3D Environment Research Articles

Eco-Evo-Devo in the Adaptive Evolution of Artificial Creatures Within a 3D Physical Environment

Eco-evo-devo is an interdisciplinary field integrating ecology, evolutionary biology, and developmental biology. Niche construction refers to the phenomenon where organisms alter selection pressures through ecological activities, and ecological inheritance is the consequence of niche construction, passed down through generations. Development encompasses the process by which an organism grows and changes its form and behavior throughout its lifetime. Despite emerging research on eco-evo-devo, the evolution of organisms involving complex physical structures remains underexplored. We aim to explore interactions between these components in the evolution of the adaptive morphology and behavior of artificial creatures. We constructed an evolutionary model where a population of rigid-bodied creatures must cross two valleys to reach a goal, using morphological development and object construction in a 3D physical environment. We found that the evolution of both lifetime development (LD) and niche construction (NC) showed complementary roles, with LD contributing to crossing one valley and NC the other. Regarding ecological inheritance (EI), we observed how NC adapts to the presence of inherited objects by placing fewer objects to construct structures. On the other hand, a large percentage of EI can cause obstacles to form, which hinders the population from moving towards the target and facilitates non-adaptive morphology, making the evolution unsuccessful.

A Reference Architecture for Virtual Human Integration in the Metaverse: Enhancing the Galleries, Libraries, Archives, and Museums (GLAM) Sector with AI-Driven Experiences

The digital transformation of the GLAM (galleries, libraries, archives, and museums) sector is a multifaceted process that must address a range of requirements and critical challenges, such as traceability, long-term preservation, and sustainability. The potential of disruptive technologies to revolutionize this sector is significant, offering new ways to overcome these challenges and align with the broader objectives of smart cities. While substantial research has been conducted on the digital transformation of the GLAM sector, many opportunities remain unexplored by various stakeholders. This paper contributes to the field by proposing a reference architecture rooted in a design-oriented approach for developing and implementing processes related to the digital transformation within the context of smart cities. To illustrate the practical application of the proposed architecture, a proof of concept was developed in the form of an immersive digital gallery, which features an AI-powered virtual storyteller. The gallery, built using the open, cross-platform, Unreal, incorporates digitized physical art and a virtual metahuman tutor who guides users through the gallery. This interactive 3D environment enables users to explore the space, engage with artworks, and interact with a virtual guide. The environment’s performance was evaluated using a keystroke-level model analysis. Key findings from a pilot study involving 10 users showed that, when directed by the AI storyteller, the average time to locate an artwork was 16.5 s, while the average time to ask a question was 15.8 s. By integrating such technologies, the GLAM sector can contribute to the cultural fabric of smart cities, fostering more sustainable, accessible, and interactive public spaces.

A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects

The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios. To maximize the attack effectiveness, large and dispersed attack camouflages are often employed, which makes the camouflages overly conspicuous and reduces their visual stealth. The core issue is how to use minimal and concentrated camouflage to maximize the attack effect. Addressing this, our research focuses on developing more subtle and efficient attack methods that can better evade detection in practical settings. Based on these principles, this paper proposes a local 3D attack method driven by a Maximum Aggregated Region Sparseness (MARS) strategy. In simpler terms, our approach strategically concentrates the attack modifications to specific areas to enhance effectiveness while maintaining stealth. To maximize the aggregation of attack-camouflaged regions, an aggregation regularization term is designed to constrain the mask aggregation matrix based on the face-adjacency relationships. To minimize the attack camouflage regions, a sparseness regularization is designed to make the mask weights tend toward a U-shaped distribution and limit extreme values. Additionally, neural rendering is used to obtain gradient-propagating multi-angle augmented data and suppress the model’s detection to locate universal critical decision regions from multiple angles. These technical strategies ensure that the adversarial modifications remain effective across different viewpoints and conditions. We test the attack effectiveness of different region selection strategies. On the CARLA dataset, the average attack efficiency of attacking the YOLOv3 and v5 series networks reaches 1.724, which represents an improvement of 0.986 (134%) compared to baseline methods. These results demonstrate a significant enhancement in attack performance, highlighting the potential risks to real-world object detection systems. The experimental results demonstrate that our attack method achieves both stealth and aggressiveness from different viewpoints. Furthermore, we explore the transferability of the decision regions. The results indicate that our method can be effectively combined with different texture optimization methods, with the average precision decreasing by 0.488 and 0.662 across different networks, which indicates a strong attack effectiveness.

