Navigation Decisions Research Articles

A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

A novel maritime autonomous navigation decision-making system: Modeling, integration, and real ship trial

Maritime Autonomous Surface Ships (MASS) have obtained wide attention in recent years, which is relied on their attractive socio-economic benefits and potential to improve safety. One of the main characteristics of MASS is that it can use perception information to replace Officers on Watch (OOW) to make different navigation decisions through expert and intelligent systems, so as to make navigation more intelligent, reliable and safer, which is mainly based on the Maritime Autonomous Navigation Decision-making System (MANDS). However, the state-of-the-art study lacks a systematic description and research on MANDS, and most of them mainly focus on solving a single navigation decision-making task, which is not aligned with the meaning of autonomous navigation. Therefore, this study develops a novel MANDS and installs it on a 300TEU intelligent container ship to successfully carry out real ship trials of autonomous navigation. In this paper, the definition of autonomous navigation is first proposed, which mainly contains four different navigation tasks, namely “Keep route”, “Route Optimization”, “Collision Avoidance”, “Return Back”. A well-designed architecture for MANDS is presented which includes corresponding decision-making models and communication interfaces with external systems. Furthermore, in order to realize the switching and deployment between different navigation tasks in the process of autonomous navigation, a novel hierarchical decision-making mechanism is proposed and analyzed, and finally the MANDS is integrated with perception system and ship control system respectively. Before the real ship trial, this system is tested and verified in a simulation-based environment which is composed of the electronic chart platform and ship motion simulator, we also design a scenario library for the testing work. At last, real ship trials of autonomous navigation for MANDS are carried out, which can convincingly prove the performance of MANDS. This study proposes a relative complete solution for autonomous navigation, including system modelling, integration and real ship trial. These findings offer new insight into the research of ship autonomous navigation and promote the improvement of ship intelligence and informatization level.

Neighbourhood-level pedestrian navigation using the construal level theory

Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. We present a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network-based decisions at the neighbourhood level. The framework is based on construal level theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. We implement this route choice framework in a spatial agent-based model in which pedestrian and vehicle agents complete trips in an urban environment. Using global sensitivity analysis techniques, we demonstrate the interaction between route choice components representing decision making at different spatial and temporal scales. Additionally, through comparison to a least cost network model, we demonstrate the increased route heterogeneity produced by this approach. This work could form the basis of an alternative method for producing pedestrian route alternatives. The granularity and scale of the modelled pedestrian trajectories could also help improve appraisals of street infrastructure.

Complex encounter situation modeling and prediction method for unmanned ships based on bounded rational game

Comprehensive, accurate, and timely cognition of navigation situation is a prerequisite to make safe, efficient, and scientific decisions for unmanned ships. Previous research on this topic has been more focused on situation perception, and lacks studies of mathematical representation, modeling, and prediction for multi-ship complex encounter situation from an integrated perspective. Based on this, vectorized model is constructed to express the situation for the target ship in open waters. Through analysis of bounded rational dynamic game between ships, the navigation situation of the target ship can be predicted. Historical data of Automatic Identification System (AIS) both from public data set and experiments is used to verify the prediction method. The results show that the research can provide theoretical basis for studies on multi-ship encounter situation, and supply a reference for navigation decisions of unmanned ships.

Spatial cognition training rapidly induces cortical plasticity in blind navigation: Transfer of training effect & Granger causal connectivity analysis.

How is the cortical navigation network reorganized by the Likova Cognitive-Kinesthetic Navigation Training? We measured Granger-causal connectivity of the frontal-hippocampal-insular-retrosplenial-V1 network of cortical areas before and after this one-week training in the blind. Primarily top-down influences were seen during two tasks of drawing-from-memory (drawing complex maps and drawing the shortest path between designated map locations), with the dominant role being congruent influences from the egocentric insular to the allocentric spatial retrosplenial cortex and the amodal-spatial sketchpad of V1, with concomitant influences of the frontal cortex on these areas. After training, and during planning-from-memory of the best on-demand path, the hippocampus played a much stronger role, with the V1 sketchpad feeding information forward to the retrosplenial region. The inverse causal influences among these regions generally followed a recursive feedback model of the opposite pattern to a subset of congruent influences. Thus, this navigational network reorganized its pattern of causal influences with task demands and the navigation training, which produced marked enhancement of the navigational skills.

