Complex Three-dimensional Environment Research Articles

An improved Bi-RRT*-based path planning algorithm with adaptive search strategy assignment mechanism for ultra-low-altitude penetration of fixed-wing aircraft

The ultra-low-altitude penetration with complex three-dimensional terrain environment usually requires the path planning algorithm to be computationally efficient to generate feasible optimized flight paths in a limited period of time. This paper investigates an improved bidirectional rapidly-exploring random tree* (RRT*) path planning algorithm with adaptive search strategy assignment mechanism to accelerate the exploration speed. The greedy optimization method and the three-dimensional Dubins method are utilized to reduce redundant nodes and optimize the flight path to satisfy the kinematic constraints of the fixed-wing aircraft. The model predictive control method is then employed to design the path-following controller, in which multiple types of constraints can be addressed. To demonstrate the superior planning efficiency of the proposed adaptive-alternating-exploration RRT* method, the existing widely-used path planning algorithms are used as benchmarks in the simulation studies. Simulation results show that the path planning time can be significantly reduced by up to 90% with the proposed adaptive-alternating-exploration RRT* method, simultaneously with optimized path length. Moreover, the designed model predictive path-following controller succeeds to track the planned collision-free path with maximum tracking errors of less than 2 meters. The effectiveness of the path planning algorithm and the designed controller is thoroughly demonstrated.

Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta*–Artificial Potential Field Method

The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.

A review of unmanned aerial vehicle path planning techniques

The paper offers an exhaustive scholarly review of the algorithms and techniques employed in Unmanned Aerial Vehicle (UAV) path planning, categorizing these methodologies based on spatial dimensions, planning steps, and the nature of planning maps. It provides a critical evaluation of a plethora of algorithms, including random search methods, particle swarm algorithms, genetic algorithms, and A* algorithms, among others. The study elucidates the advantages and limitations of each algorithm, with a particular focus on their efficacy in real-time planning and navigation within complex three-dimensional environments. It underscores that while the domain of pre-flight path planning has reached a level of relative maturity, there exists a conspicuous gap in the literature concerning real-time obstacle avoidance and optimal path planning, particularly when constrained by limited computational resources. The paper thus serves as both a comprehensive review and a call for further research aimed at addressing these identified lacunae to ensure the generation of safe and feasible UAV trajectories.

PIEZO1-mediated mechanosensing governs NK-cell killing efficiency and infiltration in three-dimensional matrices.

Natural killer (NK) cells play a vital role in eliminating tumorigenic cells. Efficient locating and killing of target cells in complex three-dimensional (3D) environments are critical for their functions under physiological conditions. However, the role of mechanosensing in regulating NK-cell killing efficiency in physiologically relevant scenarios is poorly understood. Here, we report that the responsiveness of NK cells is regulated by tumor cell stiffness. NK-cell killing efficiency in 3D is impaired against softened tumor cells, whereas it is enhanced against stiffened tumor cells. Notably, the durations required for NK-cell killing and detachment are significantly shortened for stiffened tumor cells. Furthermore, we have identified PIEZO1 as the predominantly expressed mechanosensitive ion channel among the examined candidates in NK cells. Perturbation of PIEZO1 abolishes stiffness-dependent NK-cell responsiveness, significantly impairs the killing efficiency of NK cells in 3D, and substantially reduces NK-cell infiltration into 3D collagen matrices. Conversely, PIEZO1 activation enhances NK killing efficiency as well as infiltration. In conclusion, our findings demonstrate that PIEZO1-mediated mechanosensing is crucial for NK killing functions, highlighting the role of mechanosensing in NK-cell killing efficiency under 3D physiological conditions and the influence of environmental physical cues on NK-cell functions.

Humanized brain organoids-on-chip integrated with sensors for screening neuronal activity and neurotoxicity.

The complex structure and function of the human central nervous system that develops from the neural tube made in vitro modeling quite challenging until the discovery of brain organoids. Human-induced pluripotent stem cells-derived brain organoids offer recapitulation of the features of early human neurodevelopment in vitro, including the generation, proliferation, and differentiation into mature neurons and micro-macroglial cells, as well as the complex interactions among these diverse cell types of the developing brain. Recent advancements in brain organoids, microfluidic systems, real-time sensing technologies, and their cutting-edge integrated use provide excellent models and tools for emulation of fundamental neurodevelopmental processes, the pathology of neurological disorders, personalized transplantation therapy, and high-throughput neurotoxicity testing by bridging the gap between two-dimensional models and the complex three-dimensional environment in vivo. In this review, we summarize how bioengineering approaches are applied to mitigate the limitations of brain organoids for biomedical and clinical research. We further provide an extensive overview and future perspectives of the humanized brain organoids-on-chip platforms with integrated sensors toward brain organoid intelligence and biocomputing studies. Such approaches might pave the way for increasing approvable clinical applications by solving their current limitations.