Enhancing Autonomous Vehicle Navigation in Complex Environment With Semantic Proto‐Reinforcement Learning

ABSTRACTDespite great progress in autonomous vehicle (AV) navigation, the technical challenges within this space are still considerable when it comes to successful integration of AVs into complex real‐world environments. To tackle these challenges, this paper presents a new semantic proto‐reinforcement learning (SP‐RL) method for dynamic path planning and real‐time obstacle avoidance of an autonomous car that can be adapted to various weather conditions in localization‐deficient environments, while predicting the human intentions of different shapes on road. This approach seeks to improve navigation capability of AV in dynamic and unstructured environments, as well as to address real‐time detection and avoidance response for obstacles more promptly while being able to adapt its decision‐making system based on weather condition by using the semantic graph network (SGN) within segmentation process therefore enhanced version configured with prototype‐based reinforcement learning (PRL). This innovation is new competitive edge compared previous existing approaches. Dynamic SGN is used to segment challenging 3D and free space environments, so that the AV can comprehend highly unintuitive areas like parking lots, construction site conditionals, or off‐road scenarios. At the same time, PRL is used to help real‐time decision‐making so the AV can quickly and precisely respond unexpected obstacles or changing environments. This approach is confirmed to be effective by extensive testing in the CARLA simulation environment, showing substantial improvement of AV navigation capability. This work demonstrates an exciting step towards solving the most fundamental problems faced by autonomous vehicles and could help to ensure that future AV systems are safer, more robust, and adaptable than current ones. It is appliable for urban areas which represent high volumes of pedestrians and vehicles, industrial sites with changing conditions that may be unpredictable at times or the challenging off‐road areas in rural geographies where typical terrains are rough, uneven, rugged yet not well‐structured. The model is robust to diverse weather conditions that make AV operations more reliable and safer. The model was tested on root mean square error (RMSE), computational time, no crash, obstacles avoidance, and success rate obtaining overall 98%.

Immersive Haptic Technology to Support English Language Learning Based on Metacognitive Strategies

One of the most widely used strategies for learning support is the use of Information and Communication Technologies (ICTs), due to the variety of applications and benefits they provide in the educational field. This article describes the design and implementation of an immersive application supported by Senso gloves and 3D environments for learning English as a second language in Ecuador. The following steps should be considered for the app design: (1) the creation of a classroom with characteristics similar to a real classroom and different buttons to navigate through the scenarios; (2) the creation of a virtual environment where text, images, examples, and audio are added according to the grammatical topic; (3) the creation of a dynamic environment for assessment in which multiple choice questions are interacted with, followed by automatic grading with direct feedback. The results showed that the interaction between the physical and virtual environment through navigation tests with the glove in different 3D environments achieved a complete activation and navigation rate. Teachers showed a clear interest in using the application in their classes as an additional teaching tool to complement the English language teaching process, given that it can increase motivation and memorization in students, as it is an easy-to-use application, and the 3D environments designed are attractive, which would make classes more dynamic. In addition, the availability of the application at any place and time represents a support for the current academic community as it adapts to the needs of today’s world.

Investigating the delivery of PD-L1-targeted immunoliposomes in a dynamic cervical cancer-on-a-chip model.

