Non-static Environments Research Articles

Enhancing the MCL-SLAM algorithm to overcome the issue of illumination variation, non-static environment and kidnapping to present the NIK-SLAM

AbstractSimultaneous Localization and Mapping (SLAM) is a major challenge that has been comprehensively investigated by scholars. SLAM’s ability to support autonomous navigation helps the robot to move from one place to another without being controlled by humans. This achievement has attracted the attention of scholars towards focusing on this area. Over the decades, countless procedures have been developed with extraordinary accomplishments. However, some problems can degrade the efficiency of SLAM (Simultaneous Localization and Mapping) technique, and examples of such problems include environmental noises (light intensity and shadow), non-static environment, and kidnap robots. These problems create inconsistency in measurement that can lead to erroneous navigation for an unmanned vehicle. In minimizing the effect of these problems, a novel SLAM technique based on several re-modifications of MCL is presented in this paper. This innovative technique is known as NIK-SLAM. The NIK-SLAM is equipped with filters alongside some modifications of the original Monte-Carlo algorithm (MCL) to maximize its performance. The filters were employed to overcome the effect of shadow and light intensity while the re-modification of MCL is used to overcome the limitation of kidnapping and non-static objects. A publicly available dataset (TUM-RGBD) and a private dataset were used for evaluation. Matlab was used for simulation while assessments were based on quantitative and qualitative validation. The results demonstrated a better performance of the NIK-SLAM, attaining a lower trajectory error for most experiments when compared to the original MCL and other SLAM algorithms found in the literature, but at the expense of delayed processing time. Furthermore, this accomplishment supports pathway planning, and exploration into unstable environments while it decreases accident rates/human fatalities, and in the future study, the issue of delayed processing time will be addressed.

Comparative Evaluation, Challenges, and Diverse Applications of Multi-Armed Bandit Algorithms

This manuscript offers an exhaustive comparative study of key Multi-Armed Bandits (MAB) algorithms, exploring their challenges, potential resolutions, and varied applications in today's context. It concentrates on at least three principal algorithms: the Upper Confidence Bound (UCB), Thompson Sampling, and ε-greedy. The analysis critically assesses their performance, focusing on convergence rate, precision, and computational demands. Key challenges in non-static environments and large-scale deployments are identified, including the complexities of multi-objective optimization. The paper proposes innovative solutions such as adaptive algorithmic approaches and the integration of parallel computing frameworks to address these challenges. It further delves into a range of application domains, from online advertising and recommendation systems to clinical trial methodologies, drawing comparisons between traditional and novel applications. The discussion also encompasses critical issues like data scarcity, cold start problems, ethical considerations in algorithm design, and the intricacies of processing real-time data. In its concluding sections, the study sheds light on recent successful deployments of MAB algorithms, identifying core factors contributing to their effectiveness and forecasting future developmental trajectories. This comprehensive analysis provides a detailed overview of the MAB field, highlighting its significance and practical impact in various sectors.

Ad Hoc Mesh Network Localization Using Ultra-Wideband for Mobile Robotics.

This article explores the implementation of high-accuracy GPS-denied ad hoc localization. Little research exists on ad hoc ultra-wideband-enabled localization systems with mobile and stationary nodes. This work aims to demonstrate the localization of bicycle-modeled robots in a non-static environment through a mesh network of mobile, stationary robots, and ultra-wideband sensors. The non-static environment adds a layer of complexity when actors can enter and exit the node's field of view. The method starts with an initial localization step where each unmanned ground vehicle (UGV) uses the surrounding, available anchors to derive an initial local or, if possible, global position estimate. The initial localization uses a simplified implementation of the iterative multi-iteration ad hoc localization system (AHLos). This estimate was refined using an unscented Kalman filter (UKF) following a constant turn rate and velocity magnitude model (CTRV). The UKF then fuses the robot's odometry and the range measurements from the Decawave ultra-wideband receivers stationed on the network nodes. Through this position estimation stage, the robot broadcasts its estimated position to its neighbors to help the others further improve their localization estimates and localize themselves. This wave-like cycle of nodes helping to localize each other allows the network to act as a mobile ad hoc localization network.

