Path Planning Method Research Articles

3D Printing of Flow-Inspired Anisotropic Patterns with Liquid Crystalline Polymers.

Anisotropic materials formed by living organisms possess remarkable mechanical properties due to their intricate microstructure and directional freedom. In contrast, human-made materials face challenges in achieving similar levels of directionality due to material and manufacturability constraints. To overcome these limitations, an approach using 3D printing of self-assembling thermotropic liquid crystal polymers (LCPs) is presented. Their high stiffness and strength is granted by nematic domains aligning during the extrusion process. Here, a remarkably wide range of Young's modulus from 3 to 40 GPa is obtained by utilizing directionality of the nematic flow the printing process. By determining a relationship between stiffness, nozzle diameter, and line width, a design space where shaping and mechanical performance can becombined is identified. The ability to print LCPs with on-the-fly width changes to accommodate arbitrary spatially varying directions is demonstrated. This unlocks the possibility to manufacture exquisite patterns inspired by fluid dynamics with steep curvature variations. Utilizing the synergy between this path-planning method and LCPs, functional objects with stiffness and curvature gradients can be 3D-printed,offering potential applications in lightweight sustainable structures embedding crack-mitigation strategies. This method also opens avenues for studying and replicatingintricate patterns observed in nature, such as wood or turbulent flow using 3D printing.

Interactive image segmentation based field boundary perception method and software for autonomous agricultural machinery path planning

Autonomous agricultural machinery path planning requires high-precision field boundary information. To address the challenge of rapidly acquiring accurate information about different types of land objects in complex field scenarios, this study introduces an interactive segmentation-based method and software for agricultural field boundary perception, specifically designed for high-resolution orthophotos. The method aims to accurately delineate various agricultural objects in the image, such as crops, soil, roads, edges, exits to fields, and obstacles. Compared to previous research on agricultural object detection and segmentation, this study proposes an interactive deep image segmentation model for perceiving multiple types of agricultural features. During the image segmentation process, manually adding positive and negative points provides supervised information for the segmentation of agricultural images. In addition, this research uses the PaddlePaddle deep learning framework to implement the proposed method and extends the open-source software EISeg to develop a dedicated tool for agricultural image segmentation. Through 3 to 4 interactive iterations, the method achieves an impressive mean Intersection over Union (mIoU) segmentation accuracy of about 90%. The model's average inference time on the training server was 0.197 s, meeting the real-time requirements of the interactive segmentation method. By accurately segmenting agricultural land features from high-resolution orthoimagery, the proposed method can provide valuable support for the construction of high-precision navigation maps for autonomous agricultural machinery.

A Safe Heuristic Path-Planning Method Based on a Search Strategy.

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.

Path Planning Using a Flow of Pedestrian Traffic in an Unknown Environment

This paper outlines a path planning method for autonomous rovers navigating urban environments without prior mapping, with a particular focus on addressing the Tsukuba Challenge. Our approach utilizes observations of pedestrian and robot movement trajectories to construct path graphs for global path planning. We provide a detailed overview of the autonomous rover’s hardware and software system, as well as a comprehensive description of the path planning algorithm. Our methodology entails extracting and continuously tracking dynamic objects from LiDAR data, resulting in the creation of a path graph based on their observed trajectories. Subsequently, a path aligned with the desired direction is selected. Notably, in indoor experimental settings, our approach proves effective, as the rover successfully generates a path to the goal by closely monitoring and tracking pedestrian movements. In conclusion, this paper introduces a promising path planning methodology and suggests potential areas for further research in autonomous mobility within uncharted environments.

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously.

UAV path planning based on third-party risk modeling

Drones play an important role in modern cities, they bring convenience but also bring corresponding risks. Falling drones can cause casualties or damage to urban facilities and serious property damage, which increases the third-party risk problem. An effective way to reduce these third-party risks is to avoid high-risk areas in path planning, but most current path planning methods are optimized to minimize flight distance, and third-party risk costs are rarely considered. In this paper, a comprehensive risk-cost UAV path planning method is proposed, which evaluates urban risk by establishing a third-party risk model that incorporates obstacle risk, death risk, and property loss risk. This paper proposes a Min-cost A* algorithm based on city risk assessment, and smooths the generated low-risk path through the improved Floyd algorithm. The results show that the path planning method can effectively reduce the risk in the flight path, improve the reliability of the UAV flight path in the urban environment, and solve the problem of planning the third-party risk path of the UAV.