The recent approval of pembrolizumab in recurrent or metastatic cervical cancer warrants further investigations into the usefulness of immunotherapies for more durable and less radical interventions. In this study, the targeting potential of anti-PD-L1-functionalized immunoliposomes was tested in a 3D in vitro cervical cancer-on-a-chip model. Immunolipsomes were synthesized and decorated externally with monovalent anti-PD-L1 Fab' fragments of commercially available atezolizumab. Cervical cancer cell lines with varying levels of PD-L1 expression were cultured as spheroids embedded in a collagen I matrix, and treated under flow of culture media. Flow cytometry and live-cell confocal imaging were used to measure the interactions and uptake of untargeted liposomes and immunoliposomes in this panel of cell lines. The immunoliposomes retained specific functionality regardless of protein corona formation in high serum environments. As such, spheroids expressing high levels of PD-L1 preferentially internalized immunoliposomes in a 3D environment with extracellular matrix present, while low PD-L1-expressing cell lines showed no preference for either formulation. Importantly, treatments performed in monolayer cultures (on plastic) showed no differences between immuno- and untargeted liposome uptake, including the way in which the endocytosed liposomes are trafficked subcellularly. This study demonstrates the importance of both active and passive accumulation strategies to achieve nanoparticle targeting. Immunoliposomes remain a promising platform for the development of targeted nanotherapies against cervical cancers. However, initial functional tests did not translate directly to biological performance and this should be kept in mind for future formulations. Furthermore, the in vitro model developed appeared useful for visualizing liposome uptake in a 3D, live tissue environment and represents a cost-effective and reproducible model for future studies.

Ligand-Induced Activation of Single-Atom Palladium Heterogeneous Catalysts for Cross-Coupling Reactions.

Single-atom heterogeneous catalysts (SACs) are potential, recoverable alternatives to soluble organometallic complexes for cross-coupling reactions in fine-chemical synthesis. When developing SACs for these applications, it is often expected that the need for ligands, which are essential for organometallic catalysts, can be bypassed. Contrary to that, ligands remain almost always required for palladium atoms stabilized on commonly used functionalized carbon and carbon nitride supports, as the catalysts otherwise show limited activity. Despite this, ligand optimization has received little attention, and their role in activating SACs is poorly understood. Here, we explore the impact of structurally diverse phosphine ligands on the performance of nitrogen-doped carbon supported single-atoms (Pd1@NC) in the Sonogashira-Hagihara (SH) cross-coupling reaction, using X-ray absorption spectroscopy and density functional theory simulations to rationalize the observed trends. Compared to the ligand-free SAC, SH activity is enhanced in almost all ligand-assisted systems, with reactivity varying by up to 8 orders of magnitude depending on the ligand choice. Distinct trends emerge based on the free ligand volume and ligand class. Unlike molecular systems, the electronic effects of phosphine ligands are less significant in SACs due to the modulating influence of the support. Instead, the performance of SAC-ligand systems is governed by a balance between the ligand deformation energy during coordination with metal centers, and their resulting accessibility to cross-coupling reagents. These findings offer key insights into optimizing Pd-SACs by leveraging phosphine ligands to activate metal centers and tailor the 3D environment.

An Open-source Python Tool for Traction Force Microscopy on Micropatterned Substrates.

Cell-generated forces play a critical role in driving and regulating complex biological processes, such as cell migration and division and cell and tissue morphogenesis in development and disease. Traction force microscopy (TFM) is an established technique developed in the field of mechanobiology used to quantify cellular forces exerted on soft substrates and internal mechanical tissue stresses. TFM measures cell-generated traction forces in 2D or 3D environments with varying mechanical and biochemical properties. This technique involves embedding fiducial markers in the substrate, imaging substrate deformations caused by the cells, and using mathematical models to infer forces. This protocol compiles procedures from various previously published studies and software packages and describes how to perform TFM on 2D micropatterned substrates. Although not the focus of this protocol, the methods and software packages shown here also allow to perform monolayer stress microscopy (MSM), a method to calculate internal mechanical stress within the cells by modeling them as a thin plate with linear and homogeneous material properties. TFM and MSM are non-invasive methods capable of yielding spatially and temporally resolved force and stress maps with high throughput. As such, they enable the generation of rich datasets, which can provide valuable insights into the roles of cell-generated forces in various physiological and pathological processes. Key features • TFM and MSM protocol for 2D micropatterned polyacrylamide substrates, from sample preparation over imaging to data analysis with provided code. • Sample preparation method is based on Tseng et al. [1]. • TFM analysis is done with Python custom code and is optimized for batch analysis of movies. • MSM analysis is done with pyTFM from Bauer et al. [2].

