Traditional Path Planning Research Articles

Research on an Intelligent Learning Path Planning Method for the VR-SimTrainer Virtual Reality Training Software Based on Deep Learning

This study explores an intelligent learning path planning method for the VR-SimTrainer virtual reality training software based on deep learning. With the increasing application of virtual reality technology in education and vocational training, the traditional fixed learning path training method can no longer meet the needs of personalized learning. To address this problem, this paper introduces deep learning technology to dynamically adjust the learning path by analyzing the real-time operation data and learning behavior of the trainees, providing a personalized training experience for the trainees. Experimental results show that compared with traditional path planning methods, the intelligent learning path planning method based on deep learning can significantly improve the learning effectiveness and efficiency of trainees. In addition, the research also reveals some challenges faced in the application of this method, such as the high data requirements and the limitations of model generalization ability. Overall, this study provides new ideas for the intelligent development of virtual reality training software and demonstrates the potential value of deep learning in personalized education and adaptive learning.

3D Voxel-based Path Planning for AVs in Dynamic Complex Environments

Abstract. With the rapid advancement of science and technology, autonomous driving technology has been widely applied in various fields such as the automotive industry, intelligent warehousing, and emergency rescue. Traditional 2D path planning has limitations in dealing with the complex and dynamic environments of the real world, making it difficult to meet the high precision and complex path planning requirements of autonomous vehicles. With the development of data acquisition technology, large-scale point cloud data has become easily accessible, which can better extract the three-dimensional complex of the environment. Nevertheless, the unstructured nature and vast amount of point cloud data make path planning on it extremely challenging. Voxel models can effectively compress point cloud data and provide neighboring topological structures. In view of this, we propose a voxel-based framework for the path planning of autonomous vehicles in dynamic and complex 3D environments, which involves converting point cloud data into voxels and extracting navigation spaces. Subsequently, deep reinforcement learning techniques are utilized to achieve obstacle avoidance and navigation on the voxel map. It is expected that this framework will contribute to the development of 3D path planning and enhance the utilization of point cloud data in the navigation field.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

Path planning and collision avoidance approach for a multi-agent system in grid environments

Abstract In multi-robot systems, ensuring efficient, safe, and conflict-free navigation for each robot is crucial, especially in scenarios requiring the coordination of complex tasks and interactions with the environment. Currently, most related research focuses on simplified grid environments, making it difficult to apply these findings to real-world scenarios. This paper introduces actual physical constraints into traditional grid environment path planning and designs a set of motion rules suitable for grid environments and a collision avoidance algorithm. We also propose a novel multi-agent path planning approach that integrates deep reinforcement learning with collision avoidance algorithms. Through a series of experiments, we have demonstrated that this method effectively guides agents in navigating from their starting points to their targets within grid environments that possess physical constraints, and significantly reduces the probability of collisions when used in conjunction with our collision avoidance algorithm. These findings not only showcase the efficacy of our approach but also open new avenues for deploying multi-agent systems in more complex real-world scenarios.

Research on path planning for clear aligner flexible manufacturing

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.

Research on Additive Manufacturing Path Planning of a Six-Degree-of-Freedom Manipulator

The research on additive manufacturing (AM) path planning mainly focuses on the traditional three-axis AM path planning and five-degree-of-freedom (DOF) AM path planning, while there is less research on six-DOF AM path planning. In the traditional AM path planning algorithm, the filling path is discontinuous and there is long straight-line printing in a certain direction, which can easily lead to warpage deformation. Therefore, in this work, the six-DOF manipulator is taken as the main object to build an AM platform, and the mechanism of AM path planning of the manipulator is studied. The path planning algorithm combining the contour offset filling method and Hilbert curve filling is optimized by using a cubic uniform B-spline curve, and an AM path planning algorithm suitable for a six-DOF manipulator is obtained. A continuous printing path can be generated by this algorithm. It reduces the existence of long straight-line printing in a certain direction, thereby reducing the warpage deformation of the model and improving the molding quality of the model. The traditional three-axis AM device and the six-DOF AM platform were used to print two kinds of models. By comparing the printing time, the six-DOF AM platform was 43.70% and 37.94% shorter than the traditional three-axis AM device. The same model was printed on a six-DOF AM platform by using the parallel scanning filling method, the path planning algorithm combining contour offset and Hilbert curve, and the method proposed in this paper. Through experimental verification, the average warpage deformation of the model printed by the method proposed in this paper was reduced by 37.81% and 13.79%, respectively, compared with the other two methods.

