Motion Path Research Articles

VERIFICATION OF GRID BASED FASTSLAM WITH MULTIPLE CANDIDATES OF PARTICLES

In this paper, we propose a strategy for improving the accuracy of own positions in FastSLAM (Fast-Simultaneous Localization And Mapping). One can see that estimation of self-position and mapping are important for an autonomous mobile robot. As one approach to do this, GPS (Global Positioning System) based self-positioning methods have been proposed. However, GPS-based method has disadvantage that positioning is difficult in the environment where radio waves cannot be received. On the other hand, it is well known that SLAM can perform self-positioning by sensing the external environment based on LiDAR sensors, cameras and other devices. Since SLAM does not require online communication, estimate of self-position and mapping can be performed without being affected by the driving environment. FastSLAM is one of the SLAM algorithms, and samples the path using a particle filter. Each particle represents one possible motion path of robots in the particle filter, and the observed information is used to calculate the weight of each particle and evaluate each path. In FastSLAM, when self-location is determined, a particle is selected by comparing its likelihood as evaluated by the likelihood function. Since this depends on observed information, the reliability of the estimate depends on the accuracy of the sensor. Thus, there is possibility that there are more useful particles among those not selected. In this paper, we propose a new FastSLAM algorithm that selects multiple candidates of particles in FastSLAM algorithm. In the proposed approach, multiple candidates are stored and estimates of self-position is determined by using multiple candidates in the next step. By maintaining multiple particles, more robust self-position estimation and mapping can be performed comparing with the conventional FastSLAM. In this paper, we show the proposed algorithm and numerical simulations are shown to evaluate the performance of the proposed algorithm.

City-scale buildings damage estimation based on broadband physics-based ground motion simulation of 2021 Ms 6.4 Yangbi, China, earthquake

This article proposed a computational workflow for city-scale building damage estimation based on broadband physics-based ground motion simulation. The dynamic stiffness matrix method in frequency-wavenumber domain is employed for broadband physics-based ground motion simulation of finite fault along with site response correction using equivalent-linear site response analysis approach. With simulated ground motion, seismic response of buildings, using multi-degree of freedom model, is simulated using nonlinear time history analysis, and building damage is evaluated based on a trilinear backbone curve of story’s nonlinear force-drift model. Main advantage of the framework lies in the employed dynamic stiffness matrix method for ground motion simulation, which can efficiently generate broadband ground motions in perfect parallel and naturally capture the physics of finite fault source, propagation path, and spatial variability characteristics of ground motion. To demonstrate the salient features of proposed workflow, the 2021 Ms 6.4 Yangbi earthquake is simulated with frequency resolution up to 20 Hz, and seismic damage of buildings in Yangbi county are estimated. Results show that most traditional wood-adobe structures suffer moderate to severe damage, and engineered seismic buildings (mostly RC frame) in Yangbi county suffer minor damage, which agrees with field investigation results.

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

Affine Volterra processes with jumps

The theory of affine processes has been recently extended to continuous stochastic Volterra equations. These so-called affine Volterra processes overcome modeling shortcomings of affine processes by incorporating path-dependent features and trajectories with regularity different from the paths of Brownian motion. More specifically, singular kernels yield rough affine processes. This paper extends the theory by considering affine stochastic Volterra equations with jumps. This extension is not straightforward because the jump structure and possible singularities of the kernel may induce explosions of the trajectories. This study also provides exponential affine formulas for the conditional Fourier–Laplace transform of marked Hawkes processes.

Development and preliminary validation of a virtual reality memory test for assessing visuospatial memory