Enriching surgical theatre competence through computer-based simulation.

Radiographers support the multidisciplinary team by facilitating medical imaging within the operating theatre environment. This project aimed to enhance student readiness for clinical competency in operative theatre imaging by implementing an authentic C-arm simulator for students to use prior to attending clinical placement. This study followed a pre-post, quantitative study design. Medspace.VR© CA is a computer simulation software which allows users to produce radiographic images using a virtual 'C-arm' imaging scanner, within a 3D operating theatre environment. Educational workshops were followed by Likert-scale surveys to ascertain perspectives relating to student satisfaction, engagement, knowledge and skill enhancement, and reflections on behavioral changes following engagement with the simulator. The post-simulator survey demonstrated favourable scores were favourable in terms of student enjoyment (94% agreed or strongly agreed), skill development (94%), anatomical knowledge (63%), operation of the machine (94%) and its relevance to the radiographer's role (100%) and professional capabilities (87%). Most students found the program's instructions to be straightforward (81%). Questions relating to confidence also demonstrated favourable results, with students highlighting perceived greater confidence in their abilities (87%) and the theatre environment (61%). When asked whether the C-Arm simulator is just as effective as hands-on placement experiences, 50% of participants disagreed or strongly disagreed with this statement. The C-arm simulator enabled our students to engage in authentic, scenario-based cases within simulated settings, mirroring conditions encountered in clinical practice. Overall, the findings highlight positive levels of perceived development and satisfaction. Future studies should evaluate objective knowledge and skill development within the clinical setting. Simulator use in this context prior to clinical placements, has the potential to reduce anxiety, increase satisfaction and expedite skill acquisition.

Training primates to forage in virtual 3D environments.

Virtual environment software is increasingly being employed as a non-invasive method in primate cognition research. Familiar and novel stimuli can be presented in new ways, opening the door to studying aspects of cognition in captivity which previously may not have been feasible. Despite the increased complexity of visual input compared to more traditional computerised studies, several groups of captive primates have now been trained to navigate virtual three-dimensional environments. Here, we outline a method for training primates to use a computerised virtual foraging task presented on a touchscreen. We document how to tailor this method to groups facing different training challenges. We present data from three groups: touchscreen-experienced chimpanzees (Pan troglodytes), touchscreen-naïve orang-utans (Pongo abelii), and chimpanzees tested in a group setting. Subjects from all groups mastered basic navigation challenges with relative ease (some in as little as 16 days), setting them up for systematic studies of primate cognition within virtual environments. The training method we present is flexible, yet structured, and we encourage other researchers to adapt it to implement virtual environment research with more individuals and across more species.

The nuclear lamin network passively responds to both active or passive cell movement through confinements.

Physical models of cell motility rely mostly on cytoskeletal dynamical assembly. However, when cells move through the complex 3D environment of living tissues, they have to squeeze their nucleus that is stiffer than the rest of the cell. The lamin network, organised as a shell right underneath the nuclear membrane, contributes to the nuclear integrity and stiffness. Yet, its response during squeezed cell motility has never been fully characterised. As a result, up to now, the interpretations on the lamin response mechanism are mainly speculative. Here, we quantitatively map the lamin A/C distribution in both a microfluidic migration device and a microfluidic aspiration device. In the first case, the cell is actively involved in translocating the nucleus through the constriction, while in the second case, the cell behaves as a passive object that is pushed through the constriction by an external pressure. Using a quantitative description of the lamin shell response based on mass conservation arguments applied on the fluorescence signal of lamin, we show that in both cases of migration and aspiration, the response of the lamin network is passive. In this way, our results not only further elucidate the lamin response mechanism, but also allow to characterise that this deformation is passive even when the cell is actively migrating, thus paving the way to further investigate which active nuclear responses may occur when cells migrate in confinement.

