Path Points Research Articles

Study on Propeller Positioning and Trajectory Control of Underwater Robots Based on Bayesian Inference and Multi-Objective Optimization

In ocean exploration, the positioning and trajectory optimization of underwater vehicles are critical research areas that directly impact the success of ocean resource exploration, environmental monitoring, and search and rescue missions. However, the complex and uncertain marine environment poses significant challenges. This study first determines the three-dimensional spatial position of the underwater vehicle, calculates the vehicle's speed in the marine environment through mechanistic analysis, traversing the initial position, velocity, acceleration, angular velocity and angular acceleration of the submersible, fits the motion trajectory using a random walk, and uses Bayesian inference to estimate maximum probability positions of submersibles and select the optimal initial deployment point. To better cope with real-world scenarios involving ocean currents and the diverse motion modes of underwater vehicles, this study further used an improved genetic algorithm to divide the motion trajectory of the submersible into a series of discrete path points, and constructed a multi-objective optimization model with the shortest search and rescue time and the maximum probability of successful search and rescue as the objective function to realize the precise positioning of the lost underwater vehicle.

A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning

Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability.

Path planning strategies for logistics robots: Integrating enhanced A‐star algorithm and DWA

AbstractPath planning is the key part in the process of transportation conducted by logistics robots, and there often exist some problems with it. The path designed is not always smooth enough and its search efficiency is low, for example. As a common global path planning algorithm, A‐star is based on the traditional algorithm, which is unable to solve the problem of uneven path in the movement of logistics robots. Through improving the heuristic function of the traditional A‐star algorithm, weighing the heuristic function dynamically, removing the redundant points of the traditional star algorithm path with Floyd algorithm, and setting a safe distance to prevent the logistics robot from collision at the same time, the path is finally curved to be more appropriate to the movement path of the logistics robot. The MATLAB simulation of A‐star algorithm before and after the improvement shows that the turning points of the advanced A‐star algorithm reduced 61.5% on average compared to the traditional algorithm. The path length decreased 2.4% and the traversing points reduced 58.5%. At the same time, the DWA algorithm introduces dynamic weight coefficients, which can dynamically adjust the weight coefficients when encountering obstacles, so as to safely reach the target point.

Global Path Optimal Planning Method for Autonomous Vehicles Based on Image Feature Extraction

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference.

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.

Motion capture and AR based programming by demonstration for industrial robots using handheld teaching device

In the industrial robots field, efficient and convenient programming methods have been a hot research topic. In recent years, immersive simulation technology has been developing rapidly in many fields, which provides new horizons for the development of industrial robots. This paper presents a HTC VIVE laser scan motion capture and Holohens augmented reality (AR) based interactive Programming by Demonstration (PbD) system for industrial robot. A portable Handheld Teaching Device (HTD) and its calibration algorithm are designed in the system. The portable HTD which is tracked by a laser motion capture system can be viewed as an AR robot end-effector to teach paths. Meanwhile, the AR robot can be simulated in real time during programing. In addition, the robot reproducing the operator’s actions at the same position in space is the focus of programming. So, Multi-system registration methods are proposed to determine the relationship between robot systems, motion capture systems and virtual robot systems. Meanwhile, a path planning algorithm is proposed to convert the captured raw path points into robot-executable code. For unskilled operators, they can easily perform complex programming using the HTD. For skilled senior workers, their skills can be quickly learned by robots using the system.

Target setting and practical Improvement for sound quality of electric vehicles

For electric vehicles, because there is no masking effect of combustion engine noise, the middle frequency and high frequency noise of electric components become distinct and annoying. Electric driven unit, electric compressor, electric pump, and motors of seat adjusting device are always major noise sources for electric vehicles. Some of those noise signals are steady, some are transient and some are time-variable. So only SPL is hard to reflect human's true perception. Psychoacoustic metrics, such as loudness, roughness, sharpness, and tonality, is hard to evaluate all complicated NVH phenomenon of electric vehicles using one metrics. In this paper, the author tested different operation noises of above-mentioned electric components of several popular EVs, and finalized proper psychoacoustic metrics for different operations based on loudness, modulation, tonality and objective evaluation. Furthermore, the author studied target setting for dedicated metrics, and effective improving methods for sound quality of EVs from the point of source and path. For example, effective designing for acoustic encapsulation of EDU and electric compressor, and good insulation performance designing for dash inner and trunk floor trim. Keywords: Sound quality, Psychoacoustic metrics, electric vehicle, objective evaluation, target setting