BackgroundVisuospatial memory impairment is a common symptom of Alzheimer’s disease; however, conventional visuospatial memory tests are insufficient to fully reflect visuospatial memory impairment in daily life.MethodsTo address patients’ difficulties in locating and recalling misplaced objects, we introduced a novel visuospatial memory test, the Hidden Objects Test (HOT), conducted in a virtual environment. We categorized HOT scores into prospective memory, item free-recall, place free-recall, item recognition, and place-item matching scores. To validate the VR memory test, we compared HOT scores among individuals with Alzheimer’s disease (AD), amnestic mild cognitive impairment (aMCI), and normal controls (NC), and also compared these scores with those of conventional neuropsychological tests. We tracked the participants’ movement paths in the virtual environment and assessed basic features, such as total distance, duration, and speed. Additionally, we performed walking trajectory pattern mining such as outlier and stay-point detection.ResultsWe designed and implemented the HOT to simulate a house’s living room and assess participants’ ability to locate hidden objects. Our preliminary results showed that the total HOT score differed among 17 patients with AD, 14 with aMCI, and 15 NC (p < 0.001). The total HOT score correlated positively with conventional memory test scores (p < 0.001). Walking trajectories showed that patients with AD and aMCI wandered rather than going straight to the hidden objects. In terms of basic features, the total duration was significantly greater in AD than in NC (p = 0.008). In terms of trajectory pattern mining, the number of outliers, which were over 95% of the estimated trajectory, was significantly higher in AD than in NC (p = 0.002). The number of stay points, an index in which participants stayed in the same position for more than 2 s, was significantly higher in patients with AD and aMCI compared with NC (AD vs. NC: p = 0.003, aMCI vs. NC: p = 0.019).ConclusionThe HOT simulating real life showed potential as an ecologically valid test for assessing visuospatial memory function in daily life. Walking trajectory analysis suggested that patients with AD and aMCI wandered rather than going straight toward the hidden objects.

Model-free adaptive chaos control for the boost converter

In order to solve the dynamic analysis and chaotic motion control problems of the current-mode-controlled Boost converter system, a state equation model in the continuous current mode is established. The evolutionary path of the periodic motion of the system through the doubling bifurcation to the chaotic motion is analyzed by the bifurcation diagram, Poincaré sections and phase portraits. Then, according to the fundamental principle of the resonant parametric perturbation (RPP) method, a controller is designed based on the model-free adaptive control (MFAC) method to output the perturbation amplitude, and the disturbance quantity is calculated according to the perturbation amplitude. By adding a small amplitude disturbance to the controllable parameter of the controlled system, the chaotic motion of the system is guided to expected periodic orbit. The advantage of this method is that no precise mathematical model (PMM) of the system is required. At the same time, the design of the controller is simple and easy to implement.

Micro-scale mechanism for cyclic deformation behavior in Zirconium under low-cycle-fatigue loading

Cyclic deformation behaviors and damage mechanisms of pure Zr were systemically investigated at fully reversed strain-controlled tension-compression fatigue experiments (R = −1), followed by detailed microstructural characterization using scanning electron microscopy with electron backscatter diffraction and transmission electron microscopy. The results demonstrated that three stages (Ⅰ, Ⅱ, Ⅲ) of cyclic response behavior were observed for all applied strain amplitudes, and the trend of the cyclic stress response mainly originated from the substructure evolutions. The initial cyclic softening was attributed to the formation of alternating high- and low-dislocation-density regions, enhancing the free path of dislocation motion. The fatigue life prediction model was established based on the hysteresis loops strain energy. SEM/EBSD slip traces analysis and TEM double beam diffraction analysis showed that prismatic <a> dislocation slip was the dominating deformation mechanism, and pyramidal <c + a> dislocations and 101¯2 twins played an essential role in accommodating plastic deformations. Further, fatigue damage behavior was not only influenced by initial texture, but also other factors played an essential role.

Design of Viscosity and Nozzle Path Using Food 3D Printer and Pneumatic Pressure Syringe-Type Dispensing System

