Topological Map Research Articles

Self-Organizing Memory Based on Adaptive Resonance Theory for Vision and Language Navigation

Vision and Language Navigation (VLN) is a task in which an agent needs to understand natural language instructions to reach the target location in a real-scene environment. To improve the model ability of long-horizon planning, emerging research focuses on extending the models with different types of memory structures, mainly including topological maps or a hidden state vector. However, the fixed-length hidden state vector is often insufficient to capture long-term temporal context. In comparison, topological maps have been shown to be beneficial for many robotic navigation tasks. Therefore, we focus on building a feasible and effective topological map representation and using it to improve the navigation performance and the generalization across seen and unseen environments. This paper presents a S elf-organizing Memory based on Adaptive Resonance Theory (SMART) module for incremental topological mapping and a framework for utilizing the SMART module to guide navigation. Based on fusion adaptive resonance theory networks, the SMART module can extract salient scenes from historical observations and build a topological map of the environmental layout. It provides a compact spatial representation and supports the discovery of novel shortcuts through inferences while being explainable in terms of cognitive science. Furthermore, given a language instruction and on top of the topological map, we propose a vision–language alignment framework for navigational decision-making. Notably, the framework utilizes three off-the-shelf pre-trained models to perform landmark extraction, node–landmark matching, and low-level controlling, without any fine-tuning on human-annotated datasets. We validate our approach using the Habitat simulator on VLN-CE tasks, which provides a photo-realistic environment for the embodied agent in continuous action space. The experimental results demonstrate that our approach achieves comparable performance to the supervised baseline.

Research on topological grinding of bionic structured scale surface for reducing contact friction and fluid drag resistance

The structured scale surface is an important functional surface that reduces the drag resistance of fluids and contact friction resistance. In order to grind the bionic scale structure on the workpiece, based on the topological theory and the principle of grinding kinematics, a novel topological grinding strategy was proposed. To this end, the topological feature vectors of the structured scale surface and the grinding wheel surface were established by analyzing the biomimetic structured scale surface; The Topological space of grinding process is constructed and the topological mapping equation of grinding process is established; The feasibility of this grinding strategy was verified through simulation and grinding experiments. The research results indicate that the established topological mapping equation is correct; Under the condition of maintaining design parameters, the errors of the feature parameters of the ground structured scale surface are between 2.8% and 8%; As the grinding parameters change, the feature parameters of the ground scale surface will also change, but they can still maintain the same topological attributes as the designed scale surface. Therefore, the proposed topology grinding strategy is feasible.

A Study of the Global Topological Map Construction Algorithm Based on Grid Map Representation for Multirobot

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (<i>STGA</i>) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed <i>Node-Filter</i> to generate enough topological nodes, and simplified the node set by <i>Node-Selection</i>. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by <i>STGA</i> had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. <i>Note to Practitioners</i>&#x2014;Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.

Properties of Topological Partitions and Mappings of Topological Groups

Properties of Topological Partitions and Mappings of Topological Groups

Incremental Cluster Validity Index-Guided Online Learning for Performance and Robustness to Presentation Order.

In streaming data applications, the incoming samples are processed and discarded, and therefore, intelligent decision-making is crucial for the performance of lifelong learning systems. In addition, the order in which the samples arrive may heavily affect the performance of incremental learners. The recently introduced incremental cluster validity indices (iCVIs) provide valuable aid in addressing such class of problems. Their primary use case has been cluster quality monitoring; nonetheless, they have been recently integrated in a streaming clustering method. In this context, the work presented, here, introduces the first adaptive resonance theory (ART)-based model that uses iCVIs for unsupervised and semi-supervised online learning. Moreover, it shows how to use iCVIs to regulate ART vigilance via an iCVI-based match tracking mechanism. The model achieves improved accuracy and robustness to ordering effects by integrating an online iCVI module as module B of a topological ART predictive mapping (TopoARTMAP)-thereby being named iCVI-TopoARTMAP-and using iCVI-driven postprocessing heuristics at the end of each learning step. The online iCVI module provides assignments of input samples to clusters at each iteration in accordance to any of the several iCVIs. The iCVI-TopoARTMAP maintains useful properties shared by the ART predictive mapping (ARTMAP) models, such as stability, immunity to catastrophic forgetting, and the many-to-one mapping capability via the map field module. The performance and robustness to the presentation order of iCVI-TopoARTMAP were evaluated via experiments with synthetic and real-world datasets.

