ORR intermediates and the catalysts. Using binding energy as a descriptor, we predict the realization of highly active graphene-based electrocatalysts by the dual-doping scheme, which is supported by recent experimental reports. Moreover, we find that the catalytic activity of graphene basal planes can be activated by the B–Sb and B–N codoping approaches. This work elucidates the inherent correlation between the ORR activity of nonmetal-doped graphene catalysts and the dopant type and doping configurations, opening a route to design highly active graphene-based ORR electrocatalysts

Atomic Mechanism of Electrocatalytically Active Co–N Complexes in Graphene Basal Plane for Oxygen Reduction Reaction

Superior catalytic activity and high chemical stability of inexpensive electrocatalysts for the oxygen reduction reaction (ORR) are crucial to the large-scale practical application of fuel cells. The nonprecious metal/N modified graphene electrocatalysts are regarded as one of potential candidates, and the further enhancement of their catalytic activity depends on improving active reaction sites at not only graphene edges but also its basal plane. Herein, the ORR mechanism and reaction pathways of Co–N co-doping onto the graphene basal plane have been studied by using first-principles calculations and <i>ab initio</i> molecular dynamics simulations. Compared to singly N-doped and Co-doped graphenes, the Co–N co-doped graphene surface exhibits superior ORR activity and the selectivity toward a four-electron reduction pathway. The result originates from catalytic sites of the graphene surface being modified by the hybridization between Co 3d states and N 2p states, resulting in the catalyst with a moderate binding ability to oxygenated intermediates. Hence, introducing the Co–N₄ complex onto the graphene basal plane facilitates the activation of O₂ dissociation and the desorption of H₂O during the ORR, which is responsible for the electrocatalyst with a smaller ORR overpotential (∼1.0 eV) that is lower than that of Co-doped graphene by 0.93 eV. Our results suggest that the Co–N co-doped graphene is able to compete against platinum-based electrocatalysts, and the greater efficient electrocatalysts can be realized by carefully optimizing the coupling between transition metal and nonmetallic dopants in the graphene basal plane

Decadal changes in emissions of volatile organic compounds (VOCs) from on-road vehicles with intensified automobile pollution control: Case study in a busy urban tunnel in south China

In the efforts at controlling automobile emissions, it is important to know in what extent air pollutants from on-road vehicles could be truly reduced. In 2014 we conducted tests in a heavily trafficked tunnel in south China to characterize emissions of volatile organic compounds (VOC) from on-road vehicle fleet and compared our results with those obtained in the same tunnel in 2004. Alkanes, aromatics, and alkenes had average emission factors (EFs) of 338, 63, and 42&nbsp;mg&nbsp;km-1 in 2014 against that of 194, 129, and 160&nbsp;mg&nbsp;km-1 in 2004, respectively. In 2014, LPG-related propane, n-butane and i-butane were the top three non-methane hydrocarbons (NMHCs) with EFs of 184&nbsp;±&nbsp;21, 53&nbsp;±&nbsp;6 and 31&nbsp;±&nbsp;3&nbsp;mg&nbsp;km-1; the gasoline evaporation marker i-pentane had an average EF of 17&nbsp;±&nbsp;3&nbsp;mg&nbsp;km-1; ethylene and propene were the top two alkenes with average EFs of 16&nbsp;±&nbsp;1 and 9.7&nbsp;±&nbsp;0.9&nbsp;mg&nbsp;km-1, respectively; isoprene had no direct emission from vehicles; toluene showed the highest EF of 11&nbsp;±&nbsp;2&nbsp;mg&nbsp;km-1 among the aromatics; and acetylene had an average EF of 7&nbsp;±&nbsp;1&nbsp;mg&nbsp;km-1. While EFs of total NMHCs decreased only 9% from 493&nbsp;±&nbsp;120&nbsp;mg&nbsp;km-1 in 2004 to 449&nbsp;±&nbsp;40&nbsp;mg&nbsp;km-1 in 2014, their total ozone formation potential (OFP) decreased by 57% from 2.50&nbsp;×&nbsp;103&nbsp;mg&nbsp;km-1 in 2004 to 1.10&nbsp;×&nbsp;103&nbsp;mg&nbsp;km-1 in 2014, and their total secondary organic aerosol formation potential (SOAFP) decreased by 50% from 50&nbsp;mg&nbsp;km-1 in 2004 to 25&nbsp;mg&nbsp;km-1 in 2014. The large drop in ozone and SOA formation potentials could be explained by reduced emissions of reactive alkenes and aromatics, due largely to fuel transition from gasoline/diesel to LPG for taxis/buses and upgraded vehicle emission standards