A two-phase method for compaction path planning and control of the impact roller

Compaction efficiency and quality are extraordinarily significant for branch-line airport construction engineering. However, a human-determined compaction path is generally adopted during the compaction, negatively impacting compaction efficiency and quality. A two-phase approach to the compaction path is proposed to address the problem. The first phase focuses on the establishment of a model to optimize the impact roller’s complete-coverage compaction path (CCCP) to improve compaction efficiency. In this phase, the optimization problem of the CCCP is equivalent to the traveling salesman problem (TSP). Next, the optimization of the CCCP is formulated and solved as a binary integer programming problem. Finally, a method for determining the optimal traversal compaction sequence is proposed to obtain the optimal CCCP based on the secondary development of the Gurobi optimizer. In the second phase, to guarantee the compaction quality, the path navigation modulus is designed to precisely control the compaction path. Combining the compaction path planning with the path control, an integrated system is developed to provide a better service for practical earthwork compaction engineering in airport construction. The results of the simulation experiment and field experiment indicate that the CCCP determined by the proposed optimization method in the first phase can improve the compaction efficiency compared to the human-determined compaction path. In addition, the developed system can effectively generate the CCCP. Meanwhile, the developed system can provide a visual path navigation and precise path control during the compaction to ensure the compaction quality, verifying that the proposed method can offer a novel idea for compaction path control.

3D real-time dynamic path planning for UAV based on improved interfered fluid dynamical system and artificial neural network

In complex and volatile unknown flight environments, the limited environmental information obtained by sensors in the face of sudden dynamic and static obstacles makes it extremely challenging for unmanned aerial vehicles (UAVs) to obtain a safe and efficient path to avoid obstacles and reach a designated target point. Therefore, a real-time dynamic path planning method based on an improved interfered fluid dynamical system (IFDS) and artificial neural network (ANN) is proposed to enhance path quality and computational efficiency. Firstly, to address the issue of insufficient sample quality and quantity, IFDS is employed as the fundamental method for path planning to simulate and generate an adequate amount of sample data for the ANN training. Then, an enhanced sand cat swarm optimization algorithm (ESCSO) with an adaptive social neighborhood search mechanism and Lévy flight strategy is proposed to improve the sample quality. Secondly, the information between the UAV and the target points and obstacles is extracted from the sample data as the input for the network, the parameters of the IFDS are used as the feature extraction at the output of the network, and the ESCSO is applied to optimize the weights and biases of the ANN, enabling offline training of the neural network. Finally, the trained neural network is utilized to dynamically output IFDS parameters based on the real-time environmental information obtained from the sensors, enabling the generation of real-time obstacle avoidance paths. Experimental results in a series of complex simulated environments demonstrate that the proposed method outperforms other algorithms in terms of path quality and meets real-time requirements. It provides excellent obstacle avoidance characteristics for the UAV.

A flow-directed minimal path sets method for success path planning and performance analysis

Emergency operation plans are indispensable elements for effective process safety management especially when unanticipated events occur under extreme situations. In the paper, a synthesis framework is proposed for the integration success path planning and performance analysis. Within the synthesis framework, success path planning is implemented through flow-directed signal tracing, renaming and reconstruction from a complete collection of Minimal Path Sets (MPSs) that are obtained using graph traversal search on GO-FLOW model diagram. The performance of success paths is then evaluated and prioritized according to the task complexity and probability calculation of MPSs for optimum action plans identification. Finally, an Auxiliary Feed Water System of Pressurized Water Reactor (PWR-AFWS) is taken as an example system to demonstrate the flow-directed MPSs search method for success path planning and performance analysis. It is concluded that the synthesis framework is capable of providing procedural guidance for emergency response and safety management with optimal success path planning under extreme situations.

A multi-objective evolutionary algorithm based on dimension exploration and discrepancy evolution for UAV path planning problem

Path planning is a crucial process for unmanned aerial vehicles (UAVs) and involves finding a path that is both short and safe. However, with the ever-increasing complexity of the environment, solving the UAV path-planning problem is challenging. Traditional path-planning methods cannot handle conflicting goals effectively, and existing objective methods lack targeted exploration mechanisms, resulting in unsatisfactory outcomes. By modeling the UAV path-planning problem via multi-objective optimization, this study designed a reasonable objective function composition for the model and considered obstacle avoidance as a hard constraint to satisfy the actual situation. A multi-objective evolutionary algorithm based on dimensional exploration and discrepancy evolution (MOEA-2DE) is presented. In particular, MOEA-2DE utilizes dimensional perturbation to identify key dimensions to facilitate prior exploration and enhance the targeted search. An adaptive evolution strategy based on population discrepancy was employed to assess the evolution process, and various methods were adopted to balance convergence and diversity. The effectiveness of the MOEA-2DE was demonstrated through the design of two intricate terrain sets and comparisons with various classic and state-of-the-art multi-objective evolutionary algorithms (MOEAs), including those designed for UAV path planning across multiple metrics. The results verify the superiority of MOEA-2DE in terms of both convergence speed and final effect.