Ultrasonographic Presentation and Anatomic Distribution of Lung Involvement in Patients with Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic auto-immune disease, typically affecting the joints, which can also present with lung involvement (pleuritis, interstitial lung disease, pulmonary nodules, etc.). Lung ultrasound (LUS) is an upcoming tool in the detection of these pulmonary manifestations. We performed a 72-window LUS in 75 patients presenting to the outpatient rheumatology clinic and describe the abnormalities (presence of B-lines (vertical comet-tail artefacts), pleural abnormalities, pleural effusions, and subpleural nodules) on lung ultrasound. We created a topological mapping of the number of B-lines per intercostal zone. We observed pleural effusions, pleural abnormalities, and pleural nodules in, respectively, 1.3%, 45.3%, and 14% of patients. There were 35 (46.7%) patients who had less than 5 B-lines, 15 (20%) patients who had between 5 and 10 B-lines, 11 (14.6%) between 10 and 20, 10 (13.3%) between 20 and 50, 1 (1.3%) between 50 and 100, and 3 (4%) of patients who had more than 100 B-lines. LUS in patients with RA shows an array of abnormalities ranging from interstitial syndromes to pleural abnormalities, subpleural nodules, and pleural effusions. Hotspots for the presence of B-lines are situated bilaterally in the posterior subscapular regions, as well as the anterior right mid-clavicular region.

Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap.

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods.

Collaborative path planning and task allocation for multiple agricultural machines

Path planning and task allocation are critical concerns in multi-machine collaborative operations for unmanned farms. Nevertheless, several problems remain in the operation of agricultural machinery, such as the slow path planning algorithm, the omission of the working area, and the unreasonable scheduling of machines, resulting in low efficiency and wasted resources. Collaborative and complete coverage path planning was achieved to solve the problems of slow path planning algorithms and the omission of working areas. The farm’s electronic map was constructed using the topological map method. The improved Dijkstra algorithm based on priority queues was combined with three different complete coverage methods: the nested method, the reciprocating method, and the combination of nested and internal spiral path methods. The simulation results show that the improved Dijkstra method based on priority queues can effectively minimize the running time of the algorithm. The reciprocating method has a higher coverage index than the other two methods, with an average coverage rate of 94.73 %. To solve the problem of illogical scheduling of the same type of agricultural machines, an improved ant colony method was presented based on the whole working path to minimize the path cost. The simulation results show that the proposed method can allocate the task properly, and the path cost is reduced by 14 %–33 %. By combining the proposed path planning and task allocation methods, the whole-process path planning of a single agricultural machine and multiple agricultural machines of the same type was achieved, providing a technical solution for promoting the construction of unmanned farms.

New Forms of alphag - continuous Maps in Topology

Abstract: In this paper, we describe a novel class of continuous maps called as strongly alpha generalised continuous maps in topological spaces. Furthermore, we investigate their properties with existing continuous maps, and we also study their decomposition of continuous functions.

A topological weakening and softening map as simplified tool to assess the performances recovery of hybridized natural fiber reinforced composites subjected to alternate salt-fog/dry cycle

While receiving a growing attention in recent years, natural fibers cannot completely replace synthetic fibers as composite reinforcement for structural applications due to both their low durability in wet or humid environments and their limited and not homogenous overall performance. In this context, the purpose of this paper is to assess the impact of a humid/dry cycle on the mechanical stability of epoxy-based laminates reinforced with flax and glass fibers by using a topological weakening and softening map as simplified tool. The objective is, preliminarily, to evaluate the influence of glass fiber hybridization on the properties recovery of flax fiber reinforced composites subjected to alternate salt-fog/dry cycle. All laminates studied were subjected to salt spray for 15 or 30 days, and then stored in a dry controlled environment (50% relative humidity and 22 °C) for up to 21 days. The results evidenced that, compared to flax fiber reinforced composites, the glass hybridization of flax composite significantly reduce the mechanical performances degradation over time during the humid stage (about 28.0% better than flax one in stiffness). Furthermore, the mechanical performance recovery is promoted during the dry stage. A simplified topological map was lastly developed to graphically assess the decline and recovery of composites’ performances during the humid/dry cycle, amplifying the application and design effects of this approach.

