A model of autonomous positioning through associating environment memory information is presented for unmanned combat aerial vehicle(UCAV).The representation strategy of environment by constructing place cells is used...A model of autonomous positioning through associating environment memory information is presented for unmanned combat aerial vehicle(UCAV).The representation strategy of environment by constructing place cells is used to produce the memory information,and the landmarks in memory are retrieved through perceiving and processing the environment.During UCAV′s flight,the landmarks are obtained in real-time and are matched with the landmarks in memory.Then,the idea of ranging positioning is adopted to calculate UCAV′s location based on the corresponding relationship between current obtained landmarks and the memorized landmarks.Simulation shows that the proposed model can realize autonomous positioning in the memorized environment,and the positioning performance is well when the sensor has a high precision.展开更多
A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the dis...A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the distance to the nearest obstacle from any given location. A Chamfer distance based sensor model to associate observations from a laser ranger finder to the map of the environment without the need for ray tracing, data association, or feature extraction is presented. It is shown that the robot can be localised by solving a non-linear optimisation problem formulated to minimise the Chamfer distance with respect to the robot location. The proposed algorithm is able to perform well even when robot odometry is unavailable and requires only a single tuning parameter to operate even in highly dynamic environments. As such, it is superior than the state-of-the-art particle filter based solutions for robot localisation in occupancy grids, provided that an approximate initial location of the robot is available. Experimental results based on simulated and public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.展开更多
Graph representations of solid state materials that encode only interatomic-distance information lack geometrical resolution,resulting in degenerate representations that may map distinct structures to equivalent graph...Graph representations of solid state materials that encode only interatomic-distance information lack geometrical resolution,resulting in degenerate representations that may map distinct structures to equivalent graphs.Here,we propose a hypergraph representation scheme for materials that allows for the association of higher-order geometrical information with hyperedges.Hyperedges generalize edges to connected sets of more than two nodes,and may be used to represent triplets and local environments of atoms in materials.This generalization of edges requires a different approach in graph convolution,which is developed in this work.These crystal hypergraph convolutional networks are trained based on various property prediction tasks for a vast set of solid-state materials available via MatBench.Results presented here focus on the improved performance of models based on both pairwise edges and local environment hyperedges.These results demonstrate that hypergraphs are an effective and efficient method for incorporating geometrical information in material representations.展开更多
基金supported by the National Natural Science Foundation of China(No.61273048)
文摘A model of autonomous positioning through associating environment memory information is presented for unmanned combat aerial vehicle(UCAV).The representation strategy of environment by constructing place cells is used to produce the memory information,and the landmarks in memory are retrieved through perceiving and processing the environment.During UCAV′s flight,the landmarks are obtained in real-time and are matched with the landmarks in memory.Then,the idea of ranging positioning is adopted to calculate UCAV′s location based on the corresponding relationship between current obtained landmarks and the memorized landmarks.Simulation shows that the proposed model can realize autonomous positioning in the memorized environment,and the positioning performance is well when the sensor has a high precision.
文摘A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the distance to the nearest obstacle from any given location. A Chamfer distance based sensor model to associate observations from a laser ranger finder to the map of the environment without the need for ray tracing, data association, or feature extraction is presented. It is shown that the robot can be localised by solving a non-linear optimisation problem formulated to minimise the Chamfer distance with respect to the robot location. The proposed algorithm is able to perform well even when robot odometry is unavailable and requires only a single tuning parameter to operate even in highly dynamic environments. As such, it is superior than the state-of-the-art particle filter based solutions for robot localisation in occupancy grids, provided that an approximate initial location of the robot is available. Experimental results based on simulated and public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.
基金supported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,under Award No.DE-SC0023664This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231 using NERSC award BES-ERCAP0029544.
文摘Graph representations of solid state materials that encode only interatomic-distance information lack geometrical resolution,resulting in degenerate representations that may map distinct structures to equivalent graphs.Here,we propose a hypergraph representation scheme for materials that allows for the association of higher-order geometrical information with hyperedges.Hyperedges generalize edges to connected sets of more than two nodes,and may be used to represent triplets and local environments of atoms in materials.This generalization of edges requires a different approach in graph convolution,which is developed in this work.These crystal hypergraph convolutional networks are trained based on various property prediction tasks for a vast set of solid-state materials available via MatBench.Results presented here focus on the improved performance of models based on both pairwise edges and local environment hyperedges.These results demonstrate that hypergraphs are an effective and efficient method for incorporating geometrical information in material representations.
