Estimated path information gain-based robot exploration under perceptual uncertainty

At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

Partial particle and wave information and weak duality games

Duality games are a way of looking at wave-particle duality. In these games. Alice and Bob together are playing against the House. The House specifies, at random, which of two sub-games Alice and Bob will play. One game, Ways, requires that they obtain path information about a particle going through an N-path interferometer and the other, Phases, requires that they obtain phase information. In general, because of wave-particle duality, Alice and Bob cannot always win the overall game. However, if the required amount of path and phase information is not too great, for example specifying a set of paths or phases, one of which is the right one, then they can always win. Here we study examples of duality games that can always be won, and develop a wave-particle duality relation expressed only in terms of mutual information to help analyze these games.

Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types

The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.

A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm

As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.

‘Invisible’ spatial meaning: a text-based study of covert Path encoding in Ancient Greek

This paper aims to explore the main constructions showing a covert encoding of Path of motion in Classical Greek (5th–4th century BC). Based on a corpus study of five texts belonging to different literary genres, it applies the theoretical frameworks and conceptual tools of contemporary linguistic approaches, such as the semantic typology of motion events and Construction Grammar, to the data from an ancient language, in order to address the non-compositional expression of Path. The results of the analysis reveal that, in addition to the overt morphosyntactic encoding of Path information, Ancient Greek resorts to more implicit patterns, in which coercion, meaning extension and inference play a crucial role. Furthermore, as opposed to traditional views on motion expression, this study shows that the encoding of spatial meaning is rarely committed to a single lexical or morphological tool within the clause, but rather it distributes across different linguistic units and results from their interaction with one another.

‘Invisible’ spatial meaning: a text-based study of covert Path encoding in Ancient Greek

This paper aims to explore the main constructions showing a covert encoding of Path of motion in Classical Greek (5th–4th century BC). Based on a corpus study of five texts belonging to different literary genres, it applies the theoretical frameworks and conceptual tools of contemporary linguistic approaches, such as the semantic typology of motion events and Construction Grammar, to the data from an ancient language, in order to address the non-compositional expression of Path. The results of the analysis reveal that, in addition to the overt morphosyntactic encoding of Path information, Ancient Greek resorts to more implicit patterns, in which coercion, meaning extension and inference play a crucial role. Furthermore, as opposed to traditional views on motion expression, this study shows that the encoding of spatial meaning is rarely committed to a single lexical or morphological tool within the clause, but rather it distributes across different linguistic units and results from their interaction with one another.