The Radiocommunication Support Decision System to Use in Distress Situations for Captains of Small Non-conventional Vessels Operating in the Caribbean Sea Area

The paper presents the concept of a Radio Communication System to support the decision of the captain of a small non-conventional vessel including a yacht sailing in the waters of the Caribbean Sea in a dangerous situation requiring a call for RCC assistance. This is important to enhance boating safety in the area, where, due to the reduced and non-overlapping accessible areas of the A1 and A2 communications zones, calling for RCC assistance using common VHF/MF radio equipment is limited. In the event of danger to life and property on a yacht, the ability to contact Marine Rescue Centers is a primary concern. This paper presents a model of an intelligent system for supporting the captain in making the right navigation decision, in a dangerous situation, based on the method of artificial neural networks ANN. The article presents an example of numerical simulation of operation of the discussed system of Radio Communication decision support, assuming a specific position of the small non-conventional vessel in distress, with reference to the existing communication areas A1, A2 in the Caribbean Sea. The result of the simulation is related to the calculation of the distance to the nearest zone, enabling the establishment of communication with the marine Rescue Center to distress call, the value of the new corrected course of the small non-conventional vessel and the time of arrival at the above-mentioned zone.

Mapping the Minimum Detectable Activities of Gamma-Ray Sources in a 3-D Scene

The ability to formulate maps of minimum detectable activities (MDAs) that describe the sensitivity of an ad hoc measurement that used one or more freely moving radiation detector systems would be significantly beneficial for the conduct and understanding of many radiological search activities. In a real-time scenario with a free-moving detector system, an MDA map can provide useful feedback to the operator about which areas have not been searched as thoroughly as others, thereby allowing the operator to prioritize future actions. Similarly, such a calculation could be used to inform subsequent navigation decisions of autonomous platforms. Here we describe a near real-time MDA mapping approach that can be applied when searching for point sources using detected events in a spectral region of interest (ROI) while assuming a constant, unknown background rate. We show the application of this MDA mapping method to a real scenario, a survey of the interior of a small building using a handheld detector system. Repeated measurements with no sources and with 137Cs sources of different strengths yield results consistent with the estimated thresholds and MDA values; namely, that for background-only measurements no sources are seen above threshold anywhere in the scene, while when sources are present they are detected above the thresholds calculated for their locations.

Collision Risk Index Calculation Based on an Improved Ship Domain Model

The traditional ship collision risk index model based on the distance at the closest point of approach (DCPA) and the time to the closest point of approach (TCPA) is insufficient for estimating ship collision risk and planning collision avoidance operations. This paper constructs an elliptical, dynamic ship domain that changes with ship speed and maneuverability parameters to overcome subjective human factors. Based on the constructed domain model, the concept of the ship domain proximity factor is introduced to improve the ship collision risk model based on DCPA and TCPA, and a risk calculation function model that considers the safety of ship navigation is constructed. The numerical calculation of the improved collision risk index calculation model confirms that the enhanced model has a higher rate of identification of risk between ships. The model is more compatible with the requirements of ship navigation decision-making and can provide theoretical support and a technical basis for research on ship collision avoidance decision-making.

Decentralized Policy Coordination in Mobile Sensing with Consensual Communication.

In a typical mobile-sensing scenario, multiple autonomous vehicles cooperatively navigate to maximize the spatial-temporal coverage of the environment. However, as each vehicle can only make decentralized navigation decisions based on limited local observations, it is still a critical challenge to coordinate the vehicles for cooperation in an open, dynamic environment. In this paper, we propose a novel framework that incorporates consensual communication in multi-agent reinforcement learning for cooperative mobile sensing. At each step, the vehicles first learn to communicate with each other, and then, based on the received messages from others, navigate. Through communication, the decentralized vehicles can share information to break through the dilemma of local observation. Moreover, we utilize mutual information as a regularizer to promote consensus among the vehicles. The mutual information can enforce positive correlation between the navigation policy and the communication message, and therefore implicitly coordinate the decentralized policies. The convergence of this regularized algorithm can be proved theoretically under certain mild assumptions. In the experiments, we show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly in the execution phase. The results validate that consensual communication plays very important role in coordinating the behaviors of decentralized vehicles.

Navigating the narrative: An eye‐tracking study of readers' strategies when Reading comic page layouts

AbstractIn multimedia stimuli (e.g., comics), the reader must follow a narrative in which text and image both contribute information, and artists may use more irregular layouts which must still be followed correctly. While previous work has found that the external structure (outlines) of panels is a major contributor to navigation decisions in comics, other studies have shown that panel content can affect reading order. The present studies use eye‐tracking to investigate these contributions further. In Experiment 1, the reading behaviors on six layout variations were compared. The influence of the external structure was replicated, but an effect of text location was also found for one layout type. Experiment 2 focused on variations of this particular layout, manipulating the location of text within critical panels. Panel content was a consistent effect for all variations. While most navigation decisions are made using the external structure, content becomes key when resolving ambiguous layouts.

