Problem Of Path Planning Research Articles

Method of Motion Path Planning Based on a Deep Neural Network with Vector Input

The article deals with the problem of path planning in a two-dimensional environment based on deep learning neural networks. Deep neural networks require large amounts of data and place high computational requirements on computing tools. The lack of sufficient data leads to a decrease in the accuracy of the neural network, and high computational requirements at the learning stage limit the use of this technology in engineering practice. In this paper, the forms of representation of the environment for the input of a neural network are studied. Vector form allows to reduce the amount of information supplied to the input of a neural network, but it leads to the need to use more complex neural networks. In this article, a combined form of representation is proposed, including a vector global and local map layout. The vector part of the map includes the position of the robot, the position of the target point and a description of obstacles. The local raster map describes the area closest to the robot. Using numerical research, the effectiveness of this form of data representation for a precise neural network is shown, compared with the raster representation of the map. In this article, two structures of neural networks are studied, one of which uses 8 possible directions of movement, and the other uses 3 possible directions of movement. It is shown that when using 3 possible directions, the cycling of trajectories planned by the neural network is eliminated, which leads to an increase in accuracy.

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

AbstractDiverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

Machine learning-driven discovery of high-performance MEMS disk resonator gyroscope structural topologies

The design of the microelectromechanical system (MEMS) disc resonator gyroscope (DRG) structural topology is crucial for its physical properties and performance. However, creating novel high-performance MEMS DRGs has long been viewed as a formidable challenge owing to their enormous design space, the complexity of microscale physical effects, and time-consuming finite element analysis (FEA). Here, we introduce a new machine learning-driven approach to discover high-performance DRG topologies. We represent the DRG topology as pixelated binary matrices and formulate the design task as a path-planning problem. This path-planning problem is solved via deep reinforcement learning (DRL). In addition, we develop a convolutional neural network-based surrogate model to replace the expensive FEA to provide reward signals for DRL training. Benefiting from the computational efficiency of neural networks, our approach achieves a significant acceleration ratio of 4.03 × 105 compared with FEA, reducing each DRL training run to only 426.5 s. Through 8000 training runs, we discovered 7120 novel structural topologies that achieve navigation-grade precision. Many of these surpass traditional designs in performance by several orders of magnitude, revealing innovative solutions previously unconceived by humans.

Path Planning for Mobile Robots Based on the Improved DAPF-QRRT* Strategy

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety.

Interactive strategy of shared control vehicle and automated vehicles based on adaptive non-cooperative game

Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.

Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications.

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease of falling into the local optimum, a multi-strategy improved chameleon optimization algorithm (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize the chameleon population to improve the diversity of the initial population. Secondly, in the prey-search stage, the sub-population spiral search strategy was introduced to improve the global search ability and optimization accuracy of the algorithm. Then, considering the blindness of chameleon's eye turning to find prey, the Lévy flight strategy with cosine adaptive weight was combined with greed strategy to enhance the guidance of random exploration in the eyes' rotation stage. Finally, a nonlinear varying weight was introduced to update the chameleon position in the prey-capture stage, and the refraction reverse-learning strategy was used to improve the population activity in the later stage so as to improve the ability of the algorithm to jump out of the local optimum. Eighteen functions in the CEC2005 benchmark test set were selected as an experimental test set, and the performance of ICSA was tested and compared with five other swarm intelligent optimization algorithms. The analysis of the experimental results of 30 independent runs showed that ICSA has stronger convergence performance and optimization ability. Finally, ICSA was applied to the UAV path-planning problem. The simulation results showed that compared with other algorithms, the paths generated by ICSA in different terrain scenarios are shorter and more stable.

