Navigation Problem Research Articles

Striatal Cholinergic Interneurons Are Required for Contending Strategy Selection While Solving Spatial Navigation Problems.

How do animals adopt a given behavioral strategy to solve a recurrent problem when several effective strategies are available to reach the goal? Here we provide evidence that striatal cholinergic interneurons (SCINs) modulate their activity when mice must select between different strategies with similar goal-reaching effectiveness. Using a cell type-specific transgenic murine system, we show that adult SCIN ablation impairs strategy selection in navigational tasks where a goal can be independently achieved by adopting an allocentric or egocentric strategy. SCIN-depleted mice learn to achieve the goal in these tasks, regardless of their appetitive or aversive nature, in a similar way as controls. However, they cannot shift away from their initially adopted strategies, as control mice do, as training progresses. Our results indicate that SCINs are required for shaping the probability function used for strategy selection as experience accumulates throughout training. Thus, SCINs may be critical for the resolution of cognitive conflicts emerging when several strategies compete for behavioral control while adapting to environmental demands. Our findings may increase our understanding about the emergence of perseverative/compulsive traits in neuropsychiatric disorders with a reported SCIN reduction, such as Tourette and Williams syndromes.SIGNIFICANCE STATEMENT Selecting the best suited strategy to solve a problem is vital. Accordingly, available strategies must be compared across multiple dimensions, such as goal attainment effectiveness, cost-benefit trade-off, and cognitive load. The striatum is involved in strategy selection when strategies clearly diverge in their goal attainment capacity; however, its role whenever several strategies can be used for goal reaching-therefore making selection dependent on additional strategy dimensions-remains poorly understood. Here, we show that striatal cholinergic interneurons can signal strategy competition. Furthermore, they are required to adopt a given strategy whenever strategies with similar goal attainment capacity compete for behavioral control. Our study suggests that striatal cholinergic dysfunction may result in anomalous resolution of problems whenever complex cognitive valuations are required.

Relative Pose Determination of Uncooperative Spacecraft Based on Circle Feature

This paper investigates the problem of spacecraft relative navigation with respect to an unknown target during the close-proximity operations in the on-orbit service system. The serving spacecraft is equipped with a Time-of-Flight (ToF) camera for object recognition and feature detection. A fast and robust relative navigation strategy for acquisition is presented without any extra information about the target by using the natural circle features. The architecture of the proposed relative navigation strategy consists of three ingredients. First, a point cloud segmentation method based on the auxiliary gray image is developed for fast extraction of the circle feature point cloud of the target. Secondly, a new parameter fitting method of circle features is proposed including circle feature calculation by two different geometric models and results’ fusion. Finally, a specific definition of the coordinate frame system is introduced to solve the relative pose with respect to the uncooperative target. In order to validate the efficiency of the segmentation, an experimental test is conducted based on real-time image data acquired by the ToF camera. The total time consumption is saved by 94%. In addition, numerical simulations are carried out to evaluate the proposed navigation algorithm. It shows good robustness under the different levels of noises.

Adding abductive reasoning to a propositional logic

AbstractWe present a technique for obtaining a logic with abductive reasoning extending a given propositional logic. Abduction, along with deduction and induction, is recognized as important for machine learning, namely in identifying possible causes that may lead to the occurrence of an event and in providing new ways for a computational device to achieve a certain objective. Each rule in the original calculus induces a set of multiple-conclusion abductive rules. Moreover, rules stating generic properties of abduction have to be added. In the induced logic, the deductive mechanism of the base logic coexists with this abductive component. A new notion of a multiple-conclusion derivation had to be developed. Due to the canonical nature of obtaining such a logic, we prove the preservation of soundness, completeness, decidability and computational complexity. These concepts and results are illustrated in a robot navigation problem using a multimodal logic.

Extremal results for graphs of bounded metric dimension

Metric dimension is a graph parameter motivated by problems in robot navigation, drug design, and image processing. In this paper, we answer several open extremal problems on metric dimension and pattern avoidance in graphs from Geneson (2020). Specifically, we construct a new family of graphs that allows us to determine the maximum possible degree of a graph of metric dimension at most k, the maximum possible degeneracy of a graph of metric dimension at most k, the maximum possible chromatic number of a graph of metric dimension at most k, and the maximum n for which there exists a graph of metric dimension at most k that contains Kn,n.We also investigate a variant of metric dimension called edge metric dimension and solve another problem from the same paper for n sufficiently large by showing that the edge metric dimension of Pnd is d for n≥dd−1. In addition, we use a probabilistic argument to make progress on another open problem from the same paper by showing that the maximum possible clique number of a graph of edge metric dimension at most k is 2Θ(k). We also make progress on a problem from Zubrilina (2018) by finding a family of new triples (x,y,n) for which there exists a graph of metric dimension x, edge metric dimension y, and order n. In particular, we show that for each integer k>0, there exist graphs G with metric dimension k, edge metric dimension 3k(1−o(1)), and order 3k(1+o(1)).

