Global Path Research Articles

Improved genetic algorithm for mobile robot path planning in static environments

The genetic algorithm (GA) is a well-known meta-heuristic technique for addressing the static mobile robot global path planning (MRGPP) issue. Current GA, however, has certain shortcomings, such as inefficient population initialization and low-quality solutions. As an enhanced GA, a Linear Rank-based, or Clearance-based Probabilistic Road Map (CBPRM), technique is proffered to overcome these difficulties. The new model guides the population initialization process by using the fitness score of each cell in the environment, lowering the number of infeasible pathways created. Furthermore, a genetic operator combination is proposed to balance the global and local search and increase the quality of the optimum path created in terms of path length and safety. Two experiments were carried out to assess the suggested GA. The novel population initialization strategy was compared to two current models in the first experiment, and the findings revealed that the suggested approach greatly decreases the number of infeasible pathways created and the time required for the process. The ideal genetic operator combination was determined in the second experiment, and the results revealed that the suggested combination improves the quality of the optimal path created in fewer iterations. In summary, the proposed GA improves on previous models by proposing a novel population initialization method and combining numerous genetic operators. These alterations improve the quality of the optimum path and indicate the suggested model’s potential for solving the MRGPP challenge.

Global Dynamic Path Planning of AGV Based on Fusion of Improved A* Algorithm and Dynamic Window Method.

Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path.

An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional ant colony algorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced ant colony algorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.

Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles - beyond crop row detection

Field path planning provides the basis for the autonomous navigation of agricultural vehicles. Existing path planning approaches are constrained to a 2D plane, disregarding the impact of terrain factors on navigation tasks. Furthermore, these methods generate non-global paths, as evidenced by their inability to accommodate the headland turning area while performing path planning based on the actual crop growth in the farmland. Low-altitude remote sensing technology, characterized by its high spatial resolution, timely data acquisition, and strong terrain perception, holds great potential for application in autonomous navigation. This study aims to integrate low-altitude remote sensing technology with path-planning tasks for agricultural vehicles by constructing oblique photography models of fields. The objective is to achieve global 3D path planning leveraging advanced methodologies rooted in deep learning and image processing. Four main steps were included in the proposed method. Low-altitude remote sensing models of field blocks were constructed first. Secondly, the models were converted to image patches for dataset establishment. Thirdly, primary crop row identification was conducted by semantic segmentation. Finally, global 3D paths covering the farmland and headland areas were generated. Tea fields in hilly areas were used to test the algorithm. Experiments revealed that the proposed method could be adapted to different field shapes, row numbers, row spacing, and vehicle turning radii. The proposed method uses deep learning and image processing as the primary technical tools but goes beyond traditional crop row detection to form a global path-planning strategy. In addition, the elevation information enabled by the 3D detection manner allows agricultural vehicles to gain a comprehensive understanding of both their own position and that of their destination. The dataset and the code are available at: https://github.com/ZeroHeading/Global-3D-path-generation.git.

Traversable map construction and robust localization for unstructured road environments1

Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation.

Multi objective optimization scheduling of unmanned warehouse handling robots based on A star algorithm

SummaryThe sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all orders and corresponding recycling or return tasks. This paper conducts experiments on global path planning for multiple AGVs based on the A* algorithm, both without considering collision and deadlock situations, and considering collision and deadlock situations. The results show that without considering collision and deadlock situations, the method of matching fixed storage locations and picking stations can improve overall efficiency, while considering collision and deadlock problems will result in reduced efficiency. In the future, the model will be further optimized taking into account actual conditions to improve overall efficiency.

Hybrid path planning method for USV using bidirectional A* and improved DWA considering the manoeuvrability and COLREGs

This paper proposes a hybrid dynamic path planning algorithm that integrates global path planning (GPP) and local path planning (LPP). The objective is to effectively address the challenges associated with path planning and collision avoidance for unmanned surface vehicles (USV) in complex environments. The bidirectional A* algorithm is used to search the optimized strategy based on the actual geographical information map for the navigation routes of USV. The Dynamic Window Algorithm (DWA) is employed, and a novel cost function is formulated considering the USV's manoeuvring characteristics, dynamic constraints, and environmental information. The primary purpose is to ensure the compliance of the USV with COLREGs during dynamic obstacle avoidance. It accomplishes this by providing positive reinforcement for achieving the correct turning speed when the USV determines the orientation of an approaching ship. Another evaluation function is introduced to mitigate the problem of local optima in complex environments that the LPP may encounter. The actual work map is used to model the application scenario with the obstacles in the presence of incoming ships, the simulation results show that the proposed algorithm is capable of generating adaptive, collision-free routes while adhering to COLREGs rules.