Multi-agent long-distance end-to-end indoor navigation: using imitation learning pre-training and global map

Abstract To address the navigation challenges of mobile robot swarm coordination in complex environments, this paper presents a distributed navigation approach for mobile robot swarm. The study initially formulates the problem of robot swarm navigation as a partially observable Markov decision process, employing a distributed proximal policy optimization algorithm to train decision models mapping observations to actions directly. Furthermore, it adopts imitation learning pre-training to maximize expert decisions in the initial training phase, thereby reducing exploration space. Lastly, an improved path point generation algorithm along with a spatiotemporal reachability calculation method is proposed to guide the Deep Reinforcement Learning policy in accomplishing long-distance indoor navigation. Comprehensive performance evaluations are conducted in simulated environments to compare the proposed method with existing approaches. Results demonstrate that the proposed approach effectively enhances both convergence speed and model performance, while significantly improving the navigation capabilities of robot swarms in complex environments.

An optimized method for AUV trajectory model in benthonic hydrothermal area based on improved slime mold algorithm

The optimization of the desired autonomous underwater vehicle (AUV) trajectory modeling and AUV trajectory tracking control in the benthonic hydrothermal area were studied. In the conventional trajectory tracking model construction methods, the time points were roughly combined with the position points of the planned path, making it difficult to produce a smooth trajectory. Although the spline interpolation method was an ideal option for smooth curves, a great number of points were needed for a complex desired trajectory mode. In response to the demanding requirements of AUV trajectory tracking control in the benthonic hydrothermal area, an under-actuated test platform was first established, and the cubic spline interpolation was adopted to process the preset path points for a smooth desired trajectory. An improved slime mold algorithm (SMA) was put forward to optimize the interpolating points used in the trajectory modeling. The Levy flight technology and the compactness technique to speed up the search process and increase the search accuracy. The simulation experiments were conducted in comparison with the artificial fish swarm algorithm (AFSA), the particle swarm optimization (PSO), and the compact cuckoo search (CCS). The results showed that the improved SMA shortened the search process, effectively avoided the local extreme values, and generated a high-precision desired trajectory model in a shorter time. The pool test also verified the feasibility and effectiveness of the proposed method. The method proposed in this study can satisfy the modeling of benthonic hydrothermal trajectory with a fewer number of nodes, faster search progress and search accuracy.

Transition from synchronous to asynchronous mechanisms in 1,3-dipolar cycloadditions: a polarizability perspective.

This study investigates the energetic and polarizability characteristics of three 1,3-dipolar cycloaddition reactions between diazene oxide and substituted ethylenes, focusing on the transition from synchronous to asynchronous mechanisms. Synchronicity analysis, using the reaction force constant, indicates that the bond evolution process becomes increasingly decoupled as the number of cyano groups increases. Polarizability analysis reveals that isotropic polarizability reaches its maximum near the transition state in all cases, while anisotropy of polarizability shifts from the transition state toward the product direction as asynchronicity increases. The larger the shift, the more asynchronous the mechanism, as reflected by the weight of the transition region. A detailed examination of the parallel and perpendicular polarizability components to the newly formed sigma bonds shows that the evolution of the parallel component is closely aligned with the energetic changes along the reaction coordinate, particularly in the synchronous reaction. We have also identified a relationship between the displacement in the maximum state of the parallel component from the transition state and the synchronicity of the mechanism. The larger the displacement, the more asynchronous the mechanism. These findings suggest that asynchronous 1,3-dipolar cycloaddition mechanisms are characterized by a decoupling of isotropic and anisotropic polarizabilities and a shift in the maximum polarizability state of the parallel component toward the product direction. Density functional theory calculations were performed at the B3LYP/6-311 + + G(d,p)//B3LYP/6-31G(d,p) level of theory. The polarizability was calculated at each point of the reaction path, obtained using the intrinsic reaction coordinate method, as implemented in Gaussian 16.

Research on Trajectory Planning of Autonomous Vehicles in Constrained Spaces.

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments.