Multi-UAV Path Planning for Inspection of Target Points with Stable Monitoring Frequencies

In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target points, and ultimately meet the monitoring requirements with regular and stable monitoring frequencies. Herein, we present MTSP-R, a novel variant of the multiple traveling salesmen problem (MTSP), in which revisits to important target points are allowed. We address the path-planning problem of multi-UAV in two stages. First, we propose a nearest insertion algorithm with revisits (NIA-R) to determine the number of required UAVs and initial inspection paths. We then propose an improved genetic algorithm (IGA) with two-part chromosome encoding to further optimize the inspection paths of the UAVs. The simulation results demonstrate that the IGA can effectively overcome the shortcomings of the original genetic algorithm, providing shorter paths for multiple UAVs and more stable monitoring frequencies for the target points.

An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems

The Snow Ablation Optimizer (SAO) is an advanced optimization algorithm. However, it suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose an Enhanced Snow Ablation Optimization algorithm (ESAO). Initially, an adaptive T-distribution control strategy is employed to improve the algorithm's exploratory position adjustments, facilitating the identification of the global optimum. Furthermore, we introduce a Cauchy mutation strategy, endowing individuals with a robust capability to escape local extrema and steer the population towards more favorable directions. A leader-based boundary control strategy is also proposed to enhance the optimizer's search performance, significantly increasing the accuracy, speed, and stability of the algorithm in tackling complex problems. To validate the performance of ESAO, we utilize 29 CEC2017 benchmark functions for comparison against eight popular algorithms across various dimensions. Our algorithm ranked first in all comparisons, demonstrating ESAO's effectiveness. Additionally, to evaluate the practical applicability of the proposed method, we mathematically modeled the UAV swarm and solved the UAV swarm path planning problem using various competitor algorithms. Furthermore, we applied different competitor algorithms to two engineering design problems. The results demonstrate that ESAO performs the best. In general, ESAO outperforms its counterparts in terms of solution quality and stability, showcasing its superior application potential.

Multi-UAV path planning using DMGWO ensuring 4D collision avoidance and simultaneous arrival

PurposeMulti-unmanned aerial vehicle (UAV) systems have succeeded in gaining the attention of researchers in diversified fields, especially in the past decade, owing to their capability to operate in complex scenarios in a coordinated manner. Path planning for UAV swarms is a challenging task depending upon the environmental conditions, the limitations of fixed-wing UAVs and the swarm constraints. Multiple optimization techniques have been studied for path-planning problems. However, there are local optimum and convergence rate problems. This study aims to propose a multi-UAV cooperative path planning (CoPP) scheme with four-dimensional collision avoidance and simultaneous arrival time.Design/methodology/approachA new two-step optimization algorithm is developed based on multiple populations (MP) of disturbance-based modified grey-wolf optimizer (DMGWO). The optimization is performed based on the objective function subject to multi constraints, including collision avoidance, same minimum time of flight and threat and obstacle avoidance in the terrain while meeting the UAV constraints. Comparative simulations using two different algorithms are performed to authenticate the proposed DMGWO.FindingsThe critical features of the proposed MP-DMGWO-based CoPP algorithm are local optimum avoidance and rapid convergence of the solution, i.e. fewer iterations as compared to the comparative algorithms. The efficiency of the proposed method is evident from the comparative simulation results.Originality/valueA new algorithm DMGWO is proposed for the CoPP problem of UAV swarm. The local best position of each wolf is used in addition to GWO. Besides, a disturbance is introduced in the best solutions for faster convergence and local optimum avoidance. The path optimization is performed based on a newly designed objective function that depends upon multiple constraints.

Autonomous Underwater Vehicle Path Planning Based on Improved Salp Swarm Algorithm

Aiming at the problem of path planning for autonomous underwater vehicle (AUV) to cope with the influence of obstacles and eddies in complex marine environments, a path planning method based on an improved salp swarm algorithm (ISSA) is proposed. Firstly, the motion model of the AUV and eddy current model are constructed, including the relationship between position, velocity, attitude, and control inputs. Secondly, the improved SSA is proposed, which introduces the Levy flight strategy to enhance the algorithm’s optimization seeking ability and adds a nonlinear convergence factor to enhance the convergence ability of the algorithm. The stability and robustness of the improved algorithm are verified by test functions. Finally, the ISSA is applied to AUV path planning, which optimizes the AUV travel distance, improves the search efficiency and accuracy, and avoids the local optimum of the algorithm. The ISSA enhances the adaptive ability and robustness of the algorithm by introducing a dynamic adjustment strategy and feedback mechanism. Experimental verification is carried out using a simulated marine environment. The results show that the ISSA is better than the traditional algorithm in terms of path length as well as algorithm stability, and can effectively improve the navigation performance of AUV.