Path-Following and Collision-Avoidance Controls of a Robot in a Large Building with One or More Elevators

For planning a robot’s path inside a large building with one or more elevators, we develop a topological map, called the building Voronoi graph. Using the building Voronoi graph, the robot finds the shortest path to the goal and follows the path. In the case where the robot detects an object with arbitrary shapes (e.g., human) while following the path, the robot avoids the object utilizing reactive control laws. The proposed reactive collision-avoidance control is unique in considering collision avoidance with map environments as well as (moving or static) objects having arbitrary shapes. As far as we know, our paper is novel in addressing how to make the robot follow the building Voronoi graph, while avoiding collision with map environments as well as objects with arbitrary shapes.

Coverage Path Planning Method for Agricultural Spraying UAV in Arbitrary Polygon Area

In the coverage path planning (CPP) problem of an agricultural spraying UAV, a margin reduction algorithm was designed first to address special situations such as ditches and channels within the operational terrain. Regarding the particularity of a concave polygon area, an algorithm based on topology mapping was developed to judge the concave points of the concave polygon area, and the path with special concave points was scheduled. For the evaluation of the pesticide spraying mission, the flight distance and extra coverage ratio were the most appropriate optimization objectives. Therefore, this paper selected these two indicators to form a fitness function, then found the optimal operating heading angle of the mission after iterative optimization. Finally, the CPP for an agricultural spraying UAV in an arbitrary polygon area under the optimal heading angle was realized. The simulation and flight test results showed that the CPP method could significantly shorten the flight distance and reduce additional coverage, then avoid energy consumption and pesticide waste. In addition, the engineering practicability of the method was verified in this paper. This method can be popularized and widely used for an agricultural spraying UAV, which has great engineering application value.

A study on the internal logic of local government emergency response capacity indicators based on the G-Dematel-Aism model

The local government's handling of emergencies has a bearing on the lives of local people, property safety and a series of rights. Therefore, how to build the emergency response capacity of local governments has become an important topic in the field of public management. In this paper, we study the important indexes of local government's emergency management and combine them with the algorithm of systematics to construct the matrix and topological map, and finally calculate the degree of importance and the logic relationship between the indexes The calculation results provide ideas for the focus and logic of local government's emergency response capacity building.

The effects of topological features on convolutional neural networks—an explanatory analysis via Grad-CAM

Topological data analysis (TDA) characterizes the global structure of data based on topological invariants such as persistent homology, whereas convolutional neural networks (CNNs) are capable of characterizing local features in the global structure of the data. In contrast, a combined model of TDA and CNN, a family of multimodal networks, simultaneously takes the image and the corresponding topological features as the input to the network for classification, thereby significantly improving the performance of a single CNN. This innovative approach has been recently successful in various applications. However, there is a lack of explanation regarding how and why topological signatures, when combined with a CNN, improve discriminative power. In this paper, we use persistent homology to compute topological features and subsequently demonstrate both qualitatively and quantitatively the effects of topological signatures on a CNN model, for which the Grad-CAM analysis of multimodal networks and topological inverse image map are proposed and appropriately utilized. For experimental validation, we utilize two famous datasets: the transient versus bogus image dataset and the HAM10000 dataset. Using Grad-CAM analysis of multimodal networks, we demonstrate that topological features enforce the image network of a CNN to focus more on significant and meaningful regions across images rather than task-irrelevant artifacts such as background noise and texture.

