The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operationa...The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.展开更多
Exposed crystal facets directly affect the electrochemical/catalytic performance of MnO2 materials during their applications in supercapacitors, rechargeable batteries, and fuel cells. Currently, the facet-controlled ...Exposed crystal facets directly affect the electrochemical/catalytic performance of MnO2 materials during their applications in supercapacitors, rechargeable batteries, and fuel cells. Currently, the facet-controlled synthesis of MnO2 is facing serious challenges due to the lack of an in-depth understanding of their surface evolution mechanisms. Here, combining aberration-corrected scanning transmission electron microscopy （STEM） and high-resolution TEM, we revealed a mutual energy-driven mechanism between beta-MnO2 nanowires and microstructures that dominated the evolution of the lateral facets in both structures. The evolution of the lateral surfaces followed the elimination of the {100} facets and increased the occupancy of {110} facets with the increase in hydrothermal retention time. Both self-growth and oriented attachment along their {100} facets were observed as two different ways to reduce the surface energies of the beta-MnO2 structures. High-density screw dislocations with the 1/2〈100〉 Burgers vector were generated consequently. The observed surface evolution phenomenon offers guidance for the facet-controlled growth of beta- MnO2 materials with high performances for its application in metal-air batteries, fuel cells, supercapacitors, etc.展开更多
文摘The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.
文摘Exposed crystal facets directly affect the electrochemical/catalytic performance of MnO2 materials during their applications in supercapacitors, rechargeable batteries, and fuel cells. Currently, the facet-controlled synthesis of MnO2 is facing serious challenges due to the lack of an in-depth understanding of their surface evolution mechanisms. Here, combining aberration-corrected scanning transmission electron microscopy （STEM） and high-resolution TEM, we revealed a mutual energy-driven mechanism between beta-MnO2 nanowires and microstructures that dominated the evolution of the lateral facets in both structures. The evolution of the lateral surfaces followed the elimination of the {100} facets and increased the occupancy of {110} facets with the increase in hydrothermal retention time. Both self-growth and oriented attachment along their {100} facets were observed as two different ways to reduce the surface energies of the beta-MnO2 structures. High-density screw dislocations with the 1/2〈100〉 Burgers vector were generated consequently. The observed surface evolution phenomenon offers guidance for the facet-controlled growth of beta- MnO2 materials with high performances for its application in metal-air batteries, fuel cells, supercapacitors, etc.