A practical path planning method for optimal repair paths between multiple small-size defects

PurposeThis study aims to solve the problem of repair path planning between multiple small-size defects in the field of additive manufacturing (AM) repair by using Python-based ant colony algorithm (ACO). The optimal parameter combination scheme is obtained by discussing the influencing factors of parameters in the ACO.Design/methodology/approachThe effects of the information heuristic factor α, the expected heuristic factor ß and the pheromone volatile factor ρ on the simulation results were investigated by designing a three-factor and three-level orthogonal experiment. The fast convergence of ACO in finding the optimal solution of multiple small-size defect repair path problem is proved by comparing the simulation results with those of genetic algorithm (GA) on the same data set.FindingsThe ACO can effectively solve the repair path planning problem between multiple small-size defects by optimizing the parameters. In the case of 50 defect locations, the simulation results of the ACO with optimized parameters are 159.8 iterations and 3,688 average path lengths, while the GA has 4,027.2 average path lengths under the same data set and the same number of iterations, and by comparison, it is proved that the ACO can find the optimal solution quickly in the small-size defects repair path planning problem, which greatly improves the efficiency of defect repair.Originality/valueThe parameter-optimized ACO can be quickly applied to the planning problem of repair paths between multiple small-size defects in the field of AM repair, which can better improve the defect repair efficiency and reduce the waste of resources.

Reinforcement Learning-Based Energy-Saving Path Planning for UAVs in Turbulent Wind

The unmanned aerial vehicle (UAV) is prevalent in power inspection. However, due to a limited battery life, turbulent wind, and its motion, it brings some challenges. To address these problems, a reinforcement learning-based energy-saving path-planning algorithm (ESPP-RL) in a turbulent wind environment is proposed. The algorithm dynamically adjusts flight strategies for UAVs based on reinforcement learning to find the most energy-saving flight paths. Thus, the UAV can navigate and overcome real-world constraints in order to save energy. Firstly, an observation processing module is designed to combine battery energy consumption prediction with multi-target path planning. Then, the multi-target path-planning problem is decomposed into iterative, dynamically optimized single-target subproblems, which aim to derive the optimal discrete path solution for energy consumption prediction. Additionally, an adaptive path-planning reward function based on reinforcement learning is designed. Finally, a simulation scenario for a quadcopter UAV is set up in a 3-D turbulent wind environment. Several simulations show that the proposed algorithm can effectively resist the disturbance of turbulent wind and improve convergence.

Improved Whale Optimization Algorithm for Maritime Autonomous Surface Ships Using Three Objectives Path Planning Based on Meteorological Data

In recent years, global trade volume has been increasing, and marine transportation plays a significant role here. In marine transportation, the choice of transportation route has been widely discussed. Minimizing fuel consumption, minimizing voyage time, and maximizing voyage security are concerns of the International Maritime Organization (IMO) regarding Maritime Autonomous Surface Ships (MASS). These goals are contradictory and have not yet been effectively resolved. This paper describes the ship path-planning problem as a multi-objective optimization problem that considers fuel consumption, voyage time, and voyage security. The model considers wind and waves as marine environmental factors. Furthermore, this paper uses an improved Whale Optimization Algorithm to solve multi-objective problems. At the same time, it is compared to three advanced algorithms. Through seven three-objective test functions, the performance of the algorithm is tested and applied in path planning. The results indicate that the algorithm can effectively balance the fuel consumption, voyage time, and voyage security of the ship, offering reasonable paths.