Distribution path optimization of carbon emission-reducing agricultural products in the cold chain from a green economy perspective

As people's environmental awareness improves, the development of green economy has become an important goal for countries to achieve sustainable development. The extensive generation of greenhouse gases during the cold chain logistics and distribution process poses a significant challenge to the advancement of the green economy. By utilizing an enhanced genetic algorithm (GA), this research examines the expenses associated with distributing fresh agricultural products via the cold chain and constructs a model for optimizing the path of distribution. Simulation experiments show that the total cost of this model constructed in the study is 2323.94 RMB, which is less than 2623.35 RMB of the traditional path planning model, and the carbon emission is 85 kg less. It proves that the path optimization model constructed by the research has high economic and environmental benefits, and the designed and planned path is more in line with the needs of green economy than the traditional path, which has the potential to minimize carbon emissions during the fresh agricultural product cold chain distribution process while simultaneously promoting sustainable development.

Design and Simulation of Multi Unmanned Boat Cooperative Obstacle Avoidance System Based on 5G Edge Computing

Background:: Compared to single unmanned boat, multi unmanned boats have more flexible mobility and efficient task completion capabilities, which can effectively expand the types of tasks. However, the traditional independent path planning and obstacle avoidance methods of unmanned boats are difficult to meet the requirements of collaborative operation among multiple unmanned boats due to the lack of information exchange. Objective:: According to the actual demand of multi unmanned boats' cooperative operation, a method of multi unmanned boats cooperative obstacle avoidance based on 5G edge computing is proposed to realize the unified planning and scheduling of multi unmanned boats. Methods:: Firstly, 5G technology and Kubeedge edge computing tools are used to build a multi unmanned boat collaborative obstacle avoidance system based on cloud, edge and end collaboration, and the Kubeedge edge computing platform was optimized by optimizing communication strategies, building a highly available Kubeedge cluster, building a Harbor image center, and using Web management interfaces further to improve the reliability and stability of the system. Secondly, the YOLOR-Deepport multi-target recognition and tracking algorithm based on cloud, edge and end collaborative network is used to complete the recognition and tracking tasks of obstacle targets, and a set of EECBS path planning methods based on the Kubedge centralized control platform is designed to plan collision-free and efficient paths for each unmanned boat in realtime. Finally, the effectiveness of the system was verified through simulation experiments. Results:: The experimental results show that compared to the traditional autonomous planning obstacle avoidance method for unmanned boats, the collaborative planning obstacle avoidance method proposed in this paper can exhibit excellent performance in dense and narrow scenarios, with a more reasonable navigation path, a range reduction of 20% - 50%, and higher safety. Conclusion:: The results show that the cooperative obstacle avoidance system based on 5G edge computing designed in the paper is feasible, and it can effectively realize the cooperative path planning and obstacle avoidance of multi unmanned boats.

Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data

In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path planning algorithm. First, an improved Probabilistic Roadmap (PRM) algorithm is used to quickly and roughly determine the initial path. Then, the morphological dilation is applied to process the grid points of the initial path, retaining the surrounding area of the initial path for a precise positioning of the search range. Finally, the idea of the A∗ algorithm is applied to achieve precise path planning in the refined search range. During the process of constructing the topology map, we utilized parallelization acceleration strategies to expedite the graph construction. In order to verify the effectiveness of the algorithm, we used terrain data to construct a wilderness environment model, and tests were conducted on off-road path planning tasks with different terrains and distances. The experimental results show a substantial enhancement in the computational efficiency of the proposed algorithm relative to the conventional A∗ algorithm by 30 to 60 times.