Analysis of Polynomial Interpolation Characteristics Based on 6DOF Manipulator Trajectory Planning

Aiming at the problems of choosing and using polynomials of different times and mixed polynomials in the process of manipulator trajectory planning, such as difficulty, wide range of light and low accuracy, in this paper, the application of different polynomials in manipulator trajectory planning is systematically analyzed by using MATLAB Robot Toolbox simulation platform and Polynomial interpolation algorithm. Using a 6-dof robotic arm Puma560 as a simulation object, the effects of three-, five-, and seven-Polynomial interpolation on joint position and pose in joint space trajectory planning were analyzed, a preliminary understanding of the application of different polynomials, and secondly, the more complex application of the fifth and seventh degree polynomials in Descartes space for the trajectory planning of the manipulator, the influence of trajectory planning with different degree polynomials on the position and pose of manipulator joint is clarified. The results show that the higher the number of polynomials, the smoother the transition of the joint at the critical path points, but the greater the maximum angular velocity and angular acceleration of the joint, the Polynomial interpolation of joint space and Descartes space trajectory planning are 14% and 23% lower than the average of absolute maximum angular velocity and absolute maximum angular acceleration of the Polynomial interpolation, respectively. Based on the analysis of the research results, this paper presents the applicable conditions and applications of Polynomial interpolation method in trajectory planning, and further summarizes the application conditions and methods of mixed polynomials.

3D winding path modeling method with fiber overlap effect

The fibers will repeatedly cross and stack on the liner during the continuous winding process, which poses a challenge in creating accurate local and global structure models. To address the issue, a 3D winding path modeling method is proposed to simulate the fiber overlap effect. First, the initial path is offset equidistantly along the direction of width and thickness to construct the 3D path points. Then, by calculating the overlap thickness, the overlapped fibers are raised to the corresponding overlap thickness to eliminate interference in the 3D path points. Finally, a 3D winding path with the fiber overlap effect is obtained by solving the suspended points before and after the overlapping area to describe the behavior of fibers separating from the liner surface under tension. The proposed method is applied to various winding patterns and liner shapes, and the results show that the established 3D winding path can accurately simulate the fiber overlap effect and correspond with the actual local and global winding structures.

Optimization method for collision avoidance paths of inland ships based on improved ant colony algorithm

With the rise of artificial intelligence, ships are gradually becoming intelligent. Ship path planning, as the foundation of ship intelligence, has become a current research hotspot. To achieve the planning of ship collision avoidance paths, a hybrid ant colony algorithm and artificial potential field for optimal safety path planning was proposed. The model makes up for the disadvantages of the slow convergence rate and the easy local optimum. At the same time, the ant colony algorithm in turn makes up for the poor global search ability of the artificial potential field method, so that the algorithm can achieve accurate path planning. The test results show that in a simple environment, the hybrid ant colony algorithm, improved tangent and Dijkstra algorithm, and improved fast extended random tree * algorithm iterated about 7, 25, and 40 times respectively before starting to converge; In complex environments, the improved tangent and Dijkstra algorithms do not converge, while the improved fast expanding random tree * algorithm and hybrid ant colony algorithm iterate about 40 and 8 times respectively to begin convergence. It is clear that the mixed ant colony algorithm converges fast and can obtain the optimal path in the shortest time. The number of unsafe path points for the optimal path in a simple environment is 6, 1, and 0, respectively, for the improved tangent and Dijkstra algorithm, the improved fast expanding random tree * algorithm, and the hybrid ant colony algorithm; The number of unsafe path points for the optimal path in a complex environment is 9, 3, and 0, respectively; It can be seen that the path planned by the hybrid ant colony algorithm has higher security. The above results show that the mixed ant colony algorithm can effectively shorten the search time of optimal paths while improving the security of pathways.

Research on dynamic positioning of navigation system using K nearest neighbor algorithm and bayesian fusion based on visible light communication

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m×5 m×4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m×1 m×1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Research on Project-Based Teaching Strategies for the Chinese Curriculum of International Baccalaureate (IB) based on the SAMR Model

In the digital era, there have been significant changes in international students’ Chinese language learning patterns and methods. The integration of information technology and project-based teaching not only promotes the demand for applications and learning media but also provides new opportunities to enhance student’s critical thinking abilities. However, project-based teaching of information technology and Chinese is prone to the misconception of seeking the most suitable application or learning medium for verification. The SAMR (Substitution, Augmentation, Modification and Redefinition) model is an educational model based on Bloom’s taxonomy of educational objectives for “creative” teaching. This information technology-integrated teaching method provides students with diverse learning experiences. The Chinese language course of the International Baccalaureate (IB) aims to cultivate students’ profound understanding of literature, language, and culture by providing a critical exploration learning experience in the field of language and literature. It encourages students to explore similarities and differences between different cultures, delve into topics or works of interest, and conduct independent language and literature research projects. The project-based learning approach helps students meet their further academic research and personal growth skills needs, cultivating them as language users and lifelong learners who are willing to explore and have critical thinking skills. The application exploration of the SAMR model in project-based teaching proposes relevant suggestions for the integration of information technology and Chinese teaching in the digital era and provides some new focus points for effective strategies and paths of IB Chinese teaching: encouraging teachers to improve students’ skills in collaboration, communication, creativity, and critical thinking (4C) through advanced teaching at four different levels of SAMR model, flexible use of teaching rounds, creation of GRASPS teaching evaluation scenarios, and design of conceptual driving tasks, in order to achieve the improvement of language and literature construction and application abilities, and further promote the collaboration and autonomy of IB Chinese project-based learning process.

