Random Tree Algorithm Research Articles

An Improved RRT Path-Planning Algorithm Based on Vehicle Lane-Change Trajectory Data

The Rapidly-exploring Random Tree (RRT) algorithm faces issues in path planning, including low search efficiency, high randomness, and suboptimal path quality. To overcome these issues, this paper proposes an improved RRT planning algorithm based on vehicle lane change trajectory data. This algorithm dynamically adjusts the sampling area based on road environment and trajectory change laws, allowing the random tree to sample within an effective area, thereby improving the algorithm’s sampling efficiency. After determining the sampling area, a sampling point optimization strategy is used to enhance sampling quality, resulting in a smoother and more executable path. Finally, the vehicle is processed in a standardized manner to further improve path safety. Simulation results indicate that, compared to the original RRT algorithm, the improved version reduces nodes, planning time, and path length by 12.77%, 64.79%, and 12.87%, respectively. It also improves path smoothness and more closely aligns with actual lane-change trajectories, demonstrating the effectiveness and executability of the improved algorithm.

A Machine Learning Approach for Modeling the Spatial-temporal Propagation Pattern of COVID-19

Abstract. Predicting infections resulting from epidemiological contagions involves a complex therapeutic endeavor that necessitates the consideration of various factors within the available data. COVID-19, a globally recognized highly dangerous disease, presents an opportunity to effectively understand its patterns and spatial determinants to combat similar ailments. This study seeks to employ a deep neural network and several machine learning techniques to identify the crucial factors influencing COVID-19 infections and to forecast the spatial-temporal spread of the disease. Previous research has focused on predicting outcomes using a limited set of 8 patient variables. To contribute to this field, the current investigation analyzes a dataset comprising 47,029 records of infected individuals who sought medical attention, with 18,433 of them testing positive for the coronavirus and requiring hospitalization. The study explores the prediction of illness and hospitalization needs based on geographical locations by utilizing machine learning algorithms such as Random Forest, Naive Bayes, Decision Tree, and K-Nearest Neighbor. The machine learning process commences with input data (comprising the 47,029 records), enabling the machine to identify patterns within the dataset and subsequently make informed decisions based on these patterns and insights gained. The primary objective is to allow the machine to autonomously learn and refine its predictions without human intervention. Applying these algorithms to COVID-19 data for spatial-temporal prediction revealed that the Random Forest algorithm achieved the highest accuracy of 0.84, while the Simple Bayes algorithm exhibited the lowest accuracy at 0.50. Additionally, the study compared random forest, decision tree, simple bayes, and K-nearest neighbor algorithms to predict the severity of road accidents.

Optimization of Pancreatic Cell Subset Classification in Images from Multiplex Immunofluorescence Imaging Platforms

Understanding the natural history of type 1 diabetes (T1D) has been hampered by limited access to the target organ (i.e., the pancreas) in living people. To make the most of these precious tissues, cyclic imaging platforms such as Miltenyi MACSima, which increase the number of markers that can be examined per section, are becoming increasingly popular. Such advances in spatial biology require reproducible cell classification algorithms. Random Tree (RT) algorithm was previously found to be the most reliable approach for differentiating exocrine versus endocrine pancreas compartments, yet an unmet need exists for an approach that can provide accurate cell type classification within the endocrine compartment. It was hypothesized that a core set of markers could be used to maximize classification accuracy. To this end, pancreatic sections were stained with fluorescent antibodies against various markers and images were captured using the MACSima platform. RT algorithm was trained in QuPath to classify endocrine, exocrine, and vascular cell clusters in n=3 pancreas donors across a total of n=10 ROIs. Classification accuracy will be determined by comparison to ground-truth labels annotated by an expert in the field. Markers that maximize classification accuracy will be included on future pancreas runs to standardize and streamline downstream analyses.

An Innovative Path Planning Algorithm for Complex Obstacle Environments with Adaptive Obstacle Density Adjustment: AODA-PF-RRT*

To address the limitations of low node utilization and inadequate adaptability in complex environments encountered by Rapidly-exploring Random Tree (RRT) algorithms during the expansion phase, this study presents an enhanced path planning algorithm—AODA-PF-RRT* (Adaptive Obstacle Density Adjustment-PF-RRT*). The proposed algorithm implements a random extension strategy for nodes that fail collision detection, thereby improving node efficiency. Furthermore, it dynamically partitions the area surrounding sampling points and calculates local obstacle density in real time. By leveraging this density information, the algorithm flexibly adjusts both the number of expansion points and the dichotomy threshold, significantly enhancing its responsiveness to environmental changes. We rigorously demonstrate the algorithm’s probabilistic completeness and asymptotic optimality. Simulation and benchmarking results demonstrate that the AODA-PF-RRT* algorithm not only generates smooth and high-quality paths compared to existing algorithms but also maintains low computational costs in complex environments, showcasing exceptional stability and robustness.