Localization Exploiting Semantic and Metric Information in Non-static Indoor Environments

Mobile robot localization is an important task in navigation and can be challenging, especially in non-static environments as the scene naturally involves movable objects and appearance changes. In this paper, we address the problem of estimating the robot’s pose in non-static environments containing movable objects. We understand as non-static environments, dynamic environments in which objects might be moved or changed their appearance. We propose a probabilistic localization approach that combines metric and semantic information and takes into account both, static and movable objects. We perform a pixel-wise association of depth and semantic data from an RGB-D sensor with a semantically-augmented truncated signed distance field (TSDF) in order to estimate the robot’s pose. The combination of metric and semantic information increases the robustness w.r.t. movable objects and object appearance changes. The experiments conducted in a real indoor environment and a publicly-available dataset suggest that our approach successfully estimates robot pose in non-static environments and they show an improvement compared to robot localization based only on metric or semantic information and compared to a feature-based method.

Hypothesis selection with Monte Carlo tree search for feature-based simultaneous localization and mapping in non-static environments

A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not a valid assumption. This paper studies a scenario where landmarks can occupy multiple discrete positions at different points in time, where each possible position is added to a multi-hypothesis map representation. A selector-mixture distribution is introduced and used in the observation model. Each landmark position hypothesis is associated with one component in the mixture. The landmark movements are modeled by a discrete Markov chain and the Monte Carlo tree search algorithm is suggested to be used as component selector. The non-static environment model is further incorporated into the factor graph formulation of the SLAM problem and is solved by iterating between estimating discrete variables with a component selector and optimizing continuous variables with an efficient state-of-the-art nonlinear least squares SLAM solver. The proposed non-static SLAM system is validated in numerical simulation and with a publicly available dataset by showing that a non-static environment can successfully be navigated.

Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

A static world assumption is often used when considering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneous localization and mapping</i> (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not valid. This paper studies a scenario where uniquely identifiable landmarks can attend multiple discrete positions, not known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . Based on a feature based multi-hypothesis map representation, a multi-hypothesis SLAM algorithm is developed inspired by target tracking theory. The creation of such a map is merged into the SLAM framework allowing any available SLAM method to solve the underlying mapping and localization problem for each hypothesis. A recursively updated hypothesis score allows for hypothesis rejection and prevents exponential growth in the number of hypotheses. The developed method is evaluated in an underground mine application, where physical barriers can be moved in between multiple distinct positions. Simulations are conducted in this environment showing the benefits of the multi-hypothesis approach compared to executing a standard SLAM algorithm. Practical considerations as well as suitable approximations are elaborated upon and experiments on real data further validates the simulated results and show that the multi-hypothesis approach has similar performance in reality as in simulation.

Dynamic and marketing capabilities as determinants of firm performance: evidence from automotive industry

Purpose This study aims to explore the roles and relations of dynamic capabilities (DCs) and marketing capabilities (MCs) to generate firm performance through new empirical data from the automotive industry in an emerging market, Turkey, where volatile market conditions may compel firms to use both their DCs and MCs. The automotive industry dynamic character, which is shaped by fierce competition among car manufacturers, fluctuating customer demands and strong effect of environmental forces, provides an ideal context for examining the performance outcomes of MC and DC in non-static environments. This study aims to show whether better financial performance can be achieved through an integrated MC and DC framework; if the level of environmental dynamism (ED) influences the utilization of MCs; and the impact of MC and DC convergence on firm performance by using emerging market data, which is rare in the extant literature. Design/methodology/approach This paper empirically investigates the role of MCs on the relationship between DCs and firm performance and the effect of ED in marketing capability development through a study of 162 top level managers from the automotive industry in Turkey using multiple regression methods. A self-administered questionnaire was used to collect data. A maximum concern was given to obtain at least three questionnaires from each firm to minimize the risk of getting biased answers from only one manager. Findings The data were analyzed by the regression method, and the mediation and moderation tests were conducted to test the established hypotheses. The direct relationship between MCs and firm performance was examined through linear regression, and a significant relationship was found (ß = 0.408; t = 5.656; p &lt; 0.001). Pricing (ß = 0.404; p &lt; 0.001), marketing research (ß = 0.367; p &lt; 0.001) and marketing strategy and implementation (ß = 0.336; p &lt; 0.001) had the strongest association with firm performance. The mediating role of MC on the relationship between DC and firm performance was assessed, and the analysis result yielded a significant result (ß = 0.439; t = 6.174; p &lt; 0.001). Finally, the moderating effect of ED on the direct relationship between MC and firm performance was assessed. Yet, the interaction term was insignificant (ß = 0.013; t = 0.103; p = 0.918) in predicting firm performance. Research limitations/implications Although the data set covers a broad range of firms operating in the Turkish automotive industry, the generalization of findings should only be possible through obtaining fresh evidence from other emerging markets that possess the similar market characteristics of Turkey. The cross-sectional nature of the study may offer insights only for a certain period of time; thus, additional longitudinal studies are recommended to see the dynamic changes on the constructs and relationships between them. Future studies may also include qualitative methods, i.e. interviews with top managers to have a deeper understanding on how DC–MC interaction creates better performance. Practical implications This study empirically shows the importance of MCs for firm performance; thus, managers should allocate significant efforts and resources for improving MCs. The demand for the electric and even autonomous vehicles is likely to increase in the following years, and this new era in the automotive industry requires more R&amp;D and innovation-based products, i.e. green vehicles with low carbon footprint, the use of robotics and long-life batteries for electric vehicles. The cost-related pricing may no longer be a competitive advantage for the firms in emerging markets such as Turkish automotive industry; thus, more investment for disruptive technologies should be considered. Originality/value The results show that MCs of firms mediated the relationship between DCs and economic performance. Yet, ED did not play a moderating role on the relationships between MCs and performance. It is concluded that DCs were associated with improved firm performance via MCs. Furthermore, the insignificant impact of ED on the development of MCs leading to better performance was explained by firms’ given over-performing efforts in the context of emerging markets.