DBVS-APF-RRT*: A global path planning algorithm with ultra-high speed generation of initial paths and high optimal path quality

During the last decade, RRT* algorithm has been widely concerned by researchers because of its asymptotic optimality. However, the slow convergence rate of the RRT* algorithm leads to poor quality of the optimal paths at small number of iterations. Meanwhile, searching the initial path slowly limits its application scenarios. To overcome these problems, a directionally biased variable step APF-RRT* (DBVS-APF-RRT*) algorithm is proposed in this paper. Firstly, a novel directionally biased variable step sampling strategy is used in RRT* algorithm to quickly generate the initial path. Then, global random sampling and key region sampling strategies are added to improve global search ability and optimal path quality. At the same time, an artificial potential field (APF) method is introduced to improve the ability of obstacle avoidance. In addition, we propose a pruning strategy based on triangular inequality with direct connection of goal points to reduce the number of redundant nodes and shorten the path length. Finally, DBVS-APF-RRT* algorithm is compared with RRT*, Bias-RRT*, Informed-RRT* and Bias-P-RRT* algorithms to verify its superiority of optimal path quality, stability of the algorithm and its rapidity of finding the initial path.

Research on dual robot collaboration method based on improved double ant colony algorithm

Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods.

Door to door space-time path planning of intercity multimodal transport network using improved ripple-spreading algorithm

The main objective of this study is to solve the problem of recommending door-to-door space–time paths in an intercity multimodal transport network. The available modes include railway, metro, bus, rapid-bus, and walking. In terms of mathematical modeling, the reconstructed space–time path planning model considers multiple constraints, such as acyclic constraint, flow balance constraint and transfer constraints, to ensure the viability of the path, which is significantly different from the path in the traditional graph theory and road network. To attain the K shortest paths in two situations, the improved ripple-spreading algorithm (hereafter referred to as IRSA) is introduced, which is inspired by the ripple-spreading phenomenon and can find global optimal paths through only one iteration. A method for reducing the search space is applied to speed up the computing process. We demonstrate the feasibility and effectiveness of the proposed model and algorithm by comparing it with Gurobi Solver and Dijkstra algorithm using a small-scale network. The experiment results are evaluated by solving the door-to-door itinerary problem from Taiyuan to Beijing. Results indicate that the proposed approach can find constrained K shortest paths within an acceptable computing time. A set of experiments are conducted to test the performance of different methods, and results show that the speed-up method provides a significant improvement in problem-solving efficiency, while attempting to guarantee path optimality, which could reduce the CPU time by 51% to 92% for the tested OD pairs.

Path-Planning Strategy: Adaptive Ant Colony Optimization Combined with an Enhanced Dynamic Window Approach

Aiming to resolve the problems of slow convergence speed and inability to plan in real time when ant colony optimization (ACO) performs global path planning, we propose a path-planning method that improves adaptive ant colony optimization (IAACO) with the dynamic window approach (DWA). Firstly, the heuristic information function is modified, and the adaptive adjustment factor is added to speed up the algorithm’s convergence rate; secondly, elite ants and max–min ants systems are implemented to enhance the global pheromone updating process, and an adaptive pheromone volatilization factor is aimed at preventing the algorithm from enhancing its global search capabilities; then, the path optimization and withdrawal mechanism is utilized to enable smoother functioning and to avoid the deadlocks; finally, a new distance function is introduced in the evaluation function of DWA to the enhance real-time obstacle-avoidance ability. The simulation experiment results reveal that the path length of the IAACO can be shortened by 10.1% and 13.7% in contrast to the ACO. The iteration count can be decreased by 63.3% and 63.0%, respectively, leading to an enhanced optimization performance in global path planning and achieving dynamic real-time obstacle avoidance for local path planning.