Optimization of a heterogeneous computing system architecture based on its coordination with a sensors group of a mobile unmanned platform

This article analyses the existing variety of sensors used in robotics and related fields and also proposes the architecture of a heterogeneous computing system designed to analyze data obtained from sensors of a mobile unmanned platform (MUP). A feature of unmanned platforms is the presence of tasks that require a significantly different level of performance of the on-board computing system for processing data from sensors of the corresponding type. Therefore, the adaptation of existing universal computing systems seems to be impractical, compared to the development of a specialized computing system with a heterogeneous architecture. The computing system is designed to solve problems of local navigation, stabilize the position of the MUP and control its movement, as well as control special equipment installed on the MUP. Often, if the goal is to ensure maximum efficiency, expressed in speed, accuracy and reliability, it is necessary to develop specialized devices. The article provides information on sensors of the main types used in robotics and indicates the requirements for the performance of a computing system necessary for processing data from sensors of this type. This, in turn, made it possible to propose a heterogeneous architecture containing processor subsystems focused on processing data from sensors requiring low, medium and high performance according to the considered classification.

Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment

Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.

A Positioning Method Based on Place Cells and Head-Direction Cells for Inertial/Visual Brain-Inspired Navigation System.

Mammals rely on vision and self-motion information in nature to distinguish directions and navigate accurately and stably. Inspired by the mammalian brain neurons to represent the spatial environment, the brain-inspired positioning method based on multi-sensors’ input is proposed to solve the problem of accurate navigation in the absence of satellite signals. In the research related to the application of brain-inspired engineering, it is not common to fuse various sensor information to improve positioning accuracy and decode navigation parameters from the encoded information of the brain-inspired model. Therefore, this paper establishes the head-direction cell model and the place cell model with application potential based on continuous attractor neural networks (CANNs) to encode visual and inertial input information, and then decodes the direction and position according to the population neuron firing response. The experimental results confirm that the brain-inspired navigation model integrates a variety of information, outputs more accurate and stable navigation parameters, and generates motion paths. The proposed model promotes the effective development of brain-inspired navigation research.

Spatial navigation performance in people with multiple sclerosis-a large-scale online study

ObjectiveSpatial navigation has a crucial function in daily life activities, and is therefore strongly linked to quality of life, autonomy, and mobility. Navigation has been shown to be frequently impaired after forms of acquired brain injury, but the impact of MS on navigation ability has yet to be studied. A better understanding of potential navigation problems in this population could improve patient care. Therefore the aim of the current study was to measure objective and subjective navigation performance in people with MS. MethodsPerformance of a large sample of people with MS (N = 359) was compared to a group of matched controls. Additionally, the impact of ambulation and self-reported cognitive performance was studied within the MS sample. Participants filled out the Wayfinding Questionnaire, the patient-reported Expanded Disability Status Scale and the Multiple Sclerosis Neuropsychological Screening Questionnaire for self-report measures. Objective navigation performance was measured with an online navigation test using a virtual environment. ResultsResults indicate a lower subjective as well as objective performance in people with MS compared to healthy controls, and a substantial contribution of self-reported cognitive performance on navigation ability. ConclusionsThese findings indicate that spatial navigation can be a significant problem in people with MS, especially in people with MS with other cognitive impairments.

Collision Avoidance for a Car-like Mobile Robots using Deep Reinforcement Learning

—The applications of mobile robots are more and more diverse and extensive. The motion planning of the mobile robots should be considered in aspect of effectiveness of the navigation, and collision-free motion is essential for mobile robots. In addition, dynamic path planning of unknown environment has always been a challenge for mobile robots. Aiming at navigation problems, this paper proposes a Deep Reinforcement Learning (DRL) based path planning algorithm which can navigate nonholonomic car-like mobile robots in an unknown dynamic environment. The output of the learned network are the robot’s translational and angular velocities for the next time step. The method combines path planning on a 2D grid with reinforcement learning and does not need any supervision. The experiments illustrate that our trained policy can be applied to solve complex navigation tasks. Furthermore, we compare the performance of our learned controller to the popular approaches. Keywords— Deep reinforcement learning, path planning, , artificial neural network, mobile robot, autonomous vehicle