Study of the process of deflagration flame behavior in disturbance conditions

To evaluate the effect of the disturbance airflow on the methane deflagration flame, a deflagration experiment is conducted in a closed duct. The effects of three disturbance airflow levels and two disturbance positions on the deflagration flame dynamics, including the variation of flame propagation behavior, disturbance mechanism, and deflagration overpressure development are investigated. The experimental results show that the vertical disturbance of the airflow disrupts the original structure of the deflagration flame, increasing the inherent instability and turbulence of the flame. The increase in the initial pressure of the disturbance airflow can increase the flame turbulence intensity, propagation velocity, the maximum value of the deflagration overpressure and the rate of overpressure rise. The contribution of the disturbance airflow at the initial acceleration position of the flame to the characteristic parameters of deflagration is more prominent. The experimental results complement and explain the propagation behavior of deflagration flame in a non-static environment. It provides suggestions on the prevention and protection of combustible gas deflagration risks in complex environments.

A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation

Time-of-flight (ToF) measurement technology based on the amplitude-modulated continuous-wave (AMCW) model has emerged as a state-of-the-art distance-measurement method for various engineering applications. However, many of the ToF cameras employing the AMCW process phase demodulation sequentially, which requires time latency for a single distance measurement. This can result in significant distance errors, especially in non-static environments (e.g., robots and vehicles) such as those containing objects moving relatively to the sensors. To reduce the measurement time required for a distance measurement, this paper proposes a novel, parallel-phase demodulation method. The proposed method processes phase demodulation of signal in parallel rather than sequentially. Based on the parallel phase demodulation, 2π ambiguity problem is also solved in this work by adopting dual frequency modulation to increase the maximum range while maintaining the accuracy. The performance of proposed method was verified through distance measurements under various conditions. The improved distance measurement accuracy was demonstrated throughout an extended measurement range (1–10 m).

Auditory streaming and bistability paradigm extended to a dynamic environment

We explore stream segregation with temporally modulated acoustic features using behavioral experiments and modelling. The auditory streaming paradigm in which alternating high- A and low-frequency tones B appear in a repeating ABA-pattern, has been shown to be perceptually bistable for extended presentations (order of minutes). For a fixed, repeating stimulus, perception spontaneously changes (switches) at random times, every 2–15 s, between an integrated interpretation with a galloping rhythm and segregated streams. Streaming in a natural auditory environment requires segregation of auditory objects with features that evolve over time. With the relatively idealized ABA-triplet paradigm, we explore perceptual switching in a non-static environment by considering slowly and periodically varying stimulus features. Our previously published model captures the dynamics of auditory bistability and predicts here how perceptual switches are entrained, tightly locked to the rising and falling phase of modulation. In psychoacoustic experiments we find that entrainment depends on both the period of modulation and the intrinsic switch characteristics of individual listeners. The extended auditory streaming paradigm with slowly modulated stimulus features presented here will be of significant interest for future imaging and neurophysiology experiments by reducing the need for subjective perceptual reports of ongoing perception.