A Path-Planning Method to Significantly Reduce Local Oscillation of Manipulators Based on Velocity Potential Field

The robotics industry and associated technology applications are a vital support for modern production and manufacturing. With the intelligent development of the manufacturing industry, the application of collaboration robots and human-robot collaboration technology is becoming more and more extensive. In a human-robot collaboration scenario, there are uncertainties such as dynamic impediments, especially in the human upper limb, which puts forward a higher assessment of the manipulator’s route planning technology. As one of the primary branches of the artificial potential field (APF), the velocity potential field (VPF) offers the advantages of good real-time performance and convenient mathematical expression. However, the traditional VPF algorithm is prone to local oscillation phenomena near obstacles, which degrades the smoothness of the movement of the manipulators. An improved velocity potential field algorithm is proposed in this paper. This method solves the problem of sudden velocity change when the manipulator enters and departs the region of the potential field by setting new functions for attraction velocity and repulsion velocity functions. A virtual target point construction method is given to overcome the local oscillation problem of the manipulators near obstacles. The simulation and practical findings of the manipulators reveal that the improved VPF algorithm can not only avoid collision but also effectively reduce the local oscillation problem when dealing with the human upper limb as a dynamic obstacle. The implementation of this algorithm can increase the safety and real-time performance of the human-robot collaboration process and ensure that the collaborative robot is safer and smoother in the working process.

Cooperative Path Planning of Ground-air Robots for Distributed Photovoltaic Inspection

Facing the Distributed PV generation inspection scenario, to overcome the low efficiency of traditional manual inspection and the insufficient endurance of existing UAV inspection, UAV aerial inspection and UGVs to provide energy supply in the road network are adopted, and a path planning method that prioritizes the meeting points is proposed. Firstly, the improved genetic algorithm is used to plan the inspection path of each UAV, then the UAV path is clustered to determine the optimal number of meeting points, then the adaptive particle swarm algorithm is used to find the best location of the meeting points in the road network, and finally, the patrol path with the meeting points is optimized twice. Through the simulation test of the standard data set and real data, the effectiveness of the proposed method in solving the path planning problem of distributed photovoltaic coordinated inspection by ground-to-air robots is verified.

Efficient cutting path planning for a non-spherical tool based on an iso-scallop height distance field

Iso-scallop height machining means, when machining a freeform surface, the scallop height between any two neighboring tool paths on the surface will be a constant (i.e., the given threshold), which is preferable among various freeform surface machining strategies due to its high machining efficiency as well as better machine tool’s dynamics. However, all the existing iso-scallop height path planning methods pertain to only the ball-end or flat-end types of tools. In recent years, the non-spherical cutting tool has become more and more popular, especially for five-axis machining of complex freeform surfaces, majorly owing to its non-constant curvature which can be utilized to adaptively fit the tool to the surface to both avoid the local gouging and enlarge the cutting width. However, there have been no reported works on iso-scallop height five-axis tool path generation for a non-spherical tool, and, in this paper, we present one. Specifically, we first define and construct two fields on the surface to be machined-the collision-free tool orientation field (vector) and the iso-scallop height distance field (scalar). The iso-lines of the scalar field and their associated tool orientation field vectors then naturally serve as potential iso-scallop height five-axis tool paths, and we present a propagation-based algorithm to construct the desired tool path from the iso-lines. The computer simulation and physical cutting experiments confirm that everywhere on the surface, except maybe near the saddle curves of the scalar filed, the scallop height is exactly the given threshold. By adding the saddle curves as extra tool paths, the final machined surface then is assured of the required scallop height requirement.

A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints

This study focuses on the path-planning problem of rescue UAVs with regional detection priority. Initially, we propose a mixed-integer programming model that integrates coverage path planning (CPP) and the hierarchical traveling salesman problem (HTSP) to address multi-regional path planning under priority constraints. For intra-regional path planning, we present an enhanced method for acquiring reciprocating flight paths to ensure complete coverage of convex polygonal regions with shorter flight paths when a UAV is equipped with sensors featuring circular sampling ranges. An additional comparison was made for spiral flight paths, and second-order Bezier curves were employed to optimize both sets of paths. This optimization not only reduced the path length but also enhanced the ability to counteract inherent drone jitter. Additionally, we propose a variable neighborhood descent algorithm based on K-nearest neighbors to solve the inter-regional access order path-planning problem with priority. We establish parameters for measuring distance and evaluating the priority order of UAV flight paths. Simulation and experiment results demonstrate that the proposed algorithm can effectively assist UAVs in performing path-planning tasks with priority constraints, enabling faster information collection in important areas and facilitating quick exploration of three-dimensional characteristics in unknown disaster areas by rescue workers. This algorithm significantly enhances the safety of rescue workers and optimizes crucial rescue times in key areas.

Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.