Novel path planning algorithm for laser powder bed fusion to improve the scan quality of triply periodic minimal surface structures

Traditional path planning methods, such as contour and raster methods, suffer from problems like uneven filling, overfilling, and underfilling in the sliced layers, resulting in poor continuity of the lattice melt pool, internal porosity defects, and severe powder adhesion at the contour edges, while research on path planning for three-periodic minimal surfaces lattices is relatively limited. In this study, a scanning path planning method based on lattice equations control is proposed, which differs from traditional contour paths and raster paths. The new paths are controlled by the isosurface parameters of the lattice equation and optimized using the traveling salesman problem, resulting in more uniform scanning paths. The new paths avoid the underfilling issue present in raster path and the sawtooth-shaped borders of raster path. Additionally, they circumvent the nonuniform scanning path problem caused by uneven wall thickness in contour path. Through visualizing the paths and conducting printing experiments on the lattice, it is found that the new path is more uniform compared to contour paths, effectively addressing the issue of overfilling. Compared to raster paths, the new path has smoother boundaries and reduces internal porosity and powder adhesion within the lattice. This research has important value in reducing internal porosity and external powder adhesion issues in three-period minimal surface (TPMS) lattice printing processes, further enhancing the manufacturing quality of TPMS lattices.

Hierarchical path planner combining probabilistic roadmap and deep deterministic policy gradient for unmanned ground vehicles with non-holonomic constraints

Path planning plays a vital role in autonomous driving as it needs to guide the vehicle to achieve the target position without collision while satisfying the vehicle's non-holonomic constraints. A good path planner should be able to cope with sophisticated environments and try to make the planned path as short as possible. Traditional path planning methods when being applied on four-wheel vehicles may suffer from one of the following disadvantages including conflicting with non-holonomic constraints, far from close-optimal length solution, or easy to get stuck in specific environments. In this paper, a hierarchical path planning method for a four-wheel vehicle combining probabilistic roadmap (PRM) and deep deterministic policy gradient (DDPG) is proposed to address existing problems. In the upper level, PRM is used to generate a guidance path quickly, which doesn't consider the vehicle's non-holonomic constraints but gives the sketch of the path that helps the vehicle to jump out of some dilemmas in specific environments while maintaining a relatively short path distance. In the lower level, reinforcement learning, specifically DDPG, is used to optimize the guidance path generated by PRM considering multiple factors including non-holonomic constraints and obstacle avoidance. The proposed method is validated in four different environments including a simple map, an easy-to-stuck specific map, a maze-like complex map and an office-like complex map. Results show that the proposed method could generate a smooth path satisfying non-holonomic constraints while demonstrating desirable obstacle avoidance performance. The planned path of the proposed method is superior to that of both traditional and state-of-the-art methods in terms of path length and smoothness, which could be 1.3–12.9 % shorter and 2.6–52.1 % smoother in different environments.

Optimizing radiation exposure in dynamic radioactive environments: Dynamic ACO-RRT* path planning algorithm

Abstract Radioactive environments are often dynamic for various reasons, such as the dismantling of nuclear facilities, leakage of radiation sources, and nuclear accidents. Path planning in a dynamic radioactive environment is one of the important issues in radiation protection for nuclear facility workers. In traditional dynamic optimal path planning, there are phenomena, in which the optimal path is not found within the allowable time or the quality of the path searched within the allowable time is low. To solve this problem, we propose a hybrid algorithm that combines meta-heuristic and sample-based algorithms, called dynamic ant colony optimization-rapidly exploring random tree star (ACO-RRT*). A low-level grid optimal path consisting of grid vertices is obtained by the ACO algorithm, and then it is further refined by the RRT* algorithm. At every instant of environmental change, the vertices of the grid optimal path are divided into vertices with and without samples, and new samples are generated only around vertices without samples. Sampling optimization is performed only around the grid optimal path using new samples and already existing samples. Simulations in a virtual radioactive environment showed that the proposed algorithm reduces computational costs and improves the quality of the optimal path compared to traditional algorithms.

Review of path planning of welding robot based on spline curve

This paper assesses the efficacy of intelligent path planning for welding robots utilizing splines. Traditional path planning methods can result in inefficient and inaccurate welding operations. The study reviews current research and case studies to appraise the practical application of spline-based path planning across diverse industrial scenarios. It underscores the benefits of discovering the shortest path and reducing cycle time while acknowledging challenges such as calibration accuracy and sensitivity to sensor data noise. The introduction of artificial intelligence algorithms in automobile welding path planning enables a more precise replication of the body's design curve, ensuring the continuity and smoothness of the welding process. This, in turn, fosters further automation and optimization of the automotive welding manufacturing process. The current research concentrates on integrating intelligent optimization algorithms and spline curves to provide an efficient and intelligent method for welding path planning. Intelligent path planning based on spline curves demonstrates significant potential in enhancing welding efficiency, determining the shortest path, and holds promising applications in the broader research field of welding path planning.

Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem

Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.

Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.

Smooth and Efficient Path Planning for Car-like Mobile Robot Using Improved Ant Colony Optimization in Narrow and Large-Size Scenes

Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. However, path planning methods are prone to inefficiently generate unsmooth paths in narrow and large-size scenes, especially considering the chassis model complexity of CLMRs with suspension. To this end, instead of traditional path planning based on an integer order model, this paper proposes fractional-order enhanced path planning using an improved Ant Colony Optimization (ACO) for CLMRs with suspension, which can obtain smooth and efficient paths in narrow and large-size scenes. On one hand, to improve the accuracy of the kinematic model construction of CLMRs with suspension, an accurate fractional-order-based kinematic modelling method is proposed, which considers the dynamic adjustment of the angle constraints. On the other hand, an improved ACO-based path planning method using fractional-order models is introduced by adopting a global multifactorial heuristic function with dynamic angle constraints, adaptive pheromone adjustment, and fractional-order state-transfer models, which avoids easily falling into a local optimum and unsmooth problem in a narrow space while increasing the search speed and success rate in large-scale scenes. Finally, the proposed method’s effectiveness is validated in both large-scale and narrow scenes, confirming its capability to handle various challenging scenarios.

Multimodal audio-visual robot fusing 3D CNN and CRNN for player behavior recognition and prediction in basketball matches.

Intelligent robots play a crucial role in enhancing efficiency, reducing costs, and improving safety in the logistics industry. However, traditional path planning methods often struggle to adapt to dynamic environments, leading to issues such as collisions and conflicts. This study aims to address the challenges of path planning and control for logistics robots in complex environments. The proposed method integrates information from different perception modalities to achieve more accurate path planning and obstacle avoidance control, thereby enhancing the autonomy and reliability of logistics robots. Firstly, a 3D convolutional neural network (CNN) is employed to learn the feature representation of objects in the environment for object recognition. Next, long short-term memory (LSTM) is used to model spatio-temporal features and predict the behavior and trajectory of dynamic obstacles. This enables the robot to accurately predict the future position of obstacles in complex environments, reducing collision risks. Finally, the Dijkstra algorithm is applied for path planning and control decisions to ensure the robot selects the optimal path in various scenarios. Experimental results demonstrate the effectiveness of the proposed method in terms of path planning accuracy and obstacle avoidance performance. The method outperforms traditional approaches, showing significant improvements in both aspects. The intelligent path planning and control scheme presented in this paper enhances the practicality of logistics robots in complex environments, thereby promoting efficiency and safety in the logistics industry.

Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness.

Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.

Path Inference based on Voronoi Graph for Unmanned Maritime Vehicles

Design/methodology/approachWe propose a novel path inference approach based on Voronoi graph for unmanned maritime vehicles (UMV). We model the two-dimensional space with a flexible construction scheme of Voronoi graph, represent the inter-cell shortest distance with a delicate calculation of cell diameters, and infer the potential qualified navigation paths of UMVs w.r.t. the specified distance constraint based on a diameter path model with an efficient depth-first exploration algorithm. PurposeExisting path planning approaches are generally focused on finding a single optimal path. However, in many practical scenarios, such as UMV patrolling and search and rescue, it is the comprehensiveness rather than the optimality that matters for the UMV paths. Inspired by the above problem, we propose a path inference method based on Voronoi graph to infer all the possible UMV paths satisfying the distance constraint. FindingsExperimental results indicate that our path inference approach based on Voronoi graph is able to perform more delicately and efficiently than the traditional path planning approach in terms of both space modeling and path exploration. The paths identified by our path inference method were more precise in distance estimation as well as more comprehensive when compared with the grid-based path planning method. In the simulated scenario of UMV search & rescue, we obtained the coverage rates of 63.14% and 100% against those of 14.5% and 81.07% for the grid-based method. Originality/valueOur path inference approach is able to provide valuable insights in various practical UMV applications.