Merging planning in dense traffic scenarios using interactive safe reinforcement learning

Autonomous navigation in dense traffic scenarios, such as on-ramp forced merging, still poses significant challenges for autonomous vehicles to prevent accidents and alleviate traffic congestion. This paper introduces a novel motion planning framework that combines Interactive Safe Reinforcement Learning (IntSRL) with Nonlinear Model Predictive Control (NMPC). This framework develops an interactive merging planning policy that accounts for the uncertainty of traffic participants, multi-objective optimization and heterogeneous vehicle interactions, in which the upper planner, i.e., IntSRL, furnishes the lower planner, NMPC, with global guidance path and velocity guidance. An Adaptive Safety Governor (ASG) module within IntSRL adjusts potentially unsafe actions by incorporating prior knowledge and driving experience. And a coupling evaluation mechanism for multi-objective optimization is embedded into reward shaping with integration of driving safety and strategy efficiency. We evaluate the proposed controller on various dense traffic scenarios using the proposed Heterogeneous Intelligent Driver Model (H-IDM) considering different driving styles and cooperative willingness of other vehicles. The test results indicate that the proposed method surpasses existing optimization-based and learning-based baselines in qualitative and quantitative measures.

Hydration kinetics of cation exchanged clinoptilolite zeolite based cementitious materials

This paper provides information on the global reaction path and hydration kinetics of a natural clinoptilolite-Ca zeolite blended portland cementitious system. The replacement of up to 20% as-received clinoptilolite-Ca zeolite with portland cement substantially minimized the tricalcium aluminate (C3A) peak heat of hydration as observed using isothermal calorimetry. Inductively coupled plasma-optical emission spectrometry results showed that the zeolite-blended samples had a higher initial chemical dissolution of calcium and aluminate; but subsequently, the consumption of both was higher compared to the neat cement control sample, likely due to the formation of new hydration reaction products, indicating an interaction between cement and clinoptilolite-Ca zeolite. Clinoptilolite-Ca zeolite transformed into clinoptilolite-Na zeolite using NaOH and NaCl solutions showed a distinct aluminate (C3A) peak heat flow, indicating a different reaction path compared to clinoptilolite-Ca zeolite. Micro-chemical analysis using energy dispersive spectroscopy suggests a different hydration reaction mechanism for both calcium and sodium-based zeolite, along with the possibility of reduction in the formation of C–S–H, ettringite, and monosulfate due to the formation of new hydration products. The clinoptilolite-Ca zeolite consumed significantly higher aluminate contents leading to the formation of amorphous calcium-aluminum-silicate-hydrate (C-A-S-H) gels, which consequently reduced the aluminate-related reaction (C3A) heat flow in the system. In contrast, the clinoptilolite-Na zeolite released sodium ions through cation exchange to produce sodium aluminosilicate hydrate (N-A-S-H) in the blended system, which did not interfere with the C3A peak heat flow during the hydration process.

Electroencephalography oscillations can predict the cortical response following theta burst stimulation

BackgroundContinuous theta burst stimulation and intermittent theta burst stimulation are clinically popular models of repetitive transcranial magnetic stimulation. However, they are limited by high variability between individuals in cortical excitability changes following stimulation. Although electroencephalography oscillations have been reported to modulate the cortical response to transcranial magnetic stimulation, their association remains unclear. This study aims to explore whether machine learning models based on EEG oscillation features can predict the cortical response to transcranial magnetic stimulation. MethodTwenty-three young, healthy adults attended two randomly assigned sessions for continuous and intermittent theta burst stimulation. In each session, ten minutes of resting-state electroencephalography were recorded before delivering brain stimulation. Participants were classified as responders or non-responders based on changes in resting motor thresholds. Support vector machines and multi-layer perceptrons were used to establish predictive models of individual responses to transcranial magnetic stimulation. ResultAmong the evaluated algorithms, support vector machines achieved the best performance in discriminating responders from non-responders for intermittent theta burst stimulation (accuracy: 91.30%) and continuous theta burst stimulation (accuracy: 95.66%). The global clustering coefficient and global characteristic path length in the beta band had the greatest impact on model output. ConclusionThese findings suggest that EEG features can serve as markers of cortical response to transcranial magnetic stimulation. They offer insights into the association between neural oscillations and variability in individuals' responses to transcranial magnetic stimulation, aiding in the optimization of individualized protocols.