Recent advancements in 3D printing technology have integrated with Fourth Industrial Revolution technologies such as robotics and artificial intelligence, aiming to overcome the limitations of conventional manufacturing methods. In the field of functional foods, solvent casting, a common manufacturing technique, has been adopted to produce film-like structures with desired sizes and uniform thickness. However, the typical method of coating or injection on a conventional continuous film is difficult to produce in small amounts. To address this limitation, in the study, we developed a pneumatic pressure syringe-type dispensing system integrated with a food 3D printer utilizing fused deposition modeling (FDM) technology. A syringe type is needed to discharge crude liquid manufactured in the food field in a hygienic environment, and a 3D printing method that is easy to manufacture in small quantities or on demand was utilized. Through simulation and experiment, we wanted to confirm whether stable ejection results are generated according to the selected nozzle-based viscosity, inflow conditions, and the nozzle movement path of the food 3D printer. Based on the nozzle selected through simulation, it was confirmed that the fluid and flow velocity distribution of the viscous material were uniformly distributed and discharged under the conditions of 30,000 cps and inflow rate. By setting the parameters of the food 3D printer and preparing a coenzyme Q10 (CoQ10) sample, we achieved a stable oral dissolving film (ODF) extrusion shape through the design of viscosity and 3D printer nozzle path. The optimal viscosity range for the ODF solution was found to be 25,000 to 35,000 cps, exhibiting precise dimensions and shapes without distortion and yielding the most stable extrusion results. We defined four different nozzle path designs based on minimizing the movement of the 3D printer nozzle. Among them, a 16-step path design demonstrated a stable extrusion method, showing no tailing phenomenon under the conditions of 0.2 MPa pressure and −15.4 KPa vacuum pressure. In future research, we plan to conduct additional research to determine whether the discharge results vary depending on conditions such as viscosity of the crude liquid, nozzle path combination, and ODF thickness.

NIMG-70. TRACKING CELLULAR SOCIAL BEHAVIOR OF PATIENT-DERIVED PRIMARY CELL LINES TO GATHER NEW INSIGHTS FOR BETTER STRATIFICATION OF GLIOBLASTOMA PATIENTS

Abstract Glioblastoma is one of the most frequent and aggressive brain tumors. The average life of these patients is on average less than 15 months, due to the proliferation and invasiveness of the tumor cells, which not only constitute the tumor mass but which also infiltrate the normal parenchyma brain to form new cancerous sites. Knowing the specific behavior of the cell types that make up the tumor of every single patient, in terms of volume, shape, motility, and cell-cell interaction could be informative for patients’ tumor stratification. Here we developed a protocol that integrates imaging and image processing algorithms, to accurately and quantitatively identify the morphological and behavioral characteristics of tumor cells cultured in vitro and track their movement over time. This approach enables quantifying important biological processes such as cell migration and cell growth, both of which play a major role in the development of diseases. The cell tracking method combines segmentation methods with spatiotemporal optimization to analyze microscopy images. The method also determines various information about cell movements such as movement paths, speed, and distance covered. Knowing the behavior of the malignant glioma cells of a specific patient, in terms of shape and motility (e.g. direction and speed of movement), also in relation to other cells could be informative in the context of dynamic precision medicine. In this context, we created a model that integrates the characteristics of each patient's tumor cells and provides a new way of stratifying patients that can be correlated with clinical data.

Three-dimensional truss path planning of cellular robots based on improved sparrow algorithm

AbstractAs space missions are needed in the future, the assembly volume of the space truss will become larger and larger, and the advancing path of the on-orbit cellular robot to the mission target will become more and more complicated. If the shortest moving path cannot be found in the truss environment, the climbing time of the robot on the truss will be greatly increased. To improve the speed of the cellular robot moving to the target point on the large space truss, this paper designs a cellular robot structure and configuration suitable for climbing on the truss and uses the improved sparrow algorithm to solve the problem of robot motion path planning. By establishing a mathematical model of the space truss, the improved sparrow algorithm is used to find the shortest path between the starting point and the end point in the truss environment. Finally, the data of this algorithm are compared with the data of other algorithms. The data results show that the improved sparrow algorithm is very effective in solving the optimal path of the space truss. The improved sparrow algorithm keeps the same optimal path compared with the standard sparrow algorithm, and the overall reaction time is increased by 51.60%, and the number of effective iterations is increased by about 13.87%.

A spanning tree algorithm with adaptive pruning with low redundancy coverage rate