Improved multi-search strategy A* algorithm to solve three-dimensional pipe routing design

Pipe routing design (PRD) has a significant impact on the safety and operational efficiency of the system. PRD is involved in multi-disciplinary knowledge such as mechanical engineering, computer graphics and mathematical geometry, and it can be abstracted as a complex combined optimization problem. A* algorithm is one of the most commonly employed approaches to solve two-dimensional (2-D) path planning. However, due to the central idea of A* algorithm is all-traversal search, the search process considers all possibilities and requires a blind search one by one, it is difficult to solve PRD problems in complex three-dimensional (3-D) space environment. In this study, an improved multi-search strategy A* algorithm (MSSA*) is presented to overcome the disadvantages of the traditional A* algorithm in solving 3-D PRD problems. The MSSA* algorithm contains four novel mechanisms which are the node directional discrimination rule, the double-layer domain extension search strategy, the multi-factor heuristic evaluation function, and the dynamic adaptive weighting strategy. Then, a range of experiments are conducted to prove the validity of the presented mechanisms. Subsequently, the MSSA* is compared with several existing algorithms for solving 3-D PRD to further verify the superiority of MSSA* in terms of high-efficiency and practicality. Finally, the MSSA* is applied to solve the 3-D PRD problems for offshore production platforms in oil and gas treatment system.

A Multi-Strategy Improved Differential Evolution algorithm for UAV 3D trajectory planning in complex mountainous environments

In response to the complexity of power repair in mountainous areas and the limitations of traditional vehicles due to terrain constraints, this study focuses on the three-dimensional trajectory planning problem of UAVs (Unmanned Aerial Vehicles) in mountainous environments. Our goal is to provide effective solutions for the trajectory planning problem of UAVs in mountainous environments. Firstly, a UAV trajectory planning model is established, incorporating optimization objectives such as energy consumption, trajectory cost, obstacle avoidance cost, smoothing cost, and stability cost. The trajectory planning problem is transformed into an objective function optimization task with multiple performance constraints. To overcome the inefficiency and infeasibility of traditional algorithms in solving complex three-dimensional flight environments, we propose improvements to the Differential Evolution (DE) algorithm through three strategies: incorporating mutation crossover factor optimization strategy, an adaptive guidance mechanism, and an elite disturbance mechanism based on population classification. The Multi-Strategy Improved Differential Evolution (MSIDE) algorithm is introduced, and its time and space complexity are analyzed. Finally, the proposed method is compared with various algorithms through benchmark functions tests, Friedman test, Wilcoxon rank-sum test, simulation experiments in three-dimensional environments, and parameter sensitivity analysis experiments. The simulation results show that compared with the current state-of-the-art algorithms, the MSIDE algorithm improves the objective function value by 11.34% on average in regular terrain and 5.04% on average in complex terrain environments. The results demonstrate the convergence, multi-objective search capability, and global search ability of MSIDE, validating its effectiveness in solving the trajectory planning problem of UAVs in complex mountainous environments.

Three-dimensional collaborative path planning for multiple UCAVs based on improved artificial ecosystem optimizer and reinforcement learning

This study proposes a multi-strategy evolutionary artificial ecosystem optimizer based on reinforcement learning (MEAEO-RL) to tackle the collaborative path-planning problem of multiple unmanned combat aerial vehicles (UCAVs) in complex environments with multiple constraints. The objective is to generate optimal candidate paths for each UCAV, ensuring they reach the destination simultaneously while considering time variables and obstacle avoidance. To overcome the limitations of the standard artificial ecosystem optimizer (AEO), such as local optimality and slow convergence, a learning framework inspired by brain-like perception is constructed. This framework enhances swarm agents with greater intelligence by fusing swarm intelligence and human cognitive mechanisms. Meanwhile, a multi-strategy database is implemented within the evolutionary learning framework to replace the single-update method of the AEO during the consumption phase. To reduce the computational complexity of the algorithm, agents in the consumption stage utilize experience accumulation from reinforcement learning to select an effective update strategy for obtaining the latest consumer location. Path-planning simulation experiments are conducted in a series of complex three-dimensional environments, demonstrating the algorithm’s robustness, improved convergence accuracy, and ability to plan collaborative paths for multiple UCAVs while satisfying various constraints.