An Efficient Bio-inspired Technique for Secure Path Planning of Unmanned Aerial Vehicles in a 3D Environment

An Efficient Bio-inspired Technique for Secure Path Planning of Unmanned Aerial Vehicles in a 3D Environment

Establishing a Three-Dimensional Coculture Module of Epithelial Cells Using Nanofibrous Membranes.

Technical hurdles in a culture of epithelial cells include dedifferentiation and loss of function. Biomimetic three-dimensional (3D) cell culture methods can enhance cell culture efficiency. This study introduces an advanced two-layered culture system intended to cultivate epithelial cells as tissue-like layers with the culture of fibroblasts within a 3D environment. Polyvinyl alcohol (PVA) and poly(ε-caprolactone) (PCL) nanofibrous membranes (NMs) were fabricated via electrospinning and utilized as a physiologically relevant extracellular matrix for the culture of epithelial cells and fibroblasts, respectively. In the upper insert wells, lung epithelial cells were cultivated on the PVA NM, and in the lower chambers, fibroblasts were cultured on the PCL NM. This configuration eliminates direct cell-cell contact and facilitates the examination of paracrine signaling mediated by soluble factors. Confocal microscopy was employed to analyze the distribution, growth pattern, and expression of intracellular proteins, including zona occludens in epithelial cells. Z-stacking techniques enabled detailed 3D reconstructions, providing precise insights into the integrity of tight junctions and spatial organization within the epithelial layer. Scanning electron microscopy (SEM) assessed the morphological characteristics of cell types on the nanofibrous membranes. SEM imaging revealed intricate cell surface structures and interactions with the nanofibers, offering a comprehensive perspective on cellular architecture and cell interaction with nanofibrous structure. The Cell Counting Kit-8 (CCK-8) assay is a simple method for measuring epithelial cell and fibroblast growth rates over time. It provides the proliferative behaviors and potential synergistic effects of coculturing these cells. These findings highlight the effectiveness of a simple insert co-culture system for simultaneous culture of fibroblasts and epithelial cells, which is crucial in various physiological and pharmacological contexts, including epithelial tissue regeneration, tumor microenvironment with endothelial, immune, and other stroma cells, toxicity assay, and drug activity test.

Optimizing Virtual Culinary Tours Using Character-based Interaction and Finite State Machine (FSM)

This research focuses on the development of a Virtual Tour application utilizing Finite State Machine (FSM) to enhance the interaction of a Non-Playable Character (NPC) as a tour guide in a 3D virtual environment. The primary issue addressed is the significant impact of the COVID-19 pandemic on the tourism industry, which led to travel restrictions and limited opportunities for visitors to explore tourist destinations physically. The aim of the study is to create a virtual tourism experience as an alternative solution, enabling users to explore historical sites like Pasar Lama Tangerang remotely through Google Cardboard VR. To achieve this, the NPC’s behavior is controlled using FSM, allowing the character to transition between states—idle, walking, and talking—based on user interactions. Data was collected through user testing with a Likert scale questionnaire, evaluating user satisfaction and the effectiveness of the FSM method. The results revealed a 74.35% positive user rating, categorized as Good, demonstrating the potential of FSM to provide an interactive, engaging, and educational virtual tour experience. These findings highlight the effectiveness of FSM in creating a dynamic and user-responsive virtual tour, offering significant benefits to the tourism sector by providing an innovative, accessible, and immersive way for potential visitors to explore destinations during travel restrictions. This research contributes to the growing field of e-tourism, showcasing the potential of virtual reality and FSM to transform the tourism industry in times of crisis.