In reality, when UAV is used in region reconnaissance, the shape of region is usually irregular. Selecting an algorithm to find a complete coverage path with smaller coverage redundancy rate, lower path motion complexity and theoretical energy consumption has certain research significance for the complete coverage path planning (CCPP). In existing algorithms, there is always a high redundant coverage rate under irregularly shaped regions. The motion complexity and the theoretical energy consumption of the path also increase. In this paper, a kind of spanning tree with adaptive pruning (STAP) algorithm based on the grid map is proposed to solve the above problems: on the one hand, it uses a greedy growth strategy to determine the branching growth direction, and proposes an adaptive pruning method for self-optimization of the initial path; on the other hand, a kind of nearest neighbor sub-region connection rule of adjacent paths is proposed to reduce the complexity and the coverage redundancy rate. The STAP algorithm searches for all grid centers, grows, and optimizes the spanning tree by creating paths between the centers. At the end of the optimization, it creates a complete coverage path based on the distribution of the spanning tree. Experiments show that when the STAP algorithm is applied to unobstructed regions, it plans the path with the lowest redundant coverage rate, which reaches 2.6296% and 4.9402%; when it is applied to the regions include obstacles, the advantages of the algorithm are more significant: On the one hand, the path planned by STAP has the least total length, the lowest number of turning and turning back. On the other hand, the redundant coverage rate of the path is still the lowest, reaching 4.1362% and 2.7765%, respectively. The STAP is primarily suitable for situations about CCPP under grid maps formed by irregularly shaped regions, and it holds certain significance for ground reconnaissance of UAVs.

A clinical comparison of sagittal condylar inclination and Bennett angle derived from a conventional electronic tracking device and an optical jaw tracking device

The challenges of conventional methods to measure the sagittal condylar inclination and the Bennett angle include patient discomfort and an output of averaged values calculated on the arc of mandibular movement. Programming these average values into a mechanical or mathematically simulated articulator may introduce occlusal errors. The purpose of this clinical study was to compare the sagittal condylar inclination and Bennett angle measurements derived from a conventional electronic tracking device and an optical tracking device. Fifteen dentate participants with at least 12 occluding units, 25 years old and above, participated in the study. Each participant's mandibular movements were recorded by the 2 systems for different condylar guidance values. A conventional tracking device, the Cadiax Compact 2, and an optical tracking device, the MODJAW, were used for this study. Sagittal condylar inclination was measured at 3mm and 5mm using protrusive records and the values of the Bennett angle at 3mm were measured using excursive records along the path traced by mandibular condyles. The Wilcoxon signed-rank test was used to analyze the paired data from the 2 methods. A significant difference was found between right (P=.007) and left sagittal condylar inclinations (P=.010) measured at 3mm with the conventional and optical tracking devices. A significant difference was found between right (P=.015) and left sagittal condylar inclinations (P=.004) measured at 5mm with the conventional and optical tracking devices. The right and left Bennett angles measured at 3mm with the conventional and optical tracking devices were statistically different (P=.043 and P=.035 respectively). The sagittal condylar inclination and Bennett angle measured at different positions along the path of movement exhibited significant differences. The sagittal condylar inclination and Bennett angle values obtained from the conventional tracking device were generally significantly less than those derived from the optical tracking device.

Predicting astrometric microlensing events from Gaia Data Release 3

ABSTRACT Currently, astrometric microlensing is the only tool that can directly measure the mass of a single star; it can also help us to detect compact objects such as isolated neutron stars and black holes. The number of microlensing events that are being predicted and reported is increasing. In this paper, potential lens stars are selected from three types of stars: high-proper-motion stars, nearby stars, and high-mass stars. For each potential lens star, we select a larger search scope to find possible matching sources and to avoid missing events as much as possible. Using data from Gaia Data Release 3, we predict 4500 astrometric microlensing events with δθ+ &amp;gt; 0.1 mas, which occur between J2010.0 and J2070.0, where 1664 events are different from those found previously. There are 293 lens stars that can cause two or more events, where five lens stars can cause more than 50 events. We find that 116 events have the distance of background stars from the proper motion path of lens stars more than 8 arcsec in the reference epoch, where the maximum distance is 16${_{.}^{\prime\prime}}$6. Thus, the cone search method of expanding the search range of sources for each potential lens star can reduce the possibility of missing events.

Examination of head versus body heading may help clarify the extent to which animal movement pathways are structured by environmental cues?