Extreme 3D Image Navigation in Mobile Robotics

The actual problem of determining all six coordinates of the current position of a mobile robot (unmanned aerial vehicle) from 3D-range-finding images (point clouds) generated by an onboard 3D laser sensor when moving (flying) in an unknown environment is considered. An extreme navigation algorithm based on using multidimensional optimization methods is proposed. The rules for calculating the difference between 3D images of the external environment used for optimization of the functional are described. The form of the functional of the difference of 3D images for different environments (premises, industrial-urban environment, rugged and wooded areas) has been investigated. Requirements for the characteristics of the sensor and the geometry of the external environment are formulated, the fulfillment of which ensures the correct formulation and solution of the problem of extreme navigation. The optimal methods of scanning the surrounding space are described and the conditions are substantiated, the fulfillment of which ensures the solution of the navigation problem by the proposed algorithm in real time (at the rate of movement) when processing 3D images formed by modern 3D laser sensors. In particular, the dependence between the frequency of formation of 3D images and the angular and linear velocities of motion is described, which ensures that the functional of the difference of 3D images falls into the multidimensional interval of unimodality, which guarantees a direct search of global minimum in real time. Various methods of direct search for the global minimum of the functional are tested and the fastest for the case under consideration are selected. The accuracy of solving the navigation problem is estimated and a method is proposed to reduce the accumulated error, based on using an older 3D image for correcting the calculated value of the current coordinates, which has an intersection of the view area with the current view area. The proposed method, which is a modification of the reference image method, allows reduce the total error, which grows in proportion to the number of cycles of solving the extreme navigation problem, to values that ensure the autonomous functioning of transport robots and UAVs in previously unprepared and unknown environments. The effectiveness of the proposed algorithmic and developed software and hardware for extreme navigation is confirmed by field experiments carried out in real conditions of various environments.

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resources – within single or distributed graphs – can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct – called Property Paths (PPs) – to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach – dubbed DpcLD – where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.

Reinforcement Learning for Uncooperative Space Objects Smart Imaging Path-Planning

Leading space agencies are increasingly investing in the gradual automation of space missions. In fact, autonomous flight operations may be a key enabler for on-orbit servicing, assembly and manufacturing (OSAM) missions, carrying inherent benefits such as cost and risk reduction. Within the spectrum of proximity operations, this work focuses on autonomous path-planning for the reconstruction of geometry properties of an uncooperative target. The autonomous navigation problem is called active Simultaneous Localization and Mapping (SLAM) problem, and it has been largely studied within the field of robotics. Active SLAM problem may be formulated as a Partially Observable Markov Decision Process (POMDP). Previous works in astrodynamics have demonstrated that is possible to use Reinforcement Learning (RL) techniques to teach an agent that is moving along a pre-determined orbit when to collect measurements to optimize a given mapping goal. In this work, different RL methods are explored to develop an artificial intelligence agent capable of planning sub-optimal paths for autonomous shape reconstruction of an unknown and uncooperative object via imaging. Proximity orbit dynamics are linearized and include orbit eccentricity. The geometry of the target object is rendered by a polyhedron shaped with a triangular mesh. Artificial intelligent agents are created using both the Deep Q-Network (DQN) and the Advantage Actor Critic (A2C) method. State-action value functions are approximated using Artificial Neural Networks (ANN) and trained according to RL principles. Training of the RL agent architecture occurs under fixed or random initial environment conditions. A large database of training tests has been collected. Trained agents show promising performance in achieving extended coverage of the target. Policy learning is demonstrated by displaying that RL agents, at minimum, have higher mapping performance than agents that behave randomly. Furthermore, RL agent may learn to maneuver the spacecraft to control target lighting conditions as a function of the Sun location. This work, therefore, preliminary demonstrates the applicability of RL to autonomous imaging of an uncooperative space object, thus setting a baseline for future works.

Hierarchical Voltage Control Strategy in Distribution Networks Considering Customized Charging Navigation of Electric Vehicles

This paper proposes a three-layer hierarchical voltage control strategy for distribution networks considering the customized charging navigation of electric vehicles (EVs). In the first layer, optimal power flow (OPF) is performed to determine the day-ahead dispatch of on-load tap changer (OLTC) and capacitor banks (CBs). The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second layer, a customized charging navigation strategy is proposed to conduct the charging behaviors of EVs based on their own preferences. The novel preference modes are designed for different types of EV users to include not only the charge and time cost, but also the willingness to participate in voltage regulation service (VRS). The navigation problem is formulated as a mixed-integer linear programming (MILP), which is then solved by CPLEX solvers embedded with Dijkstra algorithm. In the third layer, charging stations measure local voltage and regulate the charging power of EVs to mitigate voltage violation. The charging selection and power allocation process are performed dynamically considering the mutual effect between the second and third layers. The economic compensation mechanism is also designed for both EV users and charging stations. The proposed approach is tested on the IEEE 33-bus distribution system coupled with a 24-bus transportation system, and simulation results verify the effectiveness both in charging navigation and mitigating voltage violation.

