Global Path Research Articles

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.

An Improved JPS Algorithm for Global Path Planning of the Seabed Mining Vehicle

An Improved JPS Algorithm for Global Path Planning of the Seabed Mining Vehicle

A novel directional sampling-based path planning algorithm for ambient intelligence navigation scheme in autonomous mobile robots

Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal Rapidly Exploring Random Trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.

Heuristic dynamic path planning algorithm based on SALSTM-DDPG

In the field of path planning, there are many excellent traditional algorithms proposed by scholars, but usually these traditional methods based on static environment, these methods lack the ability to deal with dynamic variable environment. A heuristic reinforcement learning dynamic environment path planning algorithm (SALSTM-DDPG) combining self-attention and long short-term memory network(LSTM) is proposed in this paper. Firstly, an automatic encoder is constructed to reduce the feature dimension of the local cost map, which reduces the complexity of the overall model. Then the local cost map and global path after dimensionality reduction are taken as input, the original source of information is guaranteed to the maximum extent possible by this method, at the same time, the global optimal path is used to guide the local path planning. Finally, the deep reinforcement learning algorithm DDPG is used for specific motion control, the Actor part is built using a network based on the SALSTM algorithm. This approach allows previous sequence to be used as a reference when an Actor makes decisions, this enables dynamic obstacles to be predictably avoided by the robot. The feasibility and efficiency of the proposed algorithm are proved by experiments.

Kompetensi Literasi Perspektif Al-Qur’an (Studi Komparasi Mufasir Minangkabau)

The author limits the comparison between Minangkabau commentators on two commentators, namely Mahmud Yunus and Buya Hamka with their commentary products Tafsir Al-Qur'an Al-Karim by Mahmud Yunus and Tafsir Al-Azhar by Buya Hamka. The background of this research departs from the problem of literacy in Indonesia which is in a low class from all over the world according to a survey conducted by PISA (Program for International Student Assessment), which is in 62nd position out of 70 countries. In fact, according to statistical data, Indonesia is a country with a predominantly Muslim population. The Qur'an as the main source of religion and the life of its people has indicated a strong suggestion to form literate human character through the first prophecy received by the Prophet Muhammad. Besides that, the same social background of the two interpreters made the writer interested in examining socio-cultural values in the interpretations carried out by the two, especially in verses regarding literacy. This study uses the muqarran method (comparison/comparison) in the context of comparing the interpretations of one commentator with another. The type of this research is library research which collects data from written sources with the main sources being Tafsir Al-Qur'an Al-Karim and Tafsir Al-Azhar, and will also be supported and complemented by related written sources. such as journals, books regarding interpretation, and others. This research resulted in an understanding that there was no disagreement or significant difference in the meaning of the verses between the two commentators. While the difference between the two lies in the method of explanation used in which Mahmud Yunus chose a concise and global interpretation path, while Buya Hamka interpreted the verse with a detailed explanation and included several supporting histories to complete his interpretation. Departing from the socio-culture of the two exegetes, the writer finds the existence of cultural values in the interpretation of the two which is shown in the form of the choice of local words, the use of local expressions, and the conditions that occur locally.

Global path planning for airport energy station inspection robots based on improved grey wolf optimization algorithm

Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time.

Deep reinforcement learning-aided autonomous navigation with landmark generators.

Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on. Improving the accuracy of autonomous navigation of mobile robots is a hot issue to be solved. However, traditional navigation methods are unable to realize crash-free navigation in an environment with dynamic obstacles, more and more scholars are gradually using autonomous navigation based on deep reinforcement learning (DRL) to replace overly conservative traditional methods. But on the other hand, DRL's training time is too long, and the lack of long-term memory easily leads the robot to a dead end, which makes its application in the actual scene more difficult. To shorten training time and prevent mobile robots from getting stuck and spinning around, we design a new robot autonomous navigation framework which combines the traditional global planning and the local planning based on DRL. Therefore, the entire navigation process can be transformed into first using traditional navigation algorithms to find the global path, then searching for several high-value landmarks on the global path, and then using the DRL algorithm to move the mobile robot toward the designated landmarks to complete the final navigation, which makes the robot training difficulty greatly reduced. Furthermore, in order to improve the lack of long-term memory in deep reinforcement learning, we design a feature extraction network containing memory modules to preserve the long-term dependence of input features. Through comparing our methods with traditional navigation methods and reinforcement learning based on end-to-end depth navigation methods, it shows that while the number of dynamic obstacles is large and obstacles are rapidly moving, our proposed method is, on average, 20% better than the second ranked method in navigation efficiency (navigation time and navigation paths' length), 34% better than the second ranked method in safety (collision times), 26.6% higher than the second ranked method in success rate, and shows strong robustness.

Research on Full Coverage Path Planning Based on Reinforcement Learning in Nuclear Environment

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of [Formula: see text]-learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, [Formula: see text]-learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the [Formula: see text]-learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain.

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment.

When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.