BEV Semantic Map Reconstruction for Self-Driving Cars with the Multi-Head Attention Mechanism

Environmental perception is crucial for safe autonomous driving, enabling accurate analysis of the vehicle’s surroundings. While 3D LiDAR is traditionally used for 3D environment reconstruction, its high cost and complexity present challenges. In contrast, camera-based cross-view frameworks can offer a cost-effective alternative. Hence, this manuscript proposes a new cross-view model to extract mapping features from camera images and then transfer them to a Bird’s-Eye View (BEV) map. Particularly, a multi-head attention mechanism in the decoder architecture generates the final semantic map. Each camera learns embedding information corresponding to its position and angle within the BEV map. Cross-view attention fuses information from different perspectives to predict top-down map features enriched with spatial information. The multi-head attention mechanism then globally performs dependency matches, enhancing long-range information and capturing latent relationships between features. Transposed convolution replaces traditional upsampling methods, avoiding high similarities of local features and facilitating semantic segmentation inference of the BEV map. Finally, we conduct numerous simulation experiments to verify the performance of our cross-view model.

Impact of photobiomodulation on neural embryoid body formation from immortalized adipose-derived stem cells

BackgroundEmbryoid bodies (EBs) are three-dimensional (3D) multicellular cell aggregates that are derived from stem cell and play a pivotal role in regenerative medicine. They recapitulate many crucial aspects of the early stages of embryonic development and is the first step in the generation of various types of stem cells, including neuronal stem cells. Current methodologies for differentiating stem cells into neural embryoid bodies (NEBs) in vitro have advanced significantly, but they still have limitations which necessitate improvement. Photobiomodulation (PBM) a low powered light therapy is a non-invasive technique shown to promote stem cell proliferation and differentiation.MethodsThis in vitro study elucidated the effects of photobiomodulation (PBM) on the differentiation of immortalized adipose-derived stem cells (iADSCs) into NEBs within a 3D cell culture environment. The study utilized PBM at wavelengths of 825 nm, 525 nm, and a combination of both, with fluences of 5 and 10 J/cm2. Morphology, viability, metabolic activity, and differentiation following PBM treatment was analysed.ResultsThe results revealed that the effects of photobiomodulation (PBM) are dose dependent. PBM, at 825 nm with a fluence of 10 J/cm2, significantly enhanced the size of neural embryoid bodies (NEBs), improved cell viability and proliferation, and reduced lactate dehydrogenase (LDH) levels, indicating minimal cell damage. Interestingly, the stem cell marker CD 44 was upregulated at 5 J/cm2 in all treatment groups at 24 and 96 hpi, CD105 increased with 825 nm at 10 J/cm2 at 24 hpi, which may be attributed to a heterogeneous cell population within the NEBs. Pax6 expression showed transient activation. Nestin was upregulated at 825 nm with 10 J/cm2 at 96 hpi, suggesting a promotion of neural precursor populations. GFAP an intermediate filament protein was upregulated at 825 nm at 10 J/cm2 at both 24 and 96 hpi. SOX2, a pluripotency marker, was expressed at 5 J/cm2 across all wavelengths. Neu N a neuronal nuclei marker was expressed at 5 J/cm2 in all treatments at 24 hpi and over time the expression was observed in all treatment groups at 10 J/cm2.ConclusionIn conclusion, the application of PBM at 825 nm with a fluence of 10 J/cm2 during the differentiation of iADSCs into NEBs resulted in optimal differentiation. Notably, the neuronal marker Nestin was significantly upregulated, highlighting the potential of the PBM approach for enhancing neuronal differentiation its promising applications in regenerative medicine.Graphical

Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework

This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted for its ability to balance multiple objectives, including path length, smoothness, collision avoidance, and real-time responsiveness. Through experimental analysis, AMOPP demonstrates superior performance, with a 15% reduction in path length compared to A*, achieving an average path length of 450 m. Its angular deviation of 8.0° ensures smoother trajectories than traditional methods like Genetic Algorithm and Particle Swarm Optimization (PSO). Moreover, AMOPP achieves a 0% collision rate across all simulations, surpassing heuristic-based methods like Cuckoo Search and Bee Colony Optimization, which exhibit higher collision rates. Real-time responsiveness is another key strength of AMOPP, with an average re-planning time of 0.75 s, significantly outperforming A* and RRT*. The computational complexities of each algorithm are analyzed, with AMOPP exhibiting a time complexity of O(k·n) and a space complexity of O(n), ensuring scalability and efficiency for large-scale operations. The study also presents a comprehensive qualitative and quantitative comparison of 14 algorithms using 3D visualizations, highlighting their strengths, limitations, and suitable application scenarios. By integrating weighted optimization with penalty-based strategies and spline interpolation, AMOPP provides a robust solution for UAV path planning, particularly in scenarios requiring smooth navigation and adaptive re-planning. This work establishes AMOPP as a promising framework for real-time, efficient, and safe UAV operations in dynamic environments.

