Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators.

The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path's success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method's effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner's efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.

Cobalt-Nickel Nanoparticles Supported on Reducible Oxides as Fischer-Tropsch Catalysts.

Efficient and more sustainable production of transportation fuels is key to fulfill the ever-increasing global demand. In order to achieve this, progress in the development of highly active and selective catalysts is fundamental. The combination of bimetallic nanoparticles and reactive support materials offers unique and complex interactions that can be exploited for improved catalyst performance. Here, we report on cobalt-nickel nanoparticles on reducible metal oxides as support material for enhanced performance in the Fischer-Tropsch synthesis. For this, different cobalt to nickel ratios (Ni/(Ni + Co): 0.0, 0.25, 0.50, 0.75, or 1.0 atom/atom) supported on reducible (TiO2 and Nb2O5) or nonreducible (α-Al2O3) oxides were studied. At 1 bar, Co-Ni nanoparticles supported on TiO2 and Nb2O5 showed stable catalytic performance, high activities and remarkably high selectivities for long-chain hydrocarbons (C5+, ∼80 wt %). In contrast, catalysts supported on α-Al2O3 independently of the metal composition showed lower activities, high methane production, and considerable deactivation throughout the experiment. At 20 bar, the combination of cobalt and nickel supported on reducible oxides allowed for 25-50% cobalt substitution by nickel with increased Fischer-Tropsch activity and without sacrificing much C5+ selectivity. STEM-EDX and IR of adsorbed CO pointed to a cobalt enrichment of the nanoparticle's surface and a weaker adsorption of CO in Co-Ni supported on TiO2 and Nb2O5 and not on α-Al2O3, modifying the rate-determining step and the catalytic performance. Overall, we show the strong effect and potential of reducible metal oxides as support materials for bimetallic nanoparticles for enhanced catalytic performance.

Activity enhancement of cobalt catalysts by tuning metal-support interactions.

Interactions between metal nanoparticles and support materials can strongly influence the performance of catalysts. In particular, reducible oxidic supports can form suboxides that can decorate metal nanoparticles and enhance catalytic performance or block active sites. Therefore, tuning this metal-support interaction is essential for catalyst design. Here, we investigate reduction-oxidation-reduction (ROR) treatments as a method to affect metal-support interactions and related catalytic performance. Controlled oxidation of pre-reduced cobalt on reducible (TiO2 and Nb2O5) and irreducible (α-Al2O3) supports leads to the formation of hollow cobalt oxide particles. The second reduction results in a twofold increase in cobalt surface area only on reducible oxides and proportionally enhances the cobalt-based catalytic activity during Fischer-Tropsch synthesis at industrially relevant conditions. Such activities are usually only obtained by noble metal promotion of cobalt catalysts. ROR proves an effective approach to tune the interaction between metallic nanoparticles and reducible oxidic supports, leading to improved catalytic performance.

Understanding the redox behaviour of PbCrO4 and its application in selective hydrogen combustion.

The performance of PbCrO(4) during reduction/oxidation cycles has been studied over the temperature range 673-873 K. During thermal treatment in an inert atmosphere, PbCrO(4) is stable up to 773 K. At higher temperatures, it decomposes rapidly and irreversibly to Pb(2)(CrO(4))O. Moreover, the redox cycling of Pb(2)(CrO(4))O is also irreversible at 873 K when the reduction semi-reaction is prolonged beyond 3 min. Taking a standard 2 min cyclic treatment, we demonstrate that the surface reduction/oxidation of Pb(2)(CrO(4))O is exothermic between 773-873 K. In contrast, the redox cycling of PbCrO(4) is endothermic at 673 K. These findings demonstrate for the first time the potential of Pb(2)(CrO(4))O as a solid oxygen reservoir for the oxidative dehydrogenation of alkanes; especially at T ≥ 773 K, where H(2) oxidation by Pb(2)(CrO(4))O is exothermic. Preliminary kinetic studies suggest that Pb(2)(CrO(4))O reduction and oxidation both proceed through a "3D diffusion of the reacting front" mechanism. Our results open up opportunities for developing energy-efficient oxidative dehydrogenation routes to commercially important olefins.