Constrained path planning for manned–unmanned rotorcraft teaming in emergency medical service missions

This paper investigates the path-planning problem applied to an innovative Unmanned Air Vehicle teaming with a helicopter to increase safety during Helicopter Emergency Medical Services operations. The unmanned vehicle, a drone that optionally can be launched from the helicopter, has the mission to explore the area of operation to determine the meteorological and environmental conditions and to detect physical obstacles. It is initially found that the combination of probabilistically optimal Rapidly-exploring Random Tree (RRT∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{*}$$\\end{document}) as the global planner and of Bidirectional Rapidly-exploring Random Tree (BiRRT) as the local planner provides a nearly optimal global path and a rapid replanning in case new obstacles are detected. Adopting a Savitzky–Golay filter in an optional post-processing phase enables trajectory smoothing, thus improving its practicability. The feasibility of the identified trajectory for a rigid-body helicopter model is assessed by computing a first estimate of attitude, forces, control inputs, and rotor power from the trajectory points and curvature. This assessment shows that the RRT∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{*}$$\\end{document} used as a local planner provides replanned trajectories more feasible than BiRRT with comparable computational times.

A hierarchical solution framework for dynamic and conflict-free AGV scheduling in an automated container terminal

Container terminals worldwide are experiencing their transitions into automated and intelligent terminals in the face of the ever increasing container handling demand and cost pressure. A key to cost-effective operations in automated container terminals is the efficient AGV scheduling algorithm that enables on-time fulfillment of container loading and discharging tasks. In this paper, we study an integrated task assignment and path planning problem for AGV scheduling in an automated container terminal. We propose a hierarchical solution framework to empower dynamic AGV scheduling, where the higher level employs a reinforcement learning algorithm for dynamic task assignment and the lower level makes use of a tailored path generation algorithm to generate low-cost and conflict-free paths for AGVs to serve the tasks. Additionally, we propose a container matching heuristic and a two-layer grid map to enhance the learning ability of the reinforcement learning algorithm. We compare the performance of the hierarchical solution framework against various benchmark methods on problem instances of practical scales. The results show that our approach is effective in reducing task delays and mitigating path conflicts, making the task assignment and path planning decisions more applicable for AGV scheduling in an automated container terminal.

Cooperative path planning study of distributed multi-mobile robots based on optimised ACO algorithm

The rapid development of robotics technology has driven the growth of robot types and the development of related technologies. As an important aspect of robot research, path planning technology plays an irreplaceable role in practical production and application. Ant colony algorithm has a wide range of applications in robot path planning, but there is also a problem of performance overly relying on initial parameter selection. In order to solve this problem and improve the performance of mobile robot path planning, an improved ant colony algorithm based on firefly algorithm was studied and designed in a two-dimensional environment. In order to further explore the performance of ant colony algorithm in solving robot coordinated path planning problems, an improved ant colony algorithm based on heuristic function was also designed. In a three-dimensional environment, an improved ant colony algorithm based on the improved artificial potential field method was designed. The research results show that the maximum running time of the improved ant colony algorithm based on the firefly algorithm in different grid environments is 819.36 s, 847.01 s, and 811.54 s, respectively. The average running time of the improved ant colony algorithm based on heuristic function in different grid environments is 5.19 s, 5.97 s, and 9.09 s, with average path lengths of 29.90 cm, 31.08 cm, and 37.01 cm, and path length variances of 0.35, 0.87, and 2.21, respectively. The ant colony algorithm based on the improved artificial potential field method has a running time of 1.930 s, 3.182 s, and 4.662 s in different grid environments, and a path length of 29.275 cm, 49.447 cm, and 67.057 cm, respectively. The ant colony algorithm for research and design optimization has good performance. The contribution of the research lies in the design of three path planning methods for mobile robots, including two-dimensional path planning and three-dimensional path planning, which improves the time of path planning and shortens the average path length. The novelty of the research is reflected in the design of a path planning method for mobile robots in two-dimensional and three-dimensional environments, which improves the ant colony algorithm through firefly algorithm and heuristic function, and combines the ant colony algorithm with the improved artificial potential field method. The method designed by the research institute can provide technical support for path planning of mobile robots.