Are the traditional socio-economic causes of poverty still pertinent nowadays? Evidence from Romania within the European Union context

PurposeFirstly, the authors aimed at offering a general perspective on poverty, highlighting important directions of thinking from the theoretical development, including poverty's main causes (like education, health, social protection, economic growth, employment and good governance). Secondly, it was investigated the problem of poverty in Romania after its accession to the European Union (EU), aiming to show (1) Romania's position among the EU member states, nearby (2) the direct and indirect effects of the selected factors particularly in this country.Design/methodology/approachBased on data from Eurostat and Global Competitiveness Index (GCI), descriptive and path analyses were performed for the period between 2007 and 2020.FindingsThe main results emphasized (1) the critical position of Romania among the EU member states in 2020 and the progress recorded since 2007, nearby (2) the direct and indirect effects of the selected factors on poverty in this country. The study highlighted the limited chances for the full participation of the poor people of Romania in the progress of the entire society, especially in terms of education and economic growth.Originality/valueThe major contribution of this study is that it assumes to investigate a profound and persistent societal phenomenon in Romania that is not sufficiently analyzed, discussed and understood. Consequently, this study attained its main objective of showing that the main traditional socio-economic causes of poverty are still pertinent in the actual Romanian society.

Global fixed-time adaptive fuzzy path following control for unmanned surface vehicles subject to lumped uncertainties and actuator saturation

A global fixed-time control strategy is presented for addressing the path following problem of underactuated unmanned surface vehicles (USVs) with lumped uncertainties and actuator saturation in this paper. Firstly, a novel fixed-time dual adaptive line-of-sight (FTDALOS) guidance law is proposed, which provides the desired heading angle and surge velocity. Subsequently, considering the complex marine environment, a novel fixed-time adaptive fuzzy controller (FTAFC) is developed. Additionally, to solve the issues of lumped uncertainties and actuator saturation, a fixed-time adaptive fuzzy control strategy and a fixed-time dynamic auxiliary saturation system are proposed. It is demonstrated that the proposed strategy can drive the USV along the desired path smoothly and rapidly, and all errors of the closed-loop system can converge to arbitrary small neighborhoods of the origin within fixed-time intervals. Finally, the effectiveness and superiority of FTDALOS guidance law and FTAFC are verified by theoretical analysis and simulation studies.

A Review of Path Planning for Unmanned Surface Vehicles

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship’s trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends.

Roboat III: An autonomous surface vessel for urban transportation

AbstractIn this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers.

Seasonal vaccination with RTS,S/AS01E vaccine with or without seasonal malaria chemoprevention in children up to the age of 5 years in Burkina Faso and Mali: a double-blind, randomised, controlled, phase 3 trial

Seasonal vaccination with the RTS,S/AS01E vaccine combined with seasonal malaria chemoprevention (SMC) prevented malaria in young children more effectively than either intervention given alone over a 3 year period. The objective of this study was to establish whether the added protection provided by the combination could be sustained for a further 2 years. This was a double-blind, individually randomised, controlled, non-inferiority and superiority, phase 3 trial done at two sites: the Bougouni district and neighbouring areas in Mali and Houndé district, Burkina Faso. Children who had been enrolled in the initial 3-year trial when aged 5-17 months were initially randomly assigned individually to receive SMC with sulphadoxine-pyrimethamine and amodiaquine plus control vaccines, RTS,S/AS01E plus placebo SMC, or SMC plus RTS,S/AS01E. They continued to receive the same interventions until the age of 5 years. The primary trial endpoint was the incidence of clinical malaria over the 5-year trial period in both the modified intention-to-treat and per-protocol populations. Over the 5-year period, non-inferiority was defined as a 20% increase in clinical malaria in the RTS,S/AS01E-alone group compared with the SMC alone group. Superiority was defined as a 12% difference in the incidence of clinical malaria between the combined and single intervention groups. The study is registered with ClinicalTrials.gov, NCT04319380, and is complete. In April, 2020, of 6861 children originally recruited, 5098 (94%) of the 5433 children who completed the initial 3-year follow-up were re-enrolled in the extension study. Over 5 years, the incidence of clinical malaria per 1000 person-years at risk was 313 in the SMC alone group, 320 in the RTS,S/AS01E-alone group, and 133 in the combined group. The combination of RTS,S/AS01E and SMC was superior to SMC (protective efficacy 57·7%, 95% CI 53·3 to 61·7) and to RTS,S/AS01E (protective efficacy 59·0%, 54·7 to 62·8) in preventing clinical malaria. RTS,S/AS01E was non-inferior to SMC (hazard ratio 1·03 [95% CI 0·95 to 1·12]). The protective efficacy of the combination versus SMC over the 5-year period of the study was very similar to that seen in the first 3 years with the protective efficacy of the combination versus SMC being 57·7% (53·3 to 61·7) and versus RTS/AS01E-alone being 59·0% (54·7 to 62·8). The comparable figures for the first 3 years of the study were 62·8% (58·4 to 66·8) and 59·6% (54·7 to 64·0%), respectively. Hospital admissions for WHO-defined severe malaria were reduced by 66·8% (95% CI 40·3 to 81·5), for malarial anaemia by 65·9% (34·1 to 82·4), for blood transfusion by 68·1% (32·6 to 84·9), for all-cause deaths by 44·5% (2·8 to 68·3), for deaths excluding external causes or surgery by 41·1% (-9·2 to 68·3), and for deaths from malaria by 66·8% (-2·7 to 89·3) in the combined group compared with the SMC alone group. No safety signals were detected. Substantial protection against malaria was sustained over 5 years by combining seasonal malaria vaccination with seasonal chemoprevention, offering a potential new approach to malaria control in areas with seasonal malaria transmission. UK Joint Global Health Trials and PATH's Malaria Vaccine Initiative (through a grant from the Bill & Melinda Gates Foundation). For the French translation of the abstract see Supplementary Materials section.