Relative positioning of UWB tags with deferred ranging

The aim of the work is to develop relative navigation systems based on ultrawideband signals. In a relative navigation system, measurements are transmitted not to the base station, but to mobile subscribers who solve the navigation problem. The work of the network with the SDS-TWR distance measurement protocol is shown and a method for reducing the load in the network of devices is proposed, in which delayed distance measurements are used.

Visual odometry in local underwater navigation problems

The work processes of the ORB-SLAM algorithm are presented. The results of experimental studies on temporal comparisons of the operation of the algorithm with different parameters and cameras are presented. The necessity of forming a visual odometry (VO) system as a local navigation of remote-controlled and autonomous underwater robots has been substantiated. The two most suitable odometry methods in the underwater environment are described, such as their advantages and disadvantages. The work processes of the ORB-SLAM algorithm are presented. The results of experimental studies on temporal comparisons of the operation of the algorithm with different parameters and cameras are presented. The procedure for preparing video data is described: processing a video stream, adjusting camera parameters for calibration. The experiments represent the testing of the ORB-SLAM3 algorithm on a sample of video filmed as part of the ecological monitoring of the Caspian shelf in 2020.

Metric Dimension on Path-Related Graphs

Graph theory has a large number of applications in the fields of computer networking, robotics, Loran or sonar models, medical networks, electrical networking, facility location problems, navigation problems etc. It also plays an important role in studying the properties of chemical structures. In the field of telecommunication networks such as CCTV cameras, fiber optics, and cable networking, the metric dimension has a vital role. Metric dimension can help us in minimizing cost, labour, and time in the above discussed networks and in making them more efficient. Resolvability also has applications in tricky games, processing of maps or images, pattern recognitions, and robot navigation. We defined some new graphs and named them s − middle graphs, s -total graphs, symmetrical planar pyramid graph, reflection symmetrical planar pyramid graph, middle tower path graph, and reflection middle tower path graph. In the recent study, metric dimension of these path-related graphs is computed.

Experiments on Recognition of Infinite Grid Graph Labelling

In recent years, topics related to automaton analysis of geometric environment have attracted widespread attention. The interaction of an automaton and an environment is often represented as a process of an automaton walks along a graph (labyrinth) of environment. Treating environment as vertex-labelled graph was suggested as one approach to addressing the problem of automaton analysis of environment properties. Research in this regard received a wide range of applications, for example, in the problems of image analysis and navigation of mobile robots. This paper considers a four-way infinite lattice graph as a model of an environment for a graph-walking automaton. All vertices of this graph are labelled with labels from a known set but concrete labelling function is not a priori known. The automaton looking over neighbourhood of the current vertex and may travel to some neighbouring vertex selected by its label. The objective of the automaton is to determine two pairs of opposite directions on the aforementioned graph embedded onto integer lattice, i.e. recognizing the graph labelling. In previous work we have proposed a labelling thanks to which a finite automaton can walk on graph in any arbitrary direction. We have called this labelling deterministic. We have proved that minimal deterministic vertex labelling of infinite lattice graph uses labels of five different types. The paper demonstrates that there exists 240 different minimal deterministic labellings of the infinite lattice graph with a fixed set of labels. We prove that a single automaton cannot recognize minimal deterministic labelling of the infinite lattice graph, but automaton with one pebble can. The novelty associated with this paper is a new type of experiment with vertex labelled graphs designed to recognition their labelling. The method of constructing and performing recognition experiment for labelled infinite grid graph is proposed.

Reactive navigation in partially familiar planar environments using semantic perceptual feedback

This article solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in simultaneous localization and mapping (SLAM) and visual object recognition to recast prior geometric knowledge in terms of an offline catalog of familiar objects. The resulting vector field planner guarantees convergence to an arbitrarily specified goal, avoiding collisions along the way with fixed but arbitrarily placed instances from the catalog as well as completely unknown fixed obstacles so long as they are strongly convex and well separated. We illustrate the generic robustness properties of such deterministic reactive planners as well as the relatively modest computational cost of this algorithm by supplementing an extensive numerical study with physical implementation on both a wheeled and legged platform in different settings.

Deep learning based decomposition for visual navigation in industrial platforms

AbstractIn the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms.