Dynamic 3D Environment Research Articles

Exploring the Potential of Augmented Reality (AR) and Virtual Reality (VR) in Developing Immersive Experiences for Education, Entertainment, and Training: Innovations, Applications, and Future Prospects

Augmented reality (AR) and virtual reality (VR) have become groundbreaking technologies that are revolutionizing the way we interact with digital environments. These innovations have great potential in education, entertainment, and teaching by providing a fantastic and interactive experience to bridge the gap between the real and virtual worlds. AR and VR in education create engaging learning environments that encourage deeper understanding through simulations and hands-on virtual experiments. The entertainment industry is leveraging technology to create immersive games, videos, and live events that increase user engagement. AR and VR educational tools also enable safe, effective, and realistic simulations, especially in high-risk fields such as medicine, aviation, and defense. This technology often relies on hardware such as headsets, audio sensors, and haptics, supported by advanced software that can create dynamic 3D environments. Challenges include high construction costs, accessibility, and ensuring long-term user comfort. However, ongoing advances in hardware miniaturization, cloud computing, and artificial intelligence continue to overcome these challenges and encourage the integration of AR and VR into real-world applications. This study explores current innovations, applications, and future prospects for AR and VR, and highlights their potential to transform industries and redefine human-machine interactions.

Evaluating Visual Perception of Object Motion in Dynamic Environments

Precisely understanding how objects move in 3D is essential for broad scenarios such as video editing, gaming, driving, and athletics. With screen-displayed computer graphics content, users only perceive limited cues to judge the object motion from the on-screen optical flow. Conventionally, visual perception is studied with stationary settings and singular objects. However, in practical applications, we---the observer---also move within complex scenes. Therefore, we must extract object motion from a combined optical flow displayed on screen, which can often lead to mis-estimations due to perceptual ambiguities. We measure and model observers' perceptual accuracy of object motions in dynamic 3D environments, a universal but under-investigated scenario in computer graphics applications. We design and employ a crowdsourcing-based psychophysical study, quantifying the relationships among patterns of scene dynamics and content, and the resulting perceptual judgments of object motion direction. The acquired psychophysical data underpins a model for generalized conditions. We then demonstrate the model's guidance ability to significantly enhance users' understanding of task object motion in gaming and animation design. With applications in measuring and compensating for object motion errors in video and rendering, we hope the research establishes a new frontier for understanding and mitigating perceptual errors caused by the gap between screen-displayed graphics and the physical world.

Rotating cell culture system-induced injectable self-assembled microtissues with epidermal stem cells for full-thickness skin repair.

Epidermal stem cells (EpSCs) are crucial for wound healing and tissue regeneration, and traditional culture methods often lead to their inactivation. It is urgent to increase the yield of high quality EpSCs. In this study, primary EpSCs were isolated and cultured in a serum-free, feeder-free culture system. EpSCs are then expanded in a dynamic 3D environment using a rotating cell culture system (RCCS) with biodegradable porous microcarriers (MC). Over a period of 14 days, the cells self-assembled into microtissues with superior cell proliferation compared to 3D static culture. Immunofluorescence and qPCR analyses consistently showed that the stemness of the 3D microtissues was preserved, especially the COL17A1 associated with anti-aging was highly expressed in RCCS induced microtissues. In vivo experiments demonstrated that the group treated with 3D microtissues loaded with EpSCs showed enhanced early wound healing, and the injectable 3D microtissues were more conducive to maintaining cell viability and differentiation potential. Our study provides valuable insights into the dynamic 3D culture of EpSCs and introduces an injectable therapy using 3D microtissues loaded with EpSCs, which provides a new and effective approach for cell delivery and offering a promising strategy for guiding the regeneration of full-thickness skin defects.

3D Voxel-based Path Planning for AVs in Dynamic Complex Environments

Abstract. With the rapid advancement of science and technology, autonomous driving technology has been widely applied in various fields such as the automotive industry, intelligent warehousing, and emergency rescue. Traditional 2D path planning has limitations in dealing with the complex and dynamic environments of the real world, making it difficult to meet the high precision and complex path planning requirements of autonomous vehicles. With the development of data acquisition technology, large-scale point cloud data has become easily accessible, which can better extract the three-dimensional complex of the environment. Nevertheless, the unstructured nature and vast amount of point cloud data make path planning on it extremely challenging. Voxel models can effectively compress point cloud data and provide neighboring topological structures. In view of this, we propose a voxel-based framework for the path planning of autonomous vehicles in dynamic and complex 3D environments, which involves converting point cloud data into voxels and extracting navigation spaces. Subsequently, deep reinforcement learning techniques are utilized to achieve obstacle avoidance and navigation on the voxel map. It is expected that this framework will contribute to the development of 3D path planning and enhance the utilization of point cloud data in the navigation field.

Adaptive Factor Fuzzy Controller for Keeping Multi-UAV Formation While Avoiding Dynamic Obstacles

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.

Dynamic 3D model for decoding archaeological complexity of funerary contexts. Phenomena of collapse and fragmentation in an Iron Age burial of Padua (Italy)

Archaeological settings are intrinsically dynamic, undergoing transformations over time that significantly impact the archaeological record. These changes result from both deliberate human interventions and natural degradation processes. Post-depositional phenomena often distort our understanding of ancient contexts, complicating the identification of the original arrangement and hindering the interpretation of archaeological findings. This issue is particularly pronounced in funerary contexts, such as burials, which are susceptible to various natural and human-induced alterations. This study shows the potential of analysing funerary contexts within a processual framework and reconstructing them in a dynamic 3D environment. By employing metrically and morphologically accurate 3D reconstructions, it becomes possible to simulate, isolate, and analyze post-depositional phenomena. The precision of 3D simulation increases significantly when considering factors such as gravity. The goal of this study is to assess changes resulting from transformative phenomena, with a specific focus on creating a sequential representation that elucidates the burial's transformation processes, spanning from deposition to excavation phases.

Robust Radar Inertial Odometry in Dynamic 3D Environments

Millimeter-Wave Radar is one promising sensor to achieve robust perception against challenging observing conditions. In this paper, we propose a Radar Inertial Odometry (RIO) pipeline utilizing a long-range 4D millimeter-wave radar for autonomous vehicle navigation. Initially, we develop a perception frontend based on radar point cloud filtering and registration to estimate the relative transformations between frames reliably. Then an optimization-based backbone is formulated, which fuses IMU data, relative poses, and point cloud velocities from radar Doppler measurements. The proposed method is extensively tested in challenging on-road environments and in-the-air environments. The results indicate that the proposed RIO can provide a reliable localization function for mobile platforms, such as automotive vehicles and Unmanned Aerial Vehicles (UAVs), in various operation conditions.

VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments

Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders provide a limited environmental overview, and LiDAR is energy-intensive. To address these challenges, this paper proposes VizNav, a modular DRL-based framework for autonomous UAV navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay and Importance Sampling (PER) to improve performance in continuous action spaces and mitigate overestimations. Additionally, VizNav employs depth map images (DMIs) to enhance visual navigation by accurately estimating objects’ depth information, thereby improving obstacle avoidance. Empirical results show that VizNav, by leveraging TD3, improves navigation, and the inclusion of PER and DMI further boosts performance. Furthermore, the deployment of VizNav across various experimental settings confirms its flexibility and adaptability. The framework’s architecture separates the agent’s learning from the training process, facilitating integration with various DRL algorithms, simulation environments, and reward functions. This modularity creates a potential to influence RL simulation in various autonomous navigation systems, including robotics control and autonomous vehicles.

Enhancing Assault Maneuvers in Simulated Scenarios of Multiple Invader Kamikaze Drones through the Utilization of a Modified Adaptive Elforce Algorithm

The development of autonomous drone technology has led in their widespread deployment, especially in combat scenarios. One instance of this is the utilization of kamikaze drones, as seen in the Ukraine war. Autonomous defense drones have been used to counter these invading kamikaze drones. This study focuses on simulating scenarios involving invader vs. defender drones, primarily exploring invader drone maneuver motions to maximize damage inflicted on chosen targets. The work we conducted presents an enhanced el-force algorithm that employs Coulomb's Law-based maneuver techniques to improve the effectiveness of multiple kamikaze invader drones when engaging target defended by defender drones. We aim to improve traditional el-force by addressing key challenges such as siege tendencies and unproductive conduct. In addition, we explore various attacking formations to determine the most effective formation. To evaluate the performance of our proposed algorithm, we conducted simulation in a dynamic 3D environment, employing damage inflicted as the evaluation metric. Through rigorous testing, we conclusively demonstrate that our proposed method combining with a circular formation, outperforms alternative attacking maneuvers and formations. Our findings provide insights into optimal maneuver movements and attacking formations, improving the effectiveness of invader drones in engaging and damaging designated targets.

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments.

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.

Stealth Aircraft Penetration Trajectory Planning in 3D Complex Dynamic Environment Based on Sparse A* Algorithm

To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle rapidly to reduce the time of high radar cross-section (RCS) exposure to radar. However, in real flight, not only does the azimuth angle to the radar change rapidly but the elevation angle also changes rapidly, and the change in the radar cross-section is also significant in this process. Based on this premise, this paper established a trajectory planning method based on the sparse A* algorithm in a 3D complex, dynamic environment, called the 3D sparse A* method, based on a log-normal radar model (the 3D-SASLRM method), which considers the RCS statistical uncertainty and the statistical characteristics of the radar signals. Simulations were performed in both simple and complex scenarios. It was concluded that the established 3D-SASLRM method can significantly reduce the radar detection probability. And the essence of reducing under the assumption of 3D space is also to reduce the time of high radar cross-section exposure to radar.

Multi-Robot Cooperative Target Search Based on Distributed Reinforcement Learning Method in 3D Dynamic Environments

Multi-Robot Cooperative Target Search Based on Distributed Reinforcement Learning Method in 3D Dynamic Environments

NDT RC: Normal Distribution Transform Occupancy 3D Mapping With Recentering

The Normal Distribution Transform Occupancy Map (NDT OM) is a mapping algorithm able to represent a dynamic 3D environment. The resulting map has fixed boundaries, thus a robot with unbounded displacement might fall outside of the map due to memory limitation. In this paper, a recentering algorithm called NDT RC is proposed to avoid this issue. NDT RC extends the use of NDT OM for vehicles with unbounded displacements. NDT RC provides a seamless translation of the map as the robot gets far from the center of the previous map. The influence of NDT RC on the precision of the estimated trajectory of the robot, or odometry, is examined on two publicly available datasets, the KITTI and Ford datasets. An analysis of the sensitivity of the NDT RC to its tuning parameters is carried out using the Ford dataset, while the KITTI dataset is used to measure the influence of the density of the input point cloud. The results show that the proposed recentering strategy improves the accuracy of the odometry calculated by registering the latest lidar scan on the generated map compared to other NDT based approaches (NDT OM, NDT OM Fusion, SE-NDT). In particular, the proposed method, which does not perform loop closure, reduces the mean absolute translation error by 16% and the runtime by 88% compared to the NDT OM Fusion on the Ford dataset.

Effect of Mechanical Stimuli on the Phenotypic Plasticity of Induced Pluripotent Stem-Cell-Derived Vascular Smooth Muscle Cells in a 3D Hydrogel.

Introduction: Vascular smooth muscle cells (VSMCs) play a pivotal role in vascular homeostasis, with dysregulation leading to vascular complications. Human-induced pluripotent stem-cell (hiPSC)-derived VSMCs offer prospects for personalized disease modeling and regenerative strategies. Current research lacks comparative studies on the impact of three-dimensional (3D) substrate properties under cyclic strain on phenotypic adaptation in hiPSC-derived VSMCs. Here, we aim to investigate the impact of intrinsic substrate properties, such as the hydrogel's elastic modulus and cross-linking density in a 3D static and dynamic environment, on the phenotypical adaptation of human mural cells derived from hiPSC-derived organoids (ODMCs), compared to aortic VSMCs. Methods and results: ODMCs were cultured in two-dimensional (2D) conditions with synthetic or contractile differentiation medium or in 3D Gelatin Methacryloyl (GelMa) substrates with varying degrees of functionalization and percentages to modulate Young's modulus and cross-linking density. Cells in 3D substrates were exposed to cyclic, unidirectional strain. Phenotype characterization was conducted using specific markers through immunofluorescence and gene expression analysis. Under static 2D culture, ODMCs derived from hiPSCs exhibited a VSMC phenotype, expressing key mural markers, and demonstrated a level of phenotypic plasticity similar to primary human VSMCs. In static 3D culture, a substrate with a higher Young's modulus and cross-linking density promoted a contractile phenotype in ODMCs and VSMCs. Dynamic stimulation in the 3D substrate promoted a switch toward a contractile phenotype in both cell types. Conclusion: Our study demonstrates phenotypic plasticity of human ODMCs in response to 2D biological and 3D mechanical stimuli that equals that of primary human VSMCs. These findings may contribute to the advancement of tailored approaches for vascular disease modeling and regenerative strategies.

A Web-Based Platform for 3D Visualization of Multimodal Imaging Data in Cultural Heritage Asset Documentation

Complex demands in the field of cultural heritage preservation often require a multidisciplinary approach and substantial volumes of multimodal data integration and management. The conventional approach to tackling these issues revolves around using different H-BIM (historical building information model) solutions. This paper presents a prototype for a web platform that moves closer to the idea of a digital twin for physical cultural assets. Based on a light development framework, it is designed for online open access and features a versatile custom 3D viewer for intuitive interaction with the presented data. The concept requires a workflow similar to the video-game industry’s 3D asset optimization to generate highly detailed 3D models and to facilitate the display of multilayered imaging data. The technological stack features a minimal MVC architecture framework and front-end stylesheets. It is designed to be independent of specific databases, enhancing portability for potential future open-source releases. Moreover, the platform employs WebGL libraries to create a dynamic 3D environment interaction. The capabilities of the web platform were tested in a case study regarding the documentation of an important 17th-century church in Romania. Further developments and current limitations of the platform are also discussed.

A real‐time fuzzy motion planning system for unmanned aerial vehicles in dynamic 3D environments

This paper presents a new fuzzy potential system to plan the motion of Unmanned Aerial Vehicles (UAVs) in dynamic 3D Space. The system consists of two fuzzy subsystems representing the attractive model and the repulsive model of virtual forces in 3D. The attractive model will generate the attractive force required to pull the UAV in a smooth and optimized trajectory to land softly on a moving or stationary target. The repulsive model will generate the required repulsive force to avoid stationary or moving obstacles in 3D Space. The attractive fuzzy inference system takes the relative position and relative velocity between UAV and the target in the x, y, and z directions as inputs. It generates the required attractive force in the x, y, and z directions. The repulsive fuzzy inference system takes the relative position between UAV and obstacle in the xyz directions as input. Fuzzy associative memory (FAM) models the inputs and generates the required repulsive force in the x, y, and z directions. As a result, the UAV is considered to be moving under the influence of fuzzy virtual attractive and repulsive forces simultaneously. Accordingly, it will be able to change both its altitude and projected planner position concurrently and resolves the local minima problem if occurred. On the other hand, many classical models in dynamic environments require several additional inputs, such as the relative position and relative velocity, which increase the requirement on the measurement system to localize the moving objects in the 3D Space. Several experiments were performed and discussed to verify the robustness and effectiveness of the proposed motion planner with real-time implementations. The system performance was validated using three robotics platforms, two quadcopter drones, and one ground robot. The position and orientation of each robot were defined using a motion capture system with 6 opti-track cameras. The motion planning system produces a quadcopter drone's efficient and accurate low-frequency trajectory. The generated trajectory allows the drone to track the ground robot and avoid collision effectively with the second drone in the vicinity.

3D traversability analysis and path planning based on mechanical effort for UGVs in forest environments

Autonomous navigation in rough and dynamic 3D environments is a major challenge for modern robotics. This paper presents a novel traversability analysis and path planning technique that processes 3D point cloud maps to compute terrain gradient information and detect the presence of obstacles to generate efficient paths. These avoid unnecessary slope changes when more conservative paths are available, potentially promoting fuel economy, reducing the wear on the equipment and the associated risks. The proposed approach is shown to outperform existing techniques both in 3D simulation scenarios as well as in a real forest dataset, in which it also generates paths that are comparable to the ones drawn by humans with different backgrounds and expertise levels.

A Lightweight Robust Distance Estimation Method for Navigation Aiding in Unsupervised Environment Using Monocular Camera

This research addresses the challenges of visually impaired individuals’ independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, pillars) for near-front distance estimation. A Field of View (FOV) model calculates the camera’s angle and arbitrary pitch relative to the target Point of Interest (POI) within the image. Experimental results demonstrate the method’s proficiency in detecting distances between objects and the source camera, employing the FOV and Point of View (POV) principles. The approach achieves a mean absolute percentage error (MAPE) of 6.18% and 6.24% on YOLOv4-tiny and YOLOv4, respectively, within 10 m. The distance model only contributes a maximum error of 4% due to POV simplification, affected by target object characteristics, height, and selected POV. The proposed distance estimation method shows promise in drone racing navigation, EV autopilot, and aiding visually impaired individuals. It offers valuable insights into dynamic 3D environment distance estimation, advancing computer vision and autonomous systems.

Liposomal Doxorubicin Kinetic Study in an in vitro 2D and 3D Tumor Model for Osteosarcoma in a Perfusion Bioreactor.

In vivo drug screening in animal models is contrary to ethical values, costly and time-consuming. Traditional static in vitro models do not reflect the basic characteristics of bone tumor microenvironments; therefore, perfusion bioreactors, in particular, would be an applicable choice due to their advantages to regenerate versatile bone tumor models for studying in vitro novel drug delivery systems. In this study, an optimal drug formulation of liposomal doxorubicin was prepared, and the release kinetics of the drug and its toxicity effect on MG-63 bone cancer cell line were investigated in two-dimensional, static three-dimensional media on a PLGA/β-TCP scaffold and also in a dynamic media in a perfusion bioreactor. In this assay, the efficacy of the IC50 of this formulation which had been obtained in two-dimensional cell culture (=0.1 μg/ml), was studied in static and dynamic three-dimensional media after 3 and 7 days. Liposomes with good morphology and encapsulation efficiency of 95% had release kinetics of the Korsmeyer-Peppas model. The results of cell growth before treatment and cell viability after treatment in all three environments were compared. Cell growth in 2D was rapid, while it was slow in static 3D conditions. In the dynamic 3D environment, it was significant compared to the static tumor models. Cell viability after 3 and 7 days from treatment was 54.73% and 13.39% in 2D conditions, 72.27% and 26.78% in the static 3D model, while 100% and 78.92% in the dynamic culture indicating the effect of drug toxicity over time, but drug resistance of 3D models compared to 2D culture. In the bioreactor, the formulation used in the mentioned concentration showed very small cytotoxicity demonstrating the dominance of mechanical stimuli on cell growth over drug toxicity. Increasing drug resistance in 3D models compared to 2D models indicates the superiority of liposomal Dox over free form to reduce IC50 concentration.

Multi-UAV Cooperative Hunting in Cluttered Environments Considering Downwash Effects

This paper presents a novel solution to the three-dimensional (3D) cooperative hunting of multiple drones that deals with surrounding a target simultaneously while navigating around obstacles in the cluttered dynamic 3D environment. Meanwhile, drones avoid the airflow downwash force created by the spinning propellers on unmanned aerial vehicles (UAVs) and their effect on the other UAVs. This solution consists of a 3D Simultaneous Encirclement strategy, the cooperative hunting objective with a novel revised particle swarm optimization (PSO*) path planning algorithm, a flocking theory-inspired obstacle avoidance algorithm, and a cascade PI controller. Simulation results with varying conditions were carried out to validate the effectiveness of the proposed solution by successfully taking care of the downwash effects, and having multiple hunter UAVs hunt and encircle a moving or stationary target in a dynamic or static obstacle-rich cluttered environment.