Impact of diabetic neuropathy severity on foot clearance complexity and variability during walking

BackgroundThe control of foot trajectory during swing phase is important to achieve safe clearance with the ground. Complexity of a physiological control system arises from the interaction of structural units and regulatory feedback loops that operate to enable the organism to adapt to a non-static environment. Diabetic polyneuropathy (DPN) impairs peripheral feedback inputs and alters ankle control during gait, which might affect toe clearance (ToC) parameters and its complexity, predisposing DPN-subjects to tripping and falling. Research questionHow do different DPN-severity degrees change ToC trajectory and minimum ToC, and its complexity during gait of diabetic subjects? Methods15 healthy controls and 69 diabetic subjects were assessed and classified into DPN-severity degrees by an expert fuzzy model: absent (n = 26), mild (n = 21) and severe (n = 22). Three-dimensional kinematics was measured during comfortable walking. ToC was the minimum vertical distance between the marker placed at the first metatarsal head and the ground during swing. Mean ToC, ToC standard deviation (SD) between trials, and sample entropy (SaEn) and standard deviation (SD) of ToC trajectory were calculated from the ToC temporal series. ANOVA and ANCOVA (with the walking speed as the covariate) and Bonferroni pairwise post-hoc tests (P < 0.05) were used to compare groups. ResultsMean ToC and ToC SD did not show differences between groups (ANCOVA F = 0.436; df = 3; P = 0.705; F=1.719; df=3; P=0.170, respectively). ToC trajectory SD also did not show differences between groups (ANCOVA F = 3.98; df = 3; P = 0.755). Severe-DPN subjects showed higher ToC_Traj_SaEn than controls (ANCOVA F=2.60; df=3; P = 0.05). SignificanceSevere-DPN subjects showed a more complex pattern of overall foot-ankle trajectory in swing phase in comparison to controls, although did not present lower minimum ToC values. The higher complexity of ToC might lead to an increase in the motor system output (more strategies, increase in variability), resulting in a more unstable system and selected motor strategies.

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

Facial recognition is now a days is very emerging topic. The most challenging task for normal human being is to follow the facial recognition retrieval model for correct match in the least running time. Especially while dealing with moving or non-static environment like live video, webcam recording, or accessing real-time video in which facial features are not clear as to take as input image. Comparison between two different approached has been presented in this paper, linear binary pattern Haar technique is compared by deep learning using neural networks, different images of different persons has been taken, deep learning approach is more accurate according to different angles the video taken or any distance the video captured either for moving or static objects either from Mobile Video Camera or CCTV Camera then LPBH approach.

Lighting‐invariant Adaptive Route Following Using Iterative Closest Point Matching

Topological/metric route following, also called teach and repeat (T&amp;R), enables long‐range autonomous navigation even without globally consistent localization. In the teach pass, the robot is driven manually and builds up a topological/metric map of the environment, a graph of metric submaps connected by relative transformations. For repeating the route autonomously, the map only needs to be locally consistent; errors on the global level due to localization drift are irrelevant. This renders T&amp;R ideal for applications in which a global positioning system may not be available, such as navigation through street canyons or forests in search and rescue, reconnaissance in underground structures, surveillance, or planetary exploration. We present a T&amp;R system based on iterative closest point matching (ICP) using data from a spinning three‐dimensional (3D) laser scanner. Our algorithm is highly accurate, robust to dynamic scenes and extreme changes in the environment, and independent of ambient lighting. It enables autonomous navigation along a taught path in both structured and unstructured environments, including highly 3D terrain. Furthermore, our system is able to detect obstacles and avoid them by adapting its path using a local motion planner. It enables autonomous route following in nonstatic environments, which is not possible with classical T&amp;R systems. We demonstrate our algorithm's performance in two long‐range driving experiments, one in a highly dynamic urban environment, the other in unstructured, rough, 3D terrain. In these experiments, our robot autonomously drove a distance of over 22 km in both day and night. We analyze the localization accuracy of our system and show that it is highly precise. Moreover, we compare our ICP‐based method to a state‐of‐the‐art stereo‐vision‐based technique and show that our approach has a greatly increased robustness to path deviations and is less dependent on environmental conditions.