The Taguelft syncline (Moroccan Central High Atlas) an example of extension-related mini-basin evidenced by paleomagnetic data

The purpose of this work is to use paleomagnetic data to determine the tectonic evolution of the inverted High Atlas basin in the area of the Taguelft syncline. This syncline shows two well defined rock types of Jurassic age: marly limestones and red beds, and therefore provides the opportunity of comparing paleomagnetic results from rocks with different magnetic mineralogy. Forty-three sites, in an area of 250 km2 were the subject of a paleomagnetic and rock magnetic study. Both lithologies display a stable interfolding remagnetization, alternatively carried by magnetite in marly-limestones and hematite in red beds. The small circle intersections (SCI) method applied separately to the mean directions of the two rock types indicate a synchronous record of remagnetization, that can be dated as Mid-Cretaceous (100 Ma) by comparing the direction (SCI) with the expected directions obtained from the Global Apparent Wander Path in African coordinates. Small circle techniques were used to reconstruct the syncline geometry at the remagnetization time. Paleodips quantification reveals that this area behaved as an extension-related mini-basin, with strong control by salt migration, before and during the sedimentation of Bathonian red beds.

The internationalization of hidden champions from Germany and the UK: An extension to the Born-Again Global path

Although internationalization has been extensively studied, the majority of analyses have focused on motives and enablers, also known as triggers, with less attention paid to internationalization paths. To add to the internationalization paths debate, we explore global niche players dubbed as Hidden Champions from Germany and Britain. Our findings show that a large portion of these firms experience an international ‘rebirth’ by adopting a more accelerated approach to internationalization after already gaining first international experience. This is mainly through exporting activity, which is a variation of the Born-Again Global phenomenon. Due to their unique internationalization path, we call these firms Re-Born-Again Globals. Furthermore, we uncover the critical incidents for the internationalization of these firms, namely changes in management, technology advances, and product diversification.

An efficient indoor large map global path planning for robot navigation

Large indoor cluttered environment representation is still a challenging task when non-uniform triangle cell-based or quadrangle cell-based decomposition is used to build the map. This paper aims at proposing a new method to represent a large indoor environment for efficient and global robot path planning using a trade-off among three criteria: path length, distance to obstacles, and path search complexity. In this regard, three steps are involved: (i) the design of a tiled map that represents several regular sub-regions of the given large environment; (ii) the selection of the best representation between non-uniform triangle cell-based submap and regular quadrangle cell-based submap for each tile using the predefined efficient path planning criteria. Hence, the entire large environment is represented by a hybrid cell-based map; (iii) the path search using the well-known A* algorithm. Moreover, to find the best representation, firstly, a generative method based on cellular automata is used to build a large synthetic database of maps of the same size as the tiles. Each map of the database is associated with the representation which provides the most efficient path planning. Given a tile, the corresponding image is used to find the closed tile image from the database, and the associated representation is selected as the best representation. Extensive simulations, as well as experiments, suggest that for a given large, cluttered environment, the hybrid representation with a mix of triangle-based cells and quadrangles-based cells can provide a more efficient global path compared to the traditional regular quadrangle or triangle representations.

Research on path planning technology of mobile robot in the restaurant scene based on A* and D* algorithm

Intelligent robots have found widespread applications in various fields including medical treatment, industrial production, and social services. These robots are specifically designed to fulfill diverse tasks in different environments, the design and control of these robots play a crucial role in ensuring their efficiency and security. In response to the high demand for fast and convenient service, the catering field is a crucial and promising application field for intelligent robots. This article is primarily focused on the robot in the restaurant scene where the robot could automatically deliver food to the customers, thus reducing the burden on human service providers. In this special environment, the delivery robot must possess the capability to identify its surroundings within the restaurant and autonomously calculate an obstacle-free path from the starting point to the desired destination without colliding with either obstacles or individuals. In this article, the restaurant scene is built in the Gazebo, and map scanning and conversion methods are utilized to enable the robots to recognize the environment effectively. Furthermore, A* and D* algorithms are employed in Python to achieve global and local path planning, respectively, thus validating the feasibility of the methods. The combination of these two algorithms demonstrates a successful path-planning application within the context of a restaurant scene.

Field-enriched A* search algorithm for robot motion in path planning

Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.