Predicting Livestock Farmers’ Attitudes towards Improved Sheep Breeds in Ahar City through Data Mining Methods

Sheep breeding is one of the most important economic activities in Ahar City, Iran. However, due to traditional production techniques, livestock farmers face the problem of low productivity. To address this issue, traditional breeds can be replaced with improved and high-yielding ones; in the first stage, this requires the acceptance of these new sheep breeds by the region’s ranchers. This research aimed to evaluate the attitudes of the livestock breeders of Ahar City towards the improved breeds of sheep and the influential factors. We collected data through in-person interviews using a simple random sampling method, surveying 100 sheep breeders in Ahar. The breeders were categorized into three groups based on their attitudes towards improved breeds: negative, indifferent, and positive. Next, we employed data mining-based methods, including multilayer perceptron neural networks, random forest, and random tree algorithms. These helped identify essential variables affecting ranchers’ attitudes. The results showed that several factors contribute to the ranchers’ philosophy, with the number of sheep sold in the past year and the total sheep ownership being the most significant ones. Comparing statistical evaluation criteria, we found that the random tree algorithm outperformed other methods in predicting and classifying livestock farmers, achieving a prediction accuracy rate of 86% for a sample of 100 farmers. Based on our findings, promoting training courses and raising awareness about the benefits of breeding new sheep breeds, along with providing facilities and credits based on economic conditions, can foster a positive attitude among herders. Increasing the number of sheep owned and improving marketing strategies can further enhance this positive outlook.

Comparative analysis of machine learning techniques for detecting potability of water

This research aims to evaluate the effectiveness of machine learning algorithms in determining the potability of water. In the study, a total of 3276 water samples were analyzed for 10 different features that determine the potability of water. Besides that, the study's consideration is to evaluate the impact of trimming, IQR, and percentile methods on the performance of machine learning algorithms. The models were built using nine different classification algorithms (Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbors, Support Vector Machine, AdaBoost, and Bagging Classifier). According to the results, filling the missing data with the population mean and handling outliers with Trimming and IQR methods improved the performance of the models. Random Forest and Decision Tree algorithms were the most accurate in determining the potability of water. The findings of this research are of high importance to sustainable water resource management and serve as a crucial input for the decision-making process on the quality of water. The study also offers an example for researchers working on datasets that contain missing values and outliers.

Robot Manipulator Minimum Jerk Trajectory Planning Based on the Improved Dung Beetle Optimizer Algorithm

Trajectory planning of robotic manipulators in complex environments involves generating smooth and collision-free paths, and key aspects to consider include dynamic environment perception, path planning, trajectory smoothing and optimization, and obstacle avoidance. This paper discusses different algorithms and finally proposes a fusion of the improved Dung Beetle Optimizer (IDBO) algorithm using chaotic mapping, sinusoidal random mutation, and nonlinear convergence strategies with the bidirectional Rapidly exploring Random Tree (RRT) algorithm to achieve manipulator trajectory planning and minimum jerk optimization trajectory. In the experiment, the IDBO algorithm was compared with other heuristic algorithms. The path obtained was shortened by 9.18% on average, and the calculation time was shortened by 33.81% on average. Then, the paper explored Cartesian coordinate space trajectory planning and joint space trajectory planning when the robot was working in 3D space, and verified the superiority of the latter. In controlling the movement of the robot, by combining the minimum angle jerk planning and the bidirectional RRT obstacle avoidance algorithm to drive the joint angle parameters obtained by the spatial trajectory planning, the trajectory of the robot manipulator can be smoother, more efficient, and avoid obstacles in complex 3D space. This research helps improve the performance, safety, and technical support of robots, and is of great research significance.

Motion primitives and sample-based trajectory planning for fixed-wing unmanned aircraft

Navigating fixed-wing unmanned aircraft presents significant challenges, primarily stemming from the intricate dynamics involved and the demand for rapid planning. In this paper, we introduce an efficient approach that concurrently addresses nonlinear control and trajectory planning challenges. To tackle the control problem, we introduce a computationally efficient motion primitive that effectively maps feasible targets to control inputs. As for trajectory planning, we propose an enhanced, rapidly exploring, random tree algorithm capable of generating high-quality trajectories with minimal exploration. Our primary motivation is to seamlessly integrate the introduced motion primitive with a sample-based trajectory-planning technique, providing a comprehensive solution to the intricate challenges posed by nonlinear control and trajectory planning for fixed-wing unmanned aircraft. Experimental results showcase our approach’s superiority, delivering enhanced control precision, faster planning speeds, and improved planning quality.

