Navigation Tasks Research Articles

Optimal filtering of an information process depended on noninformative parameters described by Marcovian chain when radio signals are receiving

In radio navigation and radio location tasks tracking systems on radio signals parameters often operate when object acceleration has stepwise changing. If we synthesize various tracking systems using optimal filtering theory it is necessary to represent the estimated process as a multidimensional Marcovian process. But such representation for stepwise changed acceleration is a rough approximation that lead to estimates accuracy degradation. One of possible approach to such disambiguation is to describe acceleration as Marcovian chain with finite number of states. This approach is developed in the article. Objective. To synthesize an optimal filtering algorithm of information process depended on noninformative parameters described by Marcovian chain using the observation grouping method when receiving radio signals and to implement simulation for an illustrative task.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

Swarm Robotics Navigation Task: A Comparative Study of Reinforcement Learning and Particle Swarm Optimization Methodologies

Automatic design methods focus on generating the collective behavior of swarm robotic systems. These methods enable multiple robots to coordinate and execute complex tasks in their environments autonomously. This research paper investigated two prominent methodologies: particle swarm optimization (PSO) and reinforcement learning (RL). A new comparative study was conducted to analyze the performance of a group of mobile robots through extensive experimentation. The objective was to produce navigational collective behavior through unknown environments. These environments differ in complexity ranging from obstacle-free environments to cluttered ones. The core metrics of the comparison include the time efficiency of individual robots and the overall swarm, flexibility in pathfinding, and the ability to generalize solutions for new environments. The obtained results from the Webots simulator with Python controller suggested that RL excels in environments closely aligned with its training conditions. RL achieved a faster completion time and demonstrated superior coordination among individual robots. However, its performance dips when facing untrained scenarios necessitating computationally expensive retraining or structural complexities to enhance adaptability. Conversely, PSO showed commendable consistency in performance. Despite its slower pace, it exhibited robustness in various challenging settings without reconfiguration.

Semantic Environment Atlas for Object-Goal Navigation

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat Savva et al. (2019)scenarios show that our method not only achieves a success rate of 39.0%—an improvement of 12.4% over the current state-of-the-art—but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low.

Integration and competition between space and time in the hippocampus

Episodic memory is organized in both spatial and temporal contexts. The hippocampus is crucial for episodic memory and has been demonstrated to encode spatial and temporal information. However, how the representations of space and time interact in the hippocampal memory system is still unclear. Here, we recorded the activity of hippocampal CA1 neurons in mice in a variety of one-dimensional navigation tasks while systematically varying the speed of the animals. For all tasks, we found neurons simultaneously represented space and elapsed time. There was a negative correlation between the preferred space and lap duration, e.g., the preferred spatial position shifted more toward the origin when the lap duration became longer. A similar relationship between the preferred time and traveled distance was also observed. The results strongly suggest a competitive and integrated representation of space-time by single hippocampal neurons, which may provide the neural basis for spatiotemporal contexts.

Adaptive closed-loop modulation of cortical theta oscillations: Insights into the neural dynamics of navigational decision-making

Navigational decision-making tasks, such as spatial working memory (SWM), rely highly on information integration from several cortical and sub-cortical regions. Performance in SWM tasks is associated with theta rhythm, including low-frequency oscillations related to movement and memory. The interaction of the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC), reflected in theta synchrony, is essential in various steps of information processing during SWM. We used a closed-loop neurofeedback (CLNF) system to upregulate theta power in the mPFC and investigate its effects on circuit dynamics and behavior in animal models. Specifically, we hypothesized that enhancing the power of the theta rhythm in the mPFC might improve SWM performance. Animals were divided into three groups: closed-loop (CL), random-loop (RL), and OFF (without stimulation). We recorded local field potential (LFP) in the mPFC while electrical reward stimulation contingent on cortical theta activity was delivered to the lateral hypothalamus (LH), which is considered one of the central reward-associated regions. We also recorded LFP in the vHPC to evaluate the related subcortical neural changes. Results revealed a sustained increase in the theta power in both mPFC and vHPC for the CL group. Our analysis also revealed an increase in mPFC-vHPC synchronization in the theta range over the stimulation sessions in the CL group, as measured by coherence and cross-correlation in the theta frequency band. The reinforcement of this circuit improved spatial decision-making performance in the subsequent behavioral results. Our findings provide direct evidence of the relationship between specific theta upregulation and SWM performance and suggest that theta oscillations are integral to cognitive processes. Overall, this study highlights the potential of adaptive CLNF systems in investigating neural dynamics in various brain circuits.

Persistent Impact of Prior Experience on Spatial Learning.

Learning to solve a new problem involves identifying the operating rules, which can be accelerated if known rules generalize in the new context. We ask how prior experience affects learning a new rule that is distinct from known rules. We examined how rats learned a new spatial navigation task after having previously learned tasks with different navigation rules. The new task differed from the previous tasks in spatial layout and navigation rule. We found that experience history did not impact overall performance. However, by examining navigation choice sequences in the new task, we found experience-dependent differences in exploration patterns during early stages of learning, as well as differences in the types of errors made during stable performance. The differences were consistent with the animals adopting experience-dependent memory strategies to discover and implement the new rule. Our results indicate prior experience shapes the strategies for solving novel problems, and the impact of prior experience remains persistent.

Kinematic control in a four‐wheeled Mecanum mobile robot for trajectory tracking

AbstractThe challenges of the modern world require mobile robots with the ability to navigate in congested environments with high levels of manoeuvrability. Therefore, the Mecanum wheel may be viable for addressing this challenge. This paper presents the experimental results of a kinematic control strategy, which involves considering the dynamic model as a black box, with only the input and output signals being known. To do this, high‐level and low‐level controllers are formulated and explained. The high level aims to control the desired position of the mobile robot, which can be useful for navigation tasks. On the other hand, low‐level control involves nested controllers to regulate the speed of the mobile robot wheels. Both levels are related and computed through pure kinematics transformations with a dSPACE ds1103 card and MATLAB/Simulink software. In total, 120 experiments were conducted to determine the repeatability of the tests, using the combination of three widely explored control techniques in the literature: proportional‐integral‐derivative (PID), PID plus sliding modes, and PID plus quasi‐sliding modes. The experiments conducted are described in detail, and the results are analysed using statistical indices based on the RMS error and percentage improvement.

Behavioural interference at event boundaries reduces long-term memory performance in the virtual water maze task without affecting working memory performance

Narrative episodic memory of movie clips can be retroactively impaired by presenting unrelated stimuli coinciding with event boundaries. This effect has been linked with rapid hippocampal processes triggered by the offset of the event, that are alternatively related either to memory consolidation or with working memory processes. Here we tested whether this effect extended to spatial memory, the temporal specificity and extent of the interference, and its effect on working- vs long-term memory. In three computerized adaptations of the Morris Water Maze, participants learned the location of an invisible target over three trials each. A second spatial navigation task was presented either immediately after finding the target, after a 10-s delay, or no second task was presented (control condition). A recall session, in which participants indicated the learned target location with 10 ‘pin-drop’ trials for each condition, was performed after a 1-h or a 24-h break. Spatial memory was measured by the mean distance between pins and the true location. Results indicated that the immediate presentation of the second task led to worse memory performance, for both break durations, compared to the delayed condition. There was no difference in performance between the delayed presentation and the control condition. Despite this long-term memory effect, we found no difference in the rate of performance improvement during the learning session, indicating no effect of the second task on working memory. Our findings are in line with a rapid process, linked to the offset of an event, that is involved in the early stages of memory consolidation.

Learning automatic navigation control skills for miniature helical robots from human demonstrations

Magnetic micro-robotic technology holds immense potential for revolutionizing minimally invasive procedures, particularly in the realm of interventional medicine. The ability to effectively and precisely control micro-scale, magnetically actuated robots in real-world scenarios is important. Mastering this capability promises to elevate the precision and efficacy of medical interventions, thereby enhancing patient outcomes. Numerous methods were proposed in previous work and achieved significant progress. However, these efforts primarily focused on model-based strategies and control improvements, with little attention given to the manipulation of the surgeon. This paper presents an exploration of an imitation learning approach, leveraging extensive manual operation experiments, to replicate the task of robotic navigation in simulated vascular environments. The control strategies are directly acquired from experimental observations and encapsulated within a high-dimensional neural network, specifically a tailored variant of the Residual Network (ResNet). The robustness and effectiveness of our proposed methodology are validated through comprehensive experimentation. In automatic navigation trials, the average error spanned from 2.29 mm to 3.32 mm, leading to a mean trajectory deviation of approximately 2.92 mm. The average error rate is 31% lower than that observed in traditional model-based Proportional Integral Derivative (PID) controller (approximately 3.81 mm). In addition, the maximum error (4.87 mm) is 83% of that of the traditional method (5.85 mm). Our findings emphasize the viability and benefits of learning-based techniques in micro-robot control, paving the way for innovative control strategies in interventional surgeries applications.

Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions.

Reliable measurement selection mechanism-based tightly coupled inertial/bionic polarization integration with position correction

Tightly coupled SINS/BPNS (strapdown inertial navigation system/bionic polarization navigation system) integration has been proven to be a promising navigation tactic to substitute SINS/GNSS (global navigation satellite system) integration in GNSS-denied environments. However, the existing tightly coupled SINS/BPNS integrations lack SINS position correction and the reliable screening of BPNS measurements, leading to difficulty to complete navigation tasks. This paper presents an enhanced tightly coupled SINS/BPNS integration to correct both SINS position and attitude and conduct reliable measurement screening for BPNS. This method establishes a new measurement model by formulating the relationship between the SINS position error and angle of E-vector to effectively correct the SINS position. Based on the corrected SINS position, the solar position is calculated and further plugged in the measurement model, leading to improved accuracy. Further, based on the focal plane polarization camera, a mechanism of reliable measurement selection with two-level processing is developed and combined with the extended Kalman filter to improve the filtering robustness for tightly coupled SINS/BPNS integration. Results of simulations and experiments show that the proposed method not only can effectively correct SINS navigation information, but also can possess a strong robustness to exclude unreliable BPNS measurements, leading to enhanced navigation performance for tightly coupled SINS/BPNS integration.

SilverCycling: Exploring the Impact of Bike-Based Locomotion on Spatial Orientation for Older Adults in VR

Spatial orientation is essential for people to effectively navigate and interact with the environment in everyday life. With age-related cognitive decline, providing VR locomotion techniques with better spatial orientation performance for older adults becomes important. Such advancements not only make VR more accessible to older adults but also enable them to reap the potential health benefits of VR technology. Natural motion-based locomotion has been shown its effective in enhancing younger users' performance in VR navigation tasks that require spatial orientation. However, there is a lack of understanding regarding the impact of natural motion-based locomotion on spatial orientation for older adults in VR. To address this gap, we selected the SilverCycling system, a VR bike-based locomotion technique that we developed, as a representative of natural motion-based locomotion, guided by findings from our pilot study. We conducted a user study with 16 older adults to compare SilverCycling with the joystick-based controller. The findings suggest SilverCycling potential to significantly enhance spatial orientation in the open-road urban environment for older adults, offering a better user experience. Based on our findings, we identify key factors influencing spatial orientation and propose design recommendations to make VR locomotion more accessible and user-friendly for older adults.

How spatial-cue reliability affects navigational performance in young and older adults

ABSTRACT Navigational abilities decline with age, but the cognitive underpinnings of this cognitive decline remain partially understood. Navigation is guided by landmarks and self-motion cues, that we address when estimating our location. These sources of spatial information are often associated with noise and uncertainty, thus posing a challenge during navigation. To overcome this challenge, humans and other species rely on navigational cues according to their reliability: reliable cues are highly weighted and therefore strongly influence our spatial behavior, compared to less reliable ones. We hypothesize that older adults do not efficiently weigh spatial cues, and accordingly, the reliability levels of navigational cues may not modulate their spatial behavior, as with younger adults. To test this, younger and older adults performed a virtual navigational task, subject to modified reliability of landmarks and self-motion cues. The findings revealed that while increased reliability of spatial cues improved navigational performance across both age groups, older adults exhibited diminished sensitivity to changes in landmark reliability. The findings demonstrate a cognitive mechanism that could lead to impaired navigation abilities in older adults.

A Switched Approach for Smartphone-Based Pedestrian Navigation.

In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements from the accelerometers. This average bias is then utilized to denoise the accelerometer data when GNSS signals are unavailable. We test the effectiveness of denoising the acceleration measurements through the estimated average bias by a synthetic example. The effectiveness of the proposed approach is then validated through a real experiment which is conducted along a pre-planned 150 m path.

Research on Applications of Image Recognition in the Design of Autonomous Navigation Robots

This study evaluates traditional and deep learning-based image recognition technologies in autonomous navigation robots, detailing their strengths and limitations. Traditional image recognition techniques, which rely on predefined algorithms and pattern recognition, have proven efficient and stable for navigation in controlled environments. Conversely, deep learning approaches, notably through convolutional neural networks (CNNs), excel in dynamic and unpredictable settings by adapting more effectively to complex environmental interactions. The core challenges in this field include the need for real-time data processing, achieving consistent accuracy across diverse environments, and managing the computational demands of sophisticated algorithms. This research highlights the significant improvements deep learning techniques bring to autonomous navigation, particularly in terms of adaptability and robustness. The study also addresses the necessity for advancements in both technology domains to meet the evolving demands of autonomous robotics, emphasizing the ongoing need to enhance computational efficiency and environmental adaptability. The findings suggest a growing potential for future research to explore hybrid models that leverage the predictability of traditional methods with the flexibility of deep learning to optimize autonomous navigational tasks.

Updating local and global probabilities during maze navigation.