Reinforcement Learning-Based Opportunistic Routing Protocol Using Depth Information for Energy-Efficient Underwater Wireless Sensor Networks

An efficient routing protocol is critical for the data transmission of Underwater Wireless Sensor Networks (UWSNs). Aiming to the problem of void region in UWSNs, this paper proposes a reinforcement learning-based opportunistic routing protocol (DROR). By considering the limited energy and underwater environment, DROR is a receiver-based routing protocol, and combines reinforcement learning with opportunistic routing to ensure real-time performance of data transmission as well as energy efficiency. To ensure reliable transmission when encountering void regions, a void recovery mechanism is designed to enable packets to bypass void nodes and continue forwarding. Furthermore, a relative Q-based dynamic scheduling strategy is proposed to ensure that packets can efficiently forward along the global optimal routing path. Simulation results show that the proposed protocol performs well in terms of end-to-end delay, reliability, and energy efficiency in UWSNs.

Triple trustworthiness evaluation for knowledge graph of industrial domain

The knowledge graph is widely used in industrial fields due to its structural characteristics. In order to reduce the cost of wrong decision-making, it is more important for the industrial knowledge graph to guarantee the quality and comprehensiveness of knowledge. In order to obtain the trustworthiness information of triples in the graph, this paper proposed a model to evaluate triple trustworthiness for industrial knowledge graphs(TT-IKG) and obtains the final score of triples by fusing the output of three sub-modules that investigate the local confidence of triples, the confidence of schema-matching and the confidence of global paths. Experiments on the real industrial knowledge graph of enterprises verified the effectiveness of the model, and the experimental results on the error detection and graph completion tasks of the knowledge graph show that the effect is better than that of other models.

Research on Unmanned Surface Vessel Aggregation Formation Based on Improved A* and Dynamic Window Approach Fusion Algorithm

The traditional A* and DWA fusion algorithm has three problems in the task of aggregation formation: one is the lack of meeting coordination strategy, the other is the inability to unify the terminal course, and the third is too many turning points in the global path. To solve the above problems, an improved algorithm is proposed. Firstly, by smoothing the global planning path, the stability of a USV heading in the navigation is improved. Then, by adding the key points of the global path, a guide path is formed to unify the terminal heading range. Finally, by adding an encounter coordination strategy, aggregation efficiency is improved. Simulation experiments were carried out in the Python environment based on this algorithm. The results show that the improved algorithm can improve the navigation obstacle avoidance ability of USVs and guide multiple USVs to finish the task of aggregation formation.

Global path planning for multiple AUVs using GWO

Global path planning for multiple AUVs using GWO

Global Path Planning of Unmanned Surface Vehicle Based on Improved A-Star Algorithm.

To make unmanned surface vehicles that are better applied to the field of environmental monitoring in inland rivers, reservoirs, or coasts, we propose a global path-planning algorithm based on the improved A-star algorithm. The path search is carried out using the raster method for environment modeling and the 8-neighborhood search method: a bidirectional search strategy and an evaluation function improvement method are used to reduce the total number of traversing nodes; the planned path is smoothed to remove the inflection points and solve the path folding problem. The simulation results reveal that the improved A-star algorithm is more efficient in path planning, with fewer inflection points and traversing nodes, and the smoothed paths are more to meet the actual navigation demands of unmanned surface vehicles than the conventional A-star algorithm.

Global Continuous Toolpath Planning with Controllable Local Directions

Toolpath planning for Continuous fiber reinforced thermoplastic composites (CFRTPCs) 3D printing is a challenging task, which is vital to decrease the process complexity. In this paper, we propose a global continuous path planning method for CFRTPCs. First, for the set of 2D connected regions generated by slicing, we establish an optimized print sequence with a minimum idle stroke under interference constraints. Second, a continuous Zigzag path generation algorithm with controlled deposition direction is constructed for each 2D connected region, and path optimization is used to lessen the uneven deposition spacing. Finally, A separation axis theorem is used to prevent interference during the printing process, and we use a genetic algorithm to choose the connection points between 2D paths in a given spatial printing sequence to make the minimum idle stroke, creating a global continuous path with controllable deposition direction. The method proposed in this paper can provide the ability to customize the local deposition path direction and ensure the continuity of the global path to avoid interference and reduce the post-processing workload, so it is suitable for continuous path planning and multi-physics field optimization of complex structures, which can fully utilize the advantages of high-performance fibers to improve the customization ability and the performance of the fabricated parts for 3D printing and provide support for the manufacturing of high-performance structures in different fields.