Understanding the processes that determine how animals allocate time to space is a major challenge, although it is acknowledged that summed animal movement pathways over time must define space-time use. The critical question is then, what processes structure these pathways? Following the idea that turns within pathways might be based on environmentally determined decisions, we equipped Arabian oryx with head- and body-mounted tags to determine how they orientated their heads – which we posit is indicative of them assessing the environment – in relation to their movement paths, to investigate the role of environment scanning in path tortuosity. After simulating predators to verify that oryx look directly at objects of interest, we recorded that, during routine movement, > 60% of all turns in the animals’ paths, before being executed, were preceded by a change in head heading that was not immediately mirrored by the body heading: The path turn angle (as indicated by the body heading) correlated with a prior change in head heading (with head heading being mirrored by subsequent turns in the path) twenty-one times more than when path turns occurred due to the animals adopting a body heading that went in the opposite direction to the change in head heading. Although we could not determine what the objects of interest were, and therefore the proposed reasons for turning, we suggest that this reflects the use of cephalic senses to detect advantageous environmental features (e.g. food) or to detect detrimental features (e.g. predators). The results of our pilot study suggest how turns might emerge in animal pathways and we propose that examination of points of inflection in highly resolved animal paths could represent decisions in landscapes and their examination could enhance our understanding of how animal pathways are structured.

Super-Resolution Imaging Enhancement through a 2D Scanning Galvanometer: Algorithm Formulation and Application in Aerial Optoelectronic Systems

As the fields of aviation and aerospace optics continue to evolve, there is an increasing demand for enhanced detection capabilities in equipment. Nonetheless, in applications where both optical and mechanical constraints are stringent, the continuous expansion of optical aperture and focal length is impractical. Given the existing technological landscape, employing super-resolution algorithms to enhance the imaging capability of optical systems is both practical and highly relevant. This study capitalizes on using a 2D scanning galvanometer in optical systems to acquire micro-displacement information. Initially, an imaging model for optical systems equipped with a 2D scanning galvanometer was established, and the displacement vectors for both forward and sweep image motions were defined. On this foundation, we incorporated micro-displacement information that can induce high-frequency aliasing. Subsequently, the motion paths of the galvanometer were planned and modeled. To align image sequences with micro-displacement correlations, the Lucas–Kanade (L-K) optical flow method was employed with multi-layer pyramid iteration. Then, super-resolution reconstruction was performed using kernel regression techniques. Ultimately, we tested the algorithm on an aeronautical optoelectronic pod to evaluate its impact on optical resolution and imaging quality. Compared with the original images, the 16-frame image demonstrated a 39% improvement in optical resolution under laboratory conditions. Moreover, the algorithm exhibited satisfactory performance under both nighttime and daytime conditions, as well as during aerial tests.

Sensing the Positions of Multi Droplets on a Piezoelectric Substrate Using a Smartphone Based on CIELab Color Model

A method for simultaneously sensing positions of multi droplets on a piezoelectric substrate was presented. A double D-value algorithm was presented to distinguish target droplets using CIELab color model. An application soft (app) was developed to sense positions of target droplets. In order to verify the presented method, black ink solution droplets on a 128°-YX LiNbO3 substrate were used to demonstrate for sensing droplets position. Results show that different number of target droplets can be distinguished and their positions can be simultaneously sensed. As an application, a 5 micro-liters black ink solution droplet was driven on the piezoelectric substrate by surface acoustic wave. Its position was sensed and its movement path was analyzed using developed app. In the meantime, the transporting velocity of the droplet was obtained, of which value is 0.33 mm/s from 6th second to 7th second at 27.5 dBm electric signal power.

AUGMENTED REALITY-BASED NASAL ENDOSCOPE VIDEO RECONSTRUCTION AND REGISTRATION

Neuroendoscopic surgery is a minimally invasive surgical technique commonly used to remove a tumor through the patient’s mouth or nose, which requires the surgeon to avoid important neural and vascular structures. Augmented reality-based surgical navigation can provide surgeons with more information and visual aids, clarifying the structure and details of the surgical area. It requires real-time registration of intraoperative endoscopic video with pre-operative CT models. However, 3D reconstruction and camera tracking from nasal endoscopic video are challenging due to the narrow nasal cavity and the lack of texture. Besides, the nasal endoscopic datasets that can be used for deep learning-based depth estimation are scarce and hard to elaborate. To this end, a style-augmented module (SAM) is proposed in this study to minimize discrepancies between different endoscopic datasets. An unsupervised approach was trained to generate depth maps and camera motion paths, which were used to reconstruct the nasal scene’s 3D model. The obtained reconstruction was aligned with a pre-operative CT model for augmented reality-based surgical navigation. The results showed that the proposed SAM improved the generalization of the depth estimation model on nasal endoscopy data. The proposed approach to combining augmented reality technology with surgical navigation is considered instrumental in furnishing surgeons with richer information on the nasal endoscopic surgical scene.