Risk-based path planning for preventing collisions and groundings of maritime autonomous surface ships

In this paper, we propose and demonstrate a method for risk-based path planning, generating risk-acceptable trajectories for an autonomous surface ship. This objective is achieved in two steps. The first step uses a dynamic probabilistic risk assessment method called K-shortest-paths Probabilistic Risk Assessment (KPRA), alongside risk models and dynamic system simulation, whose output is a list of paths ranked with respect to risk. Multiple risk influencing factors and accident types may be considered in this step. In this work, collision and powered grounding accidents, and their risk-influencing factors and indicators, are considered. The second step is to perform risk evaluation on the ranked scenarios, comparing them to predefined risk acceptance criteria and defining which scenarios have acceptable risk. Evaluating multiple paths allows operators to choose how strict the autonomous ship’s decision-making should be regarding risk, avoiding the problem of minimization algorithms being too conservative in terms of risk. One of the risk-acceptable paths is then chosen as a local plan to be acted by the autonomous ship. This paper has two main scientific contributions. First, an online Bayesian belief network risk model for navigation accident probability is designed and integrated to improve KPRA. Second, we propose a modified guidance, navigation, and control framework for autonomous ships, to include risk-based planning. We implement the modified framework into a real autonomous surface vehicle (ASV) named Grethe and demonstrate the method’s capabilities in field trials, where the ASV uses the method to generate local plans that avoid possible collision and grounding accidents.

Urban high‐quality navigation path planning that integrates human emotion perception learning

AbstractGeographic information system navigation services are now incorporating psychological well‐being as a factor when devising navigation routes. However, challenges such as limited data, method generalizability, and subjective human perception remain unresolved. Therefore, a general humanized path‐navigation method that effectively quantifies human emotional perception demands is required. In this study, we designed a deep learning model using a large, crowdsourced dataset to predict emotional responses to street‐view images. Our method enhances urban path planning, thus providing comprehensive emotional benefits. Our approach and several goal‐oriented methods were applied in Nanjing, and the findings were compared via comparative analyses and questionnaire surveys. The results confirmed that our proposed method outperforms utilitarian goal‐driven path planning methods in terms of subjective perception. This study provides a widely available technique for high‐quality navigation planning that meets psychological perception needs and offers a valuable guidance for the research on humanized geographic information services.

Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning

Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs.

A Construction Method of a Sequential Decision Chain for Unmanned-Ship Autonomous Collision Avoidance Based on Human-Like Thinking

As one of the key technologies restricting the development of intelligent ships, autonomous collision avoidance has attracted the attention of many scholars all over the world. Existing research on collision-avoidance behavior focuses more on collision risk assessment and local path-planning methods for studies on the human-like sequential logic of the whole collision-avoidance process, as well as the decision-making process of various stages. Further in-depth thinking is needed urgently. Based on this, a construction method of a human-like sequential decision chain for the autonomous collision avoidance of unmanned ships is proposed through the construction of a collision-avoidance rule base and strategy set, efficient data access based on the Knowledge Graph concept, global collision risk assessment considering sequential decision process, and the construction of a complete collision-avoidance logic process to simulate the decision-making process of humans in complex multi-ship encounters in open waters. For multi-ship encounter scenarios, considering the sequential decision-making process of collision avoidance, a method was proposed to divide the collision risk of the target ship into direct collision risk and potential collision risk. The validity and reliability of the constructed sequential decision chain are verified by simulation experimental results. The results show that the method is effective for collision avoidance (especially multi-ship collision avoidance) in open waters and can provide a theoretical basis and technical support with good interpretability for the decision-making process of an unmanned ship’s autonomous collision avoidance.

A path planning algorithm for three-dimensional collision avoidance based on potential field and B-spline boundary curve

Path planning is one of the key technologies in unmanned aerial systems. The path-planning algorithm for UAVs in this study incorporates a three-dimensional obstacle model, addressing the limitations of existing research that primarily focuses on the two-dimensional plane. This approach elevates traditional two-dimensional obstacle constraints to a three-dimensional space, fulfilling the requirements for precise obstacle avoidance. Utilizing B-spline curves, an obstacle boundary model is proposed, which, in combination with an improved artificial potential field accounting for localized self-locking oscillations, achieves smooth path planning for obstacle avoidance from the starting point to the destination. The simulation results show the effectiveness of this method in collision-free path planning within three-dimensional environments containing static single or multiple obstacles. At the points of maximum curvature in the two-dimensional coordinate system, the path planned by the proposed algorithm exhibits a smoothness improvement of 68 % and 98 %, respectively, as compared to the traditional artificial potential field algorithm with the equivalent three-dimensional obstacle model and the tangent point method. The proposed algorithm enables drones to achieve precise obstacle avoidance along surfaces, generating smoother collision-free flight paths in a shorter period of time.