We examined the human ability to encode and utilize local and global uncertainty information during a navigational task. Participants were tasked with navigating a virtual maze in which wall locations were obscured. Local cues and a global direction provided guidance. The validities of the global and local cues were separately and jointly varied across the two experiments. The results demonstrated that participants effectively utilized both global and local cues for navigation with a stronger reliance on local cues and a heightened precision in estimating their reliability. Our findings suggest that the representation of uncertainty for proximate events can be dissociated from that of distal events. Furthermore, humans effectively integrate both forms of information when making decisions during navigation tasks. This research advances our understanding of uncertainty processing and its implications for decision making in complex environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Functional architecture of intracellular oscillations in hippocampal dendrites

Fast electrical signaling in dendrites is central to neural computations that support adaptive behaviors. Conventional techniques lack temporal and spatial resolution and the ability to track underlying membrane potential dynamics present across the complex three-dimensional dendritic arbor in vivo. Here, we perform fast two-photon imaging of dendritic and somatic membrane potential dynamics in single pyramidal cells in the CA1 region of the mouse hippocampus during awake behavior. We study the dynamics of subthreshold membrane potential and suprathreshold dendritic events throughout the dendritic arbor in vivo by combining voltage imaging with simultaneous local field potential recording, post hoc morphological reconstruction, and a spatial navigation task. We systematically quantify the modulation of local event rates by locomotion in distinct dendritic regions, report an advancing gradient of dendritic theta phase along the basal-tuft axis, and describe a predominant hyperpolarization of the dendritic arbor during sharp-wave ripples. Finally, we find that spatial tuning of dendritic representations dynamically reorganizes following place field formation. Our data reveal how the organization of electrical signaling in dendrites maps onto the anatomy of the dendritic tree across behavior, oscillatory network, and functional cell states.

Ontology for BIM-Based Robotic Navigation and Inspection Tasks

The availability of inspection robots in the construction and operation phases of buildings has led to expanding the scope of applications and increasing technological challenges. Furthermore, the building information modeling (BIM)-based approach for robotic inspection is expected to improve the inspection process as the BIM models contain accurate geometry and relevant information at different phases of the lifecycle of a building. Several studies have used BIM for navigation purposes. Also, some studies focused on developing a knowledge-based ontology to perform activities in a robotic environment (e.g., CRAM). However, the research in this area is still limited and fragmented, and there is a need to develop an integrated ontology to be used as a first step towards logic-based inspection. This paper aims to develop an ontology for BIM-based robotic navigation and inspection tasks (OBRNIT). This ontology can help system engineers involved in developing robotic inspection systems by identifying the different concepts and relationships between robotic inspection and navigation tasks based on BIM information. The developed ontology covers four main types of concepts: (1) robot concepts, (2) building concepts, (3) navigation task concepts, and (4) inspection task concepts. The ontology is developed using Protégé. The following steps are taken to reach the objectives: (1) the available literature is reviewed to identify the concepts, (2) the steps for developing OBRNIT are identified, (3) the basic components of the ontology are developed, and (4) the evaluation process is performed for the developed ontology. The semantic representation of OBRNIT was evaluated through a case study and a survey. The evaluation confirms that OBRNIT covers the domain’s concepts and relationships, and can be applied to develop robotic inspection systems. In a case study conducted in a building at Concordia University, OBRNIT was used to support an inspection robot in navigating to identify a ceiling leakage. Survey results from 33 experts indicate that 28.13% strongly agreed and 65.63% agreed on the usage of OBRNIT for the development of robotic navigation and inspection systems. This highlights its potential in enhancing inspection reliability and repeatability, addressing the complexity of interactions within the inspection environment, and supporting the development of more autonomous and efficient robotic inspection systems.

Examining the Use of DanMu for Crowdsourcing Control in Virtual Gatherings

The advent of web-based interactive technologies has opened up new possibilities for virtual gatherings in 3D environments. Live-streaming, in particular, has gained increasing attention due to its effectiveness in engaging a large number of users in collective online activities. With an emphasis on audience participation, live-streaming shares common characteristics of the outlook of the metaverse and is driving new waves of interaction in virtual gatherings, such as engaging users through crowdsourcing control. However, this type of social interaction has not been examined in the Asian context, and it lacks systematic investigation of user experience with different crowdsourcing control methods. In this paper, we present a novel crowdsourcing control method based on DanMu, the subtitle system of Bilibili, one of the most successful and prevalent live-streaming platforms in Asia. We organized virtual gatherings by live-streaming a Minecraft virtual campus and examined the use of DanMu for crowdsourcing control. Our first study investigated the influence of three crowdsourcing control methods (First Come First Served, Vote, and Super Command) on collective navigation task efficiency and user experience. These influences were further discussed with user activeness and group sizes in a follow-up study. The results showed that Super Command, a representative mode on top of the democratic voting mechanism, offers better user experiences and social richness in large groups. Participants also rated its usability higher in small groups. Besides, virtual gathering in small groups allows greater pragmatic quality, usability, and a sense of agency than in large groups. Our work provides design guidelines for developers and HCI practitioners to develop crowdsourcing control methods and improve novel virtual gathering experiences in virtual worlds and the future metaverse.