Enhanced ant colony algorithm with obstacle avoidance strategy for multi-objective path planning of mobile robots

Path planning with multiple evaluation metrics makes the motion of a robot realistic, but the contradiction between the metrics and the lack of global search and obstacle avoidance capabilities increases the difficulty of obtaining the optimization solution. To solve these problems, an enhanced ant colony algorithm (EACA) with an obstacle avoidance strategy is proposed in this article. First, the path planning model is constructed, and strict movement rules are designed. Secondly, the EACA with global search, balancing the contradiction between metrics, is designed. The dynamic regulation of pheromone concentration and the mechanism of fluctuating pheromone distribution are explored, heuristic information is optimized and the path planning effect is enhanced. Finally, a new mechanism of away-from-obstacles is proposed as the obstacle avoidance strategy, which ensures a reasonable safe distance. Comparative simulations on several different maps validate the performance of EACA with the obstacle avoidance strategy for planning robot movement paths.

Movement dynamics, sediment turnover and sheltering behaviours of the nocturnal coral reef sea cucumber, Stichopus cf. monotuberculatus

Animal movement and feeding studies shed light on ecological roles and can inform management strategies for fished species. However, the feeding and movement of nocturnal sea cucumbers have been rarely studied. We determined the movement dynamics, feeding ecology and sheltering behaviour of the nocturnal Stichopus cf. monotuberculatus (dragonfish) at One Tree Reef and Heron Island Reef on the southern Great Barrier Reef. The short-term movement of individual sea cucumbers were tracked, and sediment defecation rates were measured. Displacement rates averaged 3–33 cm h−1 across the four sites, with the sea cucumbers moving more slowly approaching sunrise and within patches of organically rich sediment. The movement paths were moderately tortuous, with larger sea cucumbers moving along straighter paths. Rates of sediment turnover averaged 7.8 g h−1 and were greatest for larger individuals. The faecal casts were organically richer and composed of finer grains than the ambient surface sediments, implying that S. cf. monotuberculatus avoids consuming coarse sediments and feeds on nutritionally rich particles. The sea cucumbers sheltered under hard reef substrata and moved non-directionally on the seascape, with a few individuals homing back to their original refuges. The affinity to reef substrata and short nightly net displacements of S. cf. monotuberculatus have implications for the spatial management of this and other similar species. Larger individuals occupied deeper refuges, implying that projected declines in substratum rugosity on coral reefs due to climate change could impact the size structure of sea cucumber populations, with implications for fisheries.

An improved sand cat swarm optimization for moving target search by UAV

Moving target search is a challenging dynamic path planning problem. In this scenario, unmanned aerial vehicles endeavor to locate a moving entity based on sensor information, utilizing the optimal path generated by a search algorithm. Based on the Bayesian principle, the task can be transformed into an optimization issue of the fitness function with the maximum probability of capturing the objective. In this study, an improved version of the sand cat swarm optimization algorithm, called the ISCSO search algorithm, is designed to tackle the moving target search issue effectively. Firstly, the presented ISCSO algorithm enhances the planning efficiency of the algorithm by encoding the unmanned aerial vehicle search path information into a set of motion paths through the motion-encoded mechanism. Secondly, the elite pooling strategy and the adaptive T-distribution are constructed to effectively improve the algorithm’s ability to escape local optima and enhance its variability. Finally, ISCSO proposes a main architecture that seamlessly merges the search and attack methods of the sand cat swarm optimization algorithm, striking a balance between global and local search capabilities. To evaluate the superiority of the proposed algorithm, nine diverse search scenarios are constructed to verify its performance. The simulation results demonstrate that ISCSO achieves higher detection accuracy and offers more effective search paths for locating dynamic targets in comparison to other well-established metaheuristic algorithms. Code has been available at https://github.com/yb-niu1/ISCSO.