Simulation of Dynamic Path Planning of Symmetrical Trajectory of Mobile Robots Based on Improved A* and Artificial Potential Field Fusion for Natural Resource Exploration

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path.

Unmanned Ground Vehicle Path Planning Based on Improved DRL Algorithm

Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, insufficient exploration, and unstable training, this paper proposes an improved algorithm called Dual Priority Experience and Ornstein–Uhlenbeck Soft Actor-Critic (DPEOU-SAC) based on Ornstein–Uhlenbeck (OU noise) and double-factor prioritized sampling experience replay (DPE) with the introduction of expert experience, which is used to help the agent achieve faster and better path planning and obstacle avoidance. Firstly, OU noise enhances the agent’s action selection quality through temporal correlation, thereby improving the agent’s detection performance in complex unknown environments. Meanwhile, the experience replay is based on double-factor preferential sampling, which has better sample continuity and sample utilization. Then, the introduced expert experience can help the agent to find the optimal path with faster training speed and avoid falling into a local optimum, thus achieving stable training. Finally, the proposed DPEOU-SAC algorithm is tested against other deep reinforcement learning algorithms in four different simulation environments. The experimental results show that the convergence speed of DPEOU-SAC is 88.99% higher than the traditional SAC algorithm, and the shortest path length of DPEOU-SAC is 27.24, which is shorter than that of SAC.

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

PurposeA fleet of mobile robots has been effectively used in various application domains such as industrial plant inspection. This paper proposes a solution to the combined problem of task allocation and motion planning problem for a fleet of mobile robots which are requested to operate in an intelligent industry. More specifically, the robots are requested to serve a set of inspection points within given service time windows. In comparison with the conventional time windows, our problem considers fuzzy time windows to express the decision maker’s satisfaction for visiting an inspection point.Design/methodology/approachThe paper develops a unified approach to the combined problem of task allocation and motion planning for a fleet of mobile robots with three objectives: (a) minimizing the total travel cost considering all robots and tasks, (b) balancing fairly the workloads among robots and (c) maximizing the satisfaction grade of the decision maker for receiving the services. The optimization problem is solved by using a novel combination of a Genetic Algorithm with pareto solutions and fuzzy set theory.FindingsThe computational results illustrate the efficiency and effectiveness of the proposed approach. The experimental analysis leverages the potential for using fuzzy time windows to reflect real situations and respond to demanding situations.Originality/valueThis paper provides trade-off solutions to a realistic combinatorial multi-objective optimization problem considering concurrently the motion and path planning problem for a fleet of mobile robots with fuzzy time windows.

Path Planning of UAV Using Levy Pelican Optimization Algorithm In Mountain Environment

ABSTRACT To overcome the issues of simple strategy and easy fall into local optimum when solving UAV 3D path planning problem, we propose an improved Levy Pelican Optimization Algorithm (LPOA) that incorporates the hunter-prey algorithm and solves the UAV 3D path planning problem based on the improved LPOA. Based on the original algorithm, on the one hand, we introduce Levy flight and adaptive parameters to update the pelican position, balance the algorithm development phase and exploration phase, and effectively avoid the problem of falling into local optimum at the early stage of the algorithm. On the other hand, we use chaotic mapping to initialize the population, integrate the prey generation strategy to enhance the prey generation formula and improve algorithm orientation and convergence speed. The test results are compared and analyzed with those of classical, current mainstream, and recently released swarm intelligence optimization algorithms. Using 3D mountain range model experiments, the algorithm is compared before and after optimization, and the improved optimization algorithm is then applied to the actual problem of UAV 3D path planning. Research has found that the improved LPOA optimizes the total target cost of the algorithm by 9.75% and optimizes the planned average shortest path by 20.30%.