Map Representation and Navigation Planning for Legged Climbing UGVs in 3D Environments

Legged climbing unmanned ground vehicles (LC-UGVs) possess obstacle avoidance and wall transition capabilities, allowing them to move in 3D environments. Existing navigation methods for legged UGVs are only suitable for ground locomotion rather than 3D space. Although some wall transition methods have been proposed, they are specific to certain legged structures and have not been integrated into the navigation framework in full 3D environments. The planning of collision-free and accessible paths for legged climbing UGVs with any configuration in a 3D environment remains an open problem. This paper proposes a map representation suitable for the navigation planning of LC-UGVs in 3D space, named the Multi-Level Elevation Map (MLEM). Based on this map representation, we propose a universal hierarchical planning architecture. A global planner is applied to rapidly find cross-plane topological paths, and then a local planner and a motion generator based on motion primitives produces accessible paths and continuous motion trajectories. The hierarchical planning architecture equips the LC-UGVs with the ability to transition between different walls, thereby allowing them to navigate through challenging 3D environments.

A case study on the effect of aquaculture operations on the physiology and behaviour of Atlantic salmon (Salmo salar) during two heat events on a commercial farm

Aquaculture farms represent a complex 3D environment and face regular seasonal challenges such as acute and chronically elevated temperatures during summer. Further, fish are exposed to the interaction between their environment and farm operations, which can cause challenging conditions. In the context of modern net-pen aquaculture and ocean warming, there is therefore a need to understand the welfare of these commercially important species under the realistic conditions they encounter. Fish were tagged with two types of biologgers measuring temperature, heart rate, external acceleration, and depth of fish as they experienced standard aquaculture operations over two periods of thermal stress, one short-term and one long-term. The fish response during the thermal stress events was compared with the periods preceding and following both events, and an additional analysis was carried out to further explore the effects of feeding and farm operations. Fish displayed signs of both secondary and potentially tertiary stress in response to the short- and long-term heat event and both heart rate and acceleration increased in response to feeding but displayed a more nuanced response to operations. As part of the broader concept of precision fish farming, this research, based on data from 7 individual fish, represents a case study that presents the potential use of biologgers as tools for recognising early signs of stress by observing the secondary stress response, thereby demonstrating the potential for informed and timely stress identification to guide farm management decisions to enhance fish welfare and production efficiency in commercial aquaculture.

Genetic algorithm-based path planning of quadrotor UAVs on a 3D environment

Abstract In this article, a genetic algorithm (GA) is proposed as a solution for the path planning of unmanned aerial vehicles (UAVs) in 3D, both static and dynamic environments. In most cases, genetic algorithms are utilised for optimisation in offline applications; however, this work proposes an approach that performs real-time path planning with the capability to avoid dynamic obstacles. The proposed method is based on applying a genetic algorithm to find optimised trajectories in changing static and dynamic environments. The genetic algorithm considers genetic operators that are employed for path planning, along with high mutation criteria, the population of convergence, repopulation criteria and the incorporation of the destination point within the population. The effectiveness of this approach is validated through results obtained from both simulations and experiments, demonstrating that the genetic algorithm ensures efficient path planning and the ability to effectively avoid static and dynamic obstacles. A genetic algorithm for path planning of UAVs is proposed, achieving optimised paths in both static and dynamic environments for real-time tasks. In addition, this path planning algorithm has the properties to avoid static and moving obstacles in real-time environments.