Obstacle avoidance planning for industrial robots based on singular manifold splitting configuration space

SummaryObstacle avoidance planning is the primary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C‐space) provide the pose information of any part of the manipulator and are preferentially considered in motion planning. However, it is difficult to express the environmental information directly in the high dimensional C‐space, limiting the application of C‐space obstacle avoidance planning. This paper proposes a singular manifold splitting C‐space method and designs a compatible obstacle avoidance strategy. The specific method is as follows: first, according to the specific structure of industrial robots, arm‐wrist separation obstacle avoidance planning is proposed to fix the robot as a 3R manipulator to reduce the dimension of C‐space. Next, the C‐space is segmented according to the singular manifolds, and the unique domain is delineated to complete the streamlining of the volume of the C‐space. Then, with the help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo‐obstacle map. Industrial robots' obstacle avoidance planning is completed based on the pseudo‐obstacle map combined with an improved Rapidly‐Exploring Random Trees (RRT) algorithm. This method dramatically improves the efficiency of obstacle avoidance planning in the C‐space and avoids the effect of singularities on industrial robots. Finally, the effectiveness of the method is verified by physical experiments.

Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.

Predicting Consumer Behavior in E-Commerce Using Decision Tree: A Case Study in Malaysia

Understanding and predicting consumer behavior will help e-commerce businesses improve customer satisfaction and devise better marketing strategies. This study is intended to explore the use of decision tree algorithms in predictions of consumer purchase behavior in the e-commerce platform in Malaysia. Comparing the performances of J48, Random Tree, and REPTree decision tree models using an online shopper dataset collected by surveying 560 Malaysians, on various aspects like accuracy, precision, recall, and F1 score. Results indicate that the highest accuracy has been achieved with the Random Tree algorithm, outperforming J48 and REPTree. The results will, therefore, form the basis upon which e-commerce can restrategize its marketing programs for better customer engagement. This is an important study in that it shows the efficacy of applying a decision tree algorithm to understand customer behavior in the context of Malaysia and adds to the growing body of knowledge in predictive analytics in e-commerce.

A holistic approach to software fault prediction with dynamic classification

Software Fault Prediction is a critical domain in machine learning aimed at pre-emptively identifying and mitigating software faults. This study addresses challenges related to imbalanced datasets and feature selection, significantly enhancing the effectiveness of fault prediction models. We mitigate class imbalance in the Unified Dataset using the Random-Over Sampling technique, resulting in superior accuracy for minority-class predictions. Additionally, we employ the innovative Ant-Colony Optimization algorithm (ACO) for feature selection, extracting pertinent features to amplify model performance. Recognizing the limitations of individual machine learning models, we introduce the Dynamic Classifier, a ground-breaking ensemble that combines predictions from multiple algorithms, elevating fault prediction precision. Model parameters are fine-tuned using the Grid-Search Method, achieving an accuracy of 94.129% and superior overall performance compared to random forest, decision tree and other standard machine learning algorithms. The core contribution of this study lies in the comparative analysis, pitting our Dynamic Classifier against Standard Algorithms using diverse performance metrics. The results unequivocally establish the Dynamic Classifier as a frontrunner, highlighting its prowess in fault prediction. In conclusion, this research introduces a comprehensive and innovative approach to software fault prediction. It pioneers the resolution of class imbalance, employs cutting-edge feature selection, and introduces dynamic ensemble classifiers. The proposed methodology, showcasing a significant advancement in performance over existing methods, illuminates the path toward developing more accurate and efficient fault prediction models.

A Systematic Review of Rapidly Exploring Random Tree RRT Algorithm for Single and Multiple Robots

Abstract Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review’s identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement.

GVP-RRT: a grid based variable probability Rapidly-exploring Random Tree algorithm for AGV path planning

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5–69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

Evaluation of Different Classification Algorithms for Land Use Land Cover Mapping

For efficient sustainable management and monitoring landscape changes over times, reliable land use land cover (LULC) mapping using the most accurate classification algorithms is required. Increasing innovative classification algorithms and satellite data demands finding the most suitable classifier to create accurate maps of different features efficiently. The challenge addressed in this study is to identify the most accurate algorithm for classifying and generating reliable LULC. The objective of this research was to identify the best classification among several algorithms both overall and in each individual class by using ArcGIS Pro and Google Earth Engine with Landsat 8 and Sentinel-2 datasets for Ranya city as the study area. Support vector machine (SVM), maximum likelihood, random tree, classification and regression tree, K-Nearest Neighbor and iterative self organizing cluster algorithms were used to classify the satellite image of the study area. The kappa coefficient matrix was used to assess the performance of each classifier and method. The study showed that the random tree algorithm achieved highest overall accuracy using Sentinel-2 with 83%. Meanwhile, when the specific class accuracy is priority