Multi-UAV Trajectory Planning during Cooperative Tracking Based on a Fusion Algorithm Integrating MPC and Standoff

In this paper, an intelligent algorithm integrating model predictive control and Standoff algorithm is proposed to solve trajectory planning that UAVs may face while tracking a moving target cooperatively in a complex three-dimensional environment. A fusion model using model predictive control and Standoff algorithm is thus constructed to ensure trajectory planning and formation maintenance, maximizing UAV sensors’ detection range while minimizing target loss probability. Meanwhile, with this model, a fully connected communication topology is used to complete the UAV communication, multi-UAV formation can be reconfigured and planned at the minimum cost, keeping off deficiency in avoiding real-time obstacles facing the Standoff algorithm. Simulation validation suggests that the fusion algorithm proves to be more capable of maintaining UAVs in stable formation and detecting the target, compared with the model predictive control algorithm alone, in the process of tracking the moving target in a complex 3D environment.

Three-dimensional UCAV path planning using a novel modified artificial ecosystem optimizer

As a challenging high-dimensional multimodal optimization problem, path planning for unmanned combat aerial vehicles (UCAVs) has evolved into a hard optimization problem with multiple objectives and types of constraints in complex operational environments. The traditional approaches lack good search capability in complex multimodal search spaces, resulting in difficulties in providing satisfactory flight paths under multiple constraints. Intelligent optimization algorithms have become the first choice for solving path planning problems because of their excellent global exploration capabilities. Artificial ecosystem optimizer (AEO) is an efficient and intelligent optimization algorithm that has remarkable effectiveness in handling optimization problems. However, it also has the disadvantage of slow convergence. In this work, a modified version of the AEO, named MAEO, is proposed to overcome its shortcomings and apply it to the UCAV path planning problem. In the MAEO algorithm, a new production model that focuses on the current optimal search area is proposed to improve the quality of the producer, thereby effectively guiding consumers to search for the optimal space. Then, an enhanced updating consumption mechanism inspired by animal predation behavior is designed, including two predation operations: one is the introduction of the dynamic elite individual in the original update method to simulate that consumers are also predated by their natural enemies during predation to enhance the convergence speed of the algorithm. The second is a unique spiral predation strategy adopted by the consumers to round up the prey. This mechanism not only effectively avoids the lack of population diversity but also improves the global exploration ability of the algorithm. Furthermore, an adaptive Cauchy mutation strategy is successfully hybridized with the AEO algorithm to boost the ability of the algorithm to escape from the local optimum. Simulation experiments of path planning in a series of complex three-dimensional environments show that the algorithm can plan a path satisfying the constraints stably and efficiently, which proves the superiority of the algorithm.

Real-time multi-quadrotor trajectory generation via distributed receding architecture and hierarchical planning in complex environments

Generation of multi-quadrotor trajectories in real-time in complex three-dimensional environments remains a grand challenge. Trajectory planning becomes computationally prohibitive as the number of quadrotors and obstacles increases. This paper proposes the distributed receding architecture-based hierarchical trajectory planning method (drHTP) to tackle this issue. The distributed receding architecture is established to formulate and solve a series of single-quadrotor short-horizon planning problems for reducing the computation complexity. In distributed planning, the time-heuristic priority mechanism is devised to assign a reasonable planning sequence to enhance the convergence performance. The hierarchical planning, including front-end initial trajectory generation and back-end trajectory optimization, is introduced for the single-quadrotor in each short horizon to further reduce the computation time. The sparse A* search algorithm is modified to only consider adjacent obstacles for obtaining the initial trajectory rapidly. The convergence of drHTP is analyzed theoretically. Numerical simulations with moving and dense obstacle scenarios are carried out to verify the effectiveness of drHTP. The comparative simulation results demonstrate that drHTP outperforms the state-of-the-art distributed sequential convex programming and distributed model predictive control methods in terms of computational efficiency. drHTP is also validated by the physical experiment in an indoor testbed.