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

Needle insertion is commonly performed in minimally invasive medical procedures such as biopsy and radiation cancer treatment. During such procedures, accurate needle tip placement is critical for correct diagnosis or successful treatment. Accurate placement of the needle tip inside tissue is challenging, especially when the target moves and anatomical obstacles must be avoided. We develop a needle steering system capable of autonomously and accurately guiding a steerable needle using two-dimensional (2D) ultrasound images. The needle is steered to a moving target while avoiding moving obstacles in a three-dimensional (3D) non-static environment. Using a 2D ultrasound imaging device, our system accurately tracks the needle tip motion in 3D space in order to estimate the tip pose. The needle tip pose is used by a rapidly exploring random tree-based motion planner to compute a feasible needle path to the target. The motion planner is sufficiently fast such that replanning can be performed repeatedly in a closed-loop manner. This enables the system to correct for perturbations in needle motion, and movement in obstacle and target locations. Our needle steering experiments in a soft-tissue phantom achieves maximum targeting errors of 0.86 ± 0.35 mm (without obstacles) and 2.16 ± 0.88 mm (with a moving obstacle).

Mise en page, mise en écran

This article surveys aspects of medieval ‘publishing’ practice in manuscript format and relates them to, as well as draws comparisons with, the fast-moving culture of the written word in the digital age. By means of an analysis of three key trends—‘navigational aids/hypertexts’, ‘non-sequential reading’, and ‘interactive reading’—the article demonstrates how medieval and digital reading cultures share considerable similarities, all of which are linked to a reader’s ability to exercise power over the texts they read. The analysis explores why medieval readers seem to have engaged more readily with interactive reading experiences than their digital counterparts. It investigates the extent of a manuscript reader’s influence on both texts and later readers and considers whether the digital age has as yet managed to replicate successfully this kind of non-static reading environment. In sum, the article traces analogous trends in reading in the two periods and makes tangible suggestions as to how its findings can be used to inform the publishing trade about the needs of the digital reader.

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

Real Time Distributed Content Rendering Technique based on Agent-mediator Communication Framework for Multi-display Systems

Mixed reality (MR) systems provide an immersive virtual environment where the gap between real and virtual world is minimal. Current MR applications use fixed display to create this environment. However, the environment is never fixed in real world and it is difficult to achieve this non-static environment using traditional MR frameworks. Therefore, in this paper, an efficient multi-display MR system is proposed. The proposed system uses moveable displays to create dynamically changing immersive virtual environment. An agent-mediator based framework is used for communication in which a mediator is connected to several agents. Mediator administers the position of display and synchronizes the view across all agents. Agents gather interaction data from user which is passed to mediator. Moreover, distributed graphic contents rendering technique is used to efficiently render complex graphical contents. In this technique, mediator divides the contents and transmits these to each agent. This division of contents optimises the rendering process on agents, thus increasing the efficiency of the MR system.

Environmentally robust time reversal processing based on multiple constraint method

Time-reversal processing (TRP) uses the signals from a probe source to refocus the signal at the probe source location by back-propagating the time-reversed version of the received signal. However, the performance of TRP can be degraded by the mismatches due to the non-static environment where propagation condition changes during the time between receiving a probe signal and back-propagating the time reversed signal. In this presentation, two methods for the environmentally robust TRP based on the multiple signal vectors with singular value decomposition and minimum variance with multiple constraint method has been numerically simulated and compared under the condition of mismatches. Multiple sound speed profile, probe source location and tilted array are used to make the TRP robust. The results show that the focal point using robust TRP is well maintained when compared to the conventional TRP. Both methods are efficient for the robustness of time varying environment.

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.

Robust mobile robot localization in highly non-static environments

In this paper, we propose a robust pose tracking method for mobile robot localization with an incomplete map in a highly non-static environment. This algorithm will work with a simple map that does not include complete information about the non-static environment. With only an initial incomplete map, a mobile robot cannot estimate its pose because of the inconsistency between the real observations from the environment and the predicted observations on the incomplete map. The proposed localization algorithm uses the approach of sampling from a non-corrupted window, which allows the mobile robot to estimate its pose more robustly in a non-static environment even when subjected to severe corruption of observations. The algorithm sequence involves identifying the corruption by comparing the real observations with the corresponding predicted observations of all particles, sampling particles from a non-corrupted window that consists of multiple non-corrupted sets, and filtering sensor measurements to provide weights to particles in the corrupted sets. After localization, the estimated path may still contain some errors due to long-term corruption. These errors can be corrected using nonlinear constrained least-squares optimization. The incomplete map is then updated using both the corrected path and the stored sensor information. The performance of the proposed algorithm was verified via simulations and experiments in various highly non-static environments. Our localization algorithm can increase the success rate of tracking its pose to more than 95% compared to estimates made without its use. After that, the initial incomplete map is updated based on the localization result.