Camera Network Topology Mapping Based on the Integration of Network Information and Physical Distribution Under the Background of Communication Security

At present, most cameras use internal networks and use methods such as Traceroute for security protection, which cannot meet the requirements of camera network mapping. Therefore, a camera mapping scheme of network information and physical distribution is proposed. Firstly, the network topology problem of video content information collection was analyzed. This paper uses the mapping relationship between network space and physical space to propose the subnet division conjecture method and complete the preliminary mapping of the network through video data screening. Considering the insufficient coverage of topology mapping, a judgment and inference method based on Bayesian classification technology and network information is proposed, and the results are corrected and evaluated through the test of Jackard coefficient. In the preliminary network topology performance test, two state-of-the-art schemes are selected for experimental comparison. When the number of nodes in the proposed scheme is 5, 25, and 50, the mapping can be completed in the shortest time, and the accuracy reaches 80%. However, the surveying and mapping accuracy of the proposed scheme in the preliminary test is low, and the network information method is used for data screening. In the final surveying and mapping performance test, when the number of nodes is 40, the accuracy of the proposed scheme is 96%, which is better than previously proposed schemes, while the testing delay time is shorter. The technology proposed in the study has the best overall performance. It can effectively solve the problem of intranet surveying and mapping and has important reference value for the security protection of the camera network.

Fibration theorems à la Milnor for analytic maps with non-isolated singularities

AbstractWe study the topology of real analytic maps in a neighborhood of a (possibly non-isolated) critical point. We prove fibration theorems à la Milnor for real analytic maps with non-isolated critical values. Here we study the situation for maps with arbitrary critical set. We use the concept of d-regularity introduced in an earlier paper for maps with an isolated critical value. We prove that this is the key point for the existence of a Milnor fibration on the sphere in the general setting. Plenty of examples are discussed along the text, particularly the interesting family of functions $$(f,g):{\mathbb {R}}^n \rightarrow {\mathbb {R}}^2$$ ( f , g ) : R n → R 2 of the type $$\begin{aligned} (f,g) = \left( \sum _{i=1}^n a_i x_i^p, \sum _{i=1}^n b_i x_i^q \right) , \end{aligned}$$ ( f , g ) = ∑ i = 1 n a i x i p , ∑ i = 1 n b i x i q , where $$a_i, b_i \in {\mathbb {R}}$$ a i , b i ∈ R are constants in generic position and $$p,q \ge 2$$ p , q ≥ 2 are integers.

Roboat III: An autonomous surface vessel for urban transportation

AbstractIn this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers.

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.

A Novel Self-Organizing Map (SOM) With Data Mining Model for Formulation of Vocational Education Policies

Data mining is a process of extracting valuable and previously unknown patterns, insights, and knowledge from large datasets. data mining techniques enable the analysis of educational data to inform multi-level vocational education policies that contribute to regional sustainable development. By leveraging the power of data, policymakers can make evidence-based decisions, align vocational education with regional needs, and enhance the effectiveness and relevance of vocational education programs in promoting sustainable development. In this paper proposed an Associative Rule Kohonen SOM (AR-KS) for multi-level vocational educational policies assessment for sustainable development. Initially, the proposed AR-KS model collects relevant data, including information on vocational education programs, student performance records, regional development indicators, and policy documents. This data is then preprocessed to ensure consistency and quality. The next step involves training a Kohonen SOM using the preprocessed data. The SOM forms a topological map where each neuron represents a unique combination of policy variables and regional development factors. This map captures the multidimensional relationships among the variables and provides a visual representation of the policy space. The relationship between the patterns is computed with the associative rule-based training of SOM between variables. The analysis of the AR-KS framework allows policymakers to assess the impact of different vocational education policies on sustainable development goals. Overall, the proposed AR-KS framework offers a novel approach for assessing multi-level vocational educational policies in the context of sustainable development.

Human Pose Estimation with Multi-Camera Localization Using Multi-Objective Optimization Based on Topological Structured Learning

This paper presents a method of pose estimation with camera localization based on a genetic algorithm to derive the joint angle using an inverse kinematics model. In recent years, some open-source libraries for human skeleton tracking that can be implemented on smartphones have been released. Such software can detect joint positions of a skeleton from a 2D camera image; however, the 3D posture cannot be obtained. We therefore propose a method to estimate joint angles by using skeleton data. In the proposed approach, we use a multi-island genetic algorithm to maintain the diversity of the population and design a multi-objective function to improve the robustness. Moreover, structured learning based on topological mapping is implemented in the proposed method to enhance searching efficiency. In an experiment, the proposed method reduced the effect of outliers caused by misdetection. In addition, the structured learning was effective in decreasing the difference between skeleton data and the estimated poses.