Generation of Human Induced Pluripotent Stem Cell-Derived Bone Marrow Organoids

The human bone marrow niche plays an essential role in supporting and regulating hematopoiesis throughout life. Three dimensional in vitro models to study spatio-temporal control of intrinsic and/or extrinsic factors orchestrating hematopoietic fate decisions in health and disease are needed. Here we present a method to generate a complex bone marrow-like organoid (BMO) from human induced pluripotent stem cells (iPSCs), consisting of hematopoietic, mesenchymal and vascular cells. Our approach expands current co-culture-based models of isolated primary endothelial and stromal cells, or bioengineered microfluidic chips and overcomes some of their intrinsic limitations. We devised a protocol of stepwise mesodermal progenitor differentiation through hemogenic endothelium to generate a complex heterocellular organoid: After embryoid body formation, mesoderm was induced and patterned by a sequential combination of Wnt activation, Nodal inhibition, BMP4, VEGF, bFGF and SCF. Embryoid bodies were embedded into a three-dimensional matrix and stimulated with defined cytokine cocktails in a timely fashion to generate hematopoietic progenitor cells while maintaining endothelial cell generation. At day 10 of differentiation, sprouted embryoid bodies were separated and transferred into a 96-well plate to promote full organoid maturation until day 17. Flow cytometric analysis of dissociated BMOs revealed three main cellular compartments, consisting of hematopoietic (CD45+), mesenchymal (CD45-CD31-CD34-CD271+) and endothelial cells (CD45-CD31+). Analysis of the cellular composition yielded an average content of~40,6% hematopoietic cells, ~39,5% mesenchymal cells, ~6% endothelial cells, ~1,6% HSPCs and ~0,9% MSCs per BMO. Single-cell RNA sequencing of dissociated BMOs confirmed the presence of these distinct cell clusters. Sorted hematopoietic (CD45+CD11b-CD34+-HSPCs) and mesenchymal (CD45-CD31-CD271+CD105+CD90+CD73+-MSCs) stem and progenitor cells showed multilineage differentiation potential in colony-forming-unit (CFU) and trilineage-differentiation assays, respectively. Confocal imaging of whole organoids showed that CD31+ endothelial cells formed a vessel-like network covered by a Collagen IV+ basement membrane and PDGFR-β+ murals cells, resembling pericytes. Ultrastructural analysis by transmission electron microscopy (TEM) revealed capillary-like structures of endothelial cells enclosed by pericytes, which were connected by tight junctions. Importantly, we also detected CXCL12+- CAR-like cells in close association to endothelial cells in our BMOs. Whereas CD45 expressing cells were distributed throughout the organoid, Runx1 expressing cells were found to be localized in distinct clusters. We examined whether the BMO niche promotes maturation of blood progenitors into mature cells of the myeloid lineage. We identified neutrophil progenitor stages from ProNeu to matureNeus by flow cytometry, S100A8/A9 and MPO expressing cells by confocal imaging and cells with characteristic features of neutrophil granulocytes by TEM, showing that granulopoiesis proceeds without the addition of lineage-instructing cytokines as G-CSF or GM-CSF. Next, we tested whether BMOs can be used as a model system to recapitulate the phenotype of monogenic bone marrow failure syndromes. BMOs from iPSCs edited to express a hypomorphic variant of VPS45 showed increased formation of reticulin fibers and a higher abundance of SMA expressing pericytes, reminiscent of increased myelofibrosis in bone marrow biopsies of VPS45 deficient patients. We also started to show that BMOs can be transplanted into immunodeficient NSG-mice with further BMO growth in vivo for up to four months. In ongoing experiments we analyze the contribution of human hematopoietic cells to blood cell chimerism in the host mice. In summary, iPSC-derived BMOs offer a new tool to study hematopoietic development and disease evolution within a complex three-dimensional human environment exhibiting key cellular and structural features of the human bone marrow niche.

UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization.

In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.

Local body shape control of an articulated mobile robot and an application for recovery from a stuck state

This paper proposes a method for an articulated mobile robot to change its local body shape in a complex environment. By coexisting a heuristic approach which is easy to design a motion in a complex three-dimensional environment and a model-based approach which can utilize the redundancy of the robot, the robot achieves propulsion in a complex environment and the local body shape control simultaneously. A part of the robot's body shape is changed based on a kinematic model, whereas the rest of it is controlled to follow the original shape or maintain its original position based on a heuristic method. As an application of the proposed method, the recovery motion from a stuck state is proposed. The robot is recovered from a stuck state by controlling the part of the robot that is in collision with an obstacle to move it away from the obstacle. The effectiveness of the proposed method is verified by experiments on an actual robot.

Heterogeneous spheroids with tunable interior morphologies by droplet-based microfluidics

Heterogeneous spheroids that mimic the complex three-dimensional environment of natural tissues are needed in various biomedical applications. Geometric cues from cellular matrix play invaluable roles in governing cell behavior and phenotype. However, the structural complexity of interior morphologies of spheroids is currently limited due to poor spatial resolution of positioning/orientation of cellular constructs. Here, a coaxial capillary microfluidic device is developed to generate gelatin methacrylate (GelMA) microspheres with tunable dimensions and interior morphologies, such as core-shell, or microspheres with interior undulated wavy, or spiral canals, by manipulating the two-phase flow of hydrogel precursor solution and methylcellulose solution. The formation of diverse and exquisite interior morphologies is caused by the interacting viscous instabilities of the two-phase flow in the microfluidic system, followed by water-in-oil emulsion and photo-initiated polymerization. Polyethylene glycol diacrylate (PEGDA) is incorporated into the GelMA solution to tune the mechanical properties of the fabricated microspheres, and an optimized concentration of PEGDA is confirmed by evaluating the in vitro proliferation and vascularization of human umbilical vein endothelial cells. Further, a heterogeneous spheroid with spiral blood vessel lumen is constructed to demonstrate the versatility and potential of the proposed droplet-based microfluidic approach for building functional tissue constructs.

Study on border patrol task planning of heterogeneous UAVs group based on swarm intelligence.

Heterogeneous UAVs performing patrol tasks are a new type of border patrol method with high flexibility, high patrol efficiency, and low operating cost In order to improve the ability of heterogeneous UAVs to perform border patrol task, by constructing a three-dimensional complex coordinated planning model, a multi-objective fitness function with the minimum patrol energy consumption and maximum patrol coverage of UAVs in a complex mountain environment is established. Design an improved shuffled frog leaping algorithm (ISFLA) based on spiral search mechanism to solve the problem of task planning in complex mountain environment. The proposed algorithm is verified by simulation experiments. The simulation results show that the ISFLA algorithm for solving the path problem in complex three-dimensional environment has significantly improved the solving efficiency, accuracy and global convergence compared with the particle swarm optimization (PSO), differential evolution (DE) and shuffled frog leaping algorithm (SFLA). The experiments show that the proposed algorithm also has excellent solving ability in solving complex planning problems.

Foam Replica Method in the Manufacturing of Bioactive Glass Scaffolds: Out-of-Date Technology or Still Underexploited Potential?

Since 2006, the foam replica method has been commonly recognized as a valuable technology for the production of highly porous bioactive glass scaffolds showing three-dimensional, open-cell structures closely mimicking that of natural trabecular bone. Despite this, there are important drawbacks making the usage of foam-replicated glass scaffolds a difficult achievement in clinical practice; among these, certainly the high operator-dependency of the overall manufacturing process is one of the most crucial, limiting the scalability to industrial production and, thus, the spread of foam-replicated synthetic bone substitutes for effective use in routine management of bone defect. The present review opens a window on the versatile world of the foam replica technique, focusing the dissertation on scaffold properties analyzed in relation to various processing parameters, in order to better understand which are the real issues behind the bottleneck that still puts this technology on the Olympus of the most used techniques in laboratory practice, without moving, unfortunately, to a more concrete application. Specifically, scaffold morphology, mechanical and mass transport properties will be reviewed in detail, considering the various templates proposed till now by several research groups all over the world. In the end, a comprehensive overview of in vivo studies on bioactive glass foams will be provided, in order to put an emphasis on scaffold performances in a complex three-dimensional environment.

Complex vocal learning and three-dimensional mating environments

Complex vocal learning, the capacity to imitate new sounds, underpins the evolution of animal vocal cultures and song dialects and is a key prerequisite for human speech and song. Due to its relevance for the understanding of cultural evolution and the biology and evolution of language and music, the trait has gained much scholarly attention. However, while we have seen tremendous progress with respect to our understanding of its morphological, neurological and genetic aspects, its peculiar phylogenetic distribution has remained elusive. Intriguingly, animals as distinct as hummingbirds and humpback whales share well-developed vocal learning capacity in common with humans, while this ability is quite limited in nonhuman primates. Yet, solving this ‘vocal learning conundrum’ may shed light on the constraints ancestral humans overcame to unleash their vocal capacities. To this end I consider major constraints and functions that have been proposed. I highlight an especially promising ecological constraint, namely the spatial dimensionality of the environment. Based on an informal comparative review, I suggest that complex vocal learning is associated with three-dimensional habitats such as air and water. I argue that this is consistent with recent theoretical advances—i.e., the coercion-avoidance and dimensionality hypotheses—and with the long-standing hypothesis that mate choice is a major driver of the evolution and origin of complex vocal learning. However, I stress that multiple functions may apply and that quantitative phylogenetic comparative methods should be employed to finally resolve the issue.

An attention U-Net model for detection of fine-scale hydrologic streamlines

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.

Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.