Research on time-energy optimal trajectory planning of articulated heavy-duty robot

Articulated heavy-duty robots are more and more widely used in the construction environment. Aiming at the problems of low working efficiency and high energy consumption of this kind of robot, a multi-objective adaptive trajectory planning method combining path optimization and trajectory optimization is proposed to improve the working efficiency of the robot and reduce energy consumption. Firstly, based on kinematics and dynamic analysis considering friction, the energy consumption model of robot trajectory is constructed. Then, according to the posture of the key points in the known workspace, the optimal path points in the joint space are obtained by a path point solution method considering the joint load characteristics. On this basis, a multi-objective model considering running time and energy consumption is established to plan the interpolation trajectory of the joint space of the quintic B-spline curve. Finally, constrained by the velocity, acceleration and torque of the joint of the manipulator, the optimal solution is obtained by using the elite non-dominated sorting genetic algorithm (NSGA-II), and the optimal weight factor is selected by a multi-objective adaptive optimization method to obtain the optimal position-time series. The experimental results show that compared with the quintic polynomial trajectory optimization method, the energy consumption of the proposed method is reduced by 10.90% under the same working efficiency. The research results of this paper provide a new optimization idea for other target trajectory planning.

Research on multi-vehicle formation control based on improved artificial potential field method

Multi-vehicle formation can perform various special tasks in unstructured environment. How to take into account the safety of vehicles in avoiding obstacles and the ability to maintain formation has a certain research value. In this paper, the four-circle model of vehicle is established first, and the circle radius is adjusted according to the state of vehicle, so as to describe the safety boundary of vehicle. The improved RRT algorithm is used for the whole route planning, and the discrete path points are used as vehicle guidance. Then the artificial potential field is constructed, and the formation coordination potential field is proposed, so that the vehicles can cooperate with other vehicles to keep the preset formation as far as possible when avoiding obstacles. Then the control quantity of the vehicle is calculated according to the force condition of the vehicle in the potential field by the double exponential sliding mode control method. Finally, the effectiveness of the method is verified by the simulation experiments of triangle formation and circular formation under different working conditions, and the formation error is reduced by about 20%.

An approach to robot welding path autonomous planning of the intersection weldment based on 3D visual perception

To improve the accuracy, an approach to robot welding path planning of the intersection weldment based on 3D visual perception is proposed. It adopts the two-stage location scheme of “global 3D reconstruction + local scanning correction”. First, this paper proposes an improved K-means clustering method based on the geometric features of workpiece point-cloud, to achieve accurate and fast point-cloud segmentation. Second, the intersection parameters of workpiece can be quickly calculated by the proposed parameter identification method. Third, this paper plans the scanning path of laser scanner according to the identified parameters, and puts forward an algorithm of weld path points recognition combined with the actual weldments state. Then, the pose of robot welding torch is obtained by using spline fitting algorithm and welding torch attitude estimation algorithm. Finally, the experiments show the path planning error is about 0.3 mm, which has increased a lot than the published methods.

Exploring pre-service primary teachers’ emotions in a geometry project with 3D design

This study explores the emotions that pre-service teachers (PSTs) experience when they interact with geometric knowledge and 3D design in a project focused on the way of Saint James pilgrimage route. The sample was made up of 101 PSTs from the University of Santiago de Compostela, the final point of the pilgrimage path. An emotional questionnaire, together with a participant observation notebook and a rubric, served as data collection instruments. The analytical frameworks included the didactical suitability criteria for mediational and affective facets, and SAMR (substitution, augmentation, modification, and redefinition) model for the effective integration of technology in schools. The results show a greater presence of pleasant emotions, highlighting the emotions of curiosity and cheerfulness. Despite this, less pleasant emotions such as brain-taster or bewilderment were notable. Pleasant emotions shown, as well, higher correlation rates. In conclusion, 3D design seems to indicate great potential for working on emotions with this group of students.