Odour motion sensing enhances navigation of complex plumes.

Odour plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows1-4. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odour signal, including odour identity5, intensity6 and timing6-12. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including mechanosensory and visual cues9,12,13. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odour navigation as odour-elicited upwind motion6,8-12,14,15. Here we show that Drosophila melanogaster shape their navigational decisions using an additional directional cue-the direction of motion of odours-which they detect using temporal correlations in the odour signal between their two antennae. Using a high-resolution virtual-reality paradigm to deliver spatiotemporally complex fictive odours to freely walking flies, we demonstrate that such odour-direction sensing involves algorithms analogous to those in visual-direction sensing16. Combining simulations, theory and experiments, we show that odour motion contains valuable directional information that is absent from the airflow alone, and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odour-direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments.

Neuronal signature of spatial decision-making during navigation by freely moving rats by using calcium imaging

A challenge in spatial memory is understanding how place cell firing contributes to decision-making in navigation. A spatial recency task was created in which freely moving rats first became familiar with a spatial context over several days and thereafter were required to encode and then selectively recall one of three specific locations within it that was chosen to be rewarded that day. Calcium imaging was used to record from more than 1,000 cells in area CA1 of the hippocampus of five rats during the exploration, sample, and choice phases of the daily task. The key finding was that neural activity in the startbox rose steadily in the short period prior to entry to the arena and that this selective population cell firing was predictive of the daily changing goal on correct trials but not on trials in which the animals made errors. Single-cell and population activity measures converged on the idea that prospective coding of neural activity can be involved in navigational decision-making.

Simulation Tool for Winter Navigation Decision Support in the Baltic Sea

This article presents a novel simulation tool for the analysis of winter navigation operations in the Baltic Sea in the context of the Finnish–Swedish Winter Navigation System (FSWNS). The aim of the tool is to simulate the performance of the FSWNS under various potential future operating scenarios and thereby support decision making in matters affecting the operation and development of the FSWNS, for instance, in terms of icebreaking resources and ice class regulations. To this end, the tool considers key performance factors and characteristics of the FSWNS, such as the prevailing ice conditions, the ice-going capability and other technical characteristics of the relevant merchant vessels, the availability of icebreaking resources, and the features of specific icebreaking operations (e.g., convoys). The tool would allow testing of several “what-if” scenarios, answering questions related to optimal engine power for safe, efficient, and environmentally friendly navigation and the optimal scheduling of icebreakers for effective and cost-efficient assistance missions.

TRAVEL: Traversable Ground and Above-Ground Object Segmentation Using Graph Representation of 3D LiDAR Scans

Perception of traversable regions and objects of interest from a 3D point cloud is one of the critical tasks in autonomous navigation. A ground vehicle needs to look for traversable terrains that are explorable by wheels. Then, to make safe navigation decisions, the segmentation of objects positioned on those terrains has to be followed up. However, over-segmentation and under-segmentation can negatively influence such navigation decisions. To that end, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRAVEL</i> , which performs traversable ground detection and object clustering simultaneously using the graph representation of a 3D point cloud. To segment the traversable ground, a point cloud is encoded into a graph structure, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tri-grid field</i> , which treats each tri-grid as a node. Then, the traversable regions are searched and redefined by examining local convexity and concavity of edges that connect nodes. On the other hand, our above-ground object segmentation employs a graph structure by representing a group of horizontally neighboring 3D points in a spherical-projection space as a node and vertical/horizontal relationship between nodes as an edge. Fully leveraging the node-edge structure, the above-ground segmentation ensures real-time operation and mitigates over-segmentation. Through experiments using simulations, urban scenes, and our own datasets, we have demonstrated that our proposed traversable ground segmentation algorithm outperforms other state-of-the-art methods in terms of the conventional metrics and that our newly proposed evaluation metrics are meaningful for assessing the above-ground segmentation. We make the code and our own dataset available to public at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/url-kaist/TRAVEL</uri> .

Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and Application

The factors complicating the specification of requirements for artificial intelligence systems (AIS) and their verification for the AIS creation and modernization are analyzed. The harmonization of definitions and building of a hierarchy of AIS characteristics for regulation of the development of techniques and tools for standardization, as well as evaluation and provision of requirements during the creation and implementation of AIS, is extremely important. The study aims to develop and demonstrate the use of quality models for artificial intelligence (AI), AI platform (AIP), and AIS based on the definition and ordering of characteristics. The principles of AI quality model development and its sequence are substantiated. Approaches to formulating definitions of AIS characteristics, methods of representation of dependencies, and hierarchies of characteristics are given. The definitions and harmonization options of hierarchical relations between 46 characteristics of AI and AIP are suggested. The quality models of AI, AIP, and AIS presented in analytical, tabular, and graph forms, are described. The so-called basic models with reduced sets of the most important characteristics are presented. Examples of AIS quality models for UAV video navigation systems and decision support systems for diagnosing diseases are described.

Cognitive experience alters cortical involvement in goal-directed navigation

Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same navigation decision task, revealing past learning as a critical determinant of whether cortex is necessary for goal-directed navigation. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multiarea calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.

Shared and specialized coding across posterior cortical areas for dynamic navigation decisions

Animals adaptively integrate sensation, planning, and action to navigate toward goal locations in ever-changing environments, but the functional organization of cortex supporting these processes remains unclear. We characterized encoding in approximately 90,000 neurons across the mouse posterior cortex during a virtual navigation task with rule switching. The encoding of task and behavioral variables was highly distributed across cortical areas but differed in magnitude, resulting in three spatial gradients for visual cue, spatial position plus dynamics of choice formation, and locomotion, with peaks respectively in visual, retrosplenial, and parietal cortices. Surprisingly, the conjunctive encoding of these variables in single neurons was similar throughout the posterior cortex, creating high-dimensional representations in all areas instead of revealing computations specialized for each area. We propose that, for guiding navigation decisions, the posterior cortex operates in parallel rather than hierarchically, and collectively generates a state representation of the behavior and environment, with each area specialized in handling distinct information modalities.

Assessment of Ship-Overtaking Situation Based on Swarm Intelligence Improved KDE.

This paper proposes a data-driven risk assessment model for ship overtaking based on the particle swarm optimization (PSO) improved kernel density estimation (KDE). By minimizing the mean square error between the real probability distribution of the ship overtaking point and the kernel density estimation probability distribution calculated by the current kernel density bandwidth, the longitude and latitude of the ship overtaking point are displayed by the color corresponding to the probability as the cost objective function of the search bandwidth of the algorithm. This can better show the distribution of the overtaking points of channel propagation traffic flow. A probability-based ship-overtaking risk evaluation model is developed through the bandwidth and density analysis optimized by an intelligent algorithm. In order to speed up searching the optimal variable width of the kernel density estimator for ship encountering positions, an improved adaptive variable-width kernel density estimator is proposed. The latter reduces the risk of too smooth probability density estimation phenomenon. Its convergence is proved. Finally, the model can efficiently evaluate the risk status of ship overtaking and provide navigational auxiliary decision support for pilots.

BIM-based determination of indoor navigation sign layout using hybrid simulation and optimization

Indoor navigation is difficult in large and complex navigation decision points. Helping occupants navigate more easily in these spaces is one of the important means to improve satisfaction. The navigation sign is an effective and one of the most common ways to achieve this goal. This study aims to propose a novel building information modeling based framework for designing navigation sign layout that better spatially matches the navigation cue demanded by occupants and the navigation cue supplied by signs. The demand for and supply of navigation cues are obtained from the crowd simulation and mathematical modeling, respectively. A programming model is applied to maximize the overlap between them by tuning the location of signs, the orientation of signs, and the navigation texts displayed on signs. To validate the effectiveness of the framework, the entrance lobby of a large hospital is used for the case study. An occupant navigation simulation model and a virtual reality based experiment are used for evaluating the sign layouts before and after implementing the proposed framework. The framework is supported to be effective in reducing the difficulty in navigation and navigation time.

Flexible use of memory by food-caching birds.

Animals use memory-guided and memory-independent strategies to make navigational decisions. Disentangling the contribution of these strategies to navigation is critical for understanding how memory influences behavioral output. To address this issue, we studied spatial behaviors of the chickadee, a food-caching bird. Chickadees hide food in concealed, scattered locations and retrieve their caches later in time. We designed an apparatus that allows birds to cache and retrieve food at many sites while navigating in a laboratory arena. This apparatus enabled automated tracking of behavioral variables - including caches, retrievals, and investigations of different sites. We built probabilistic models to fit these behavioral data using a combination of mnemonic and non-mnemonic factors. We found that chickadees use some navigational strategies that are independent of cache memories, including opportunistic foraging and spatial biases. They combine these strategies with spatially precise memories of which sites contain caches and which sites they have previously checked. A single memory of site contents is used in a context-dependent manner: during caching chickadees avoid sites that contain food, while during retrieval they instead preferentially access occupied sites. Our approach is a powerful way to investigate navigational decisions in a natural behavior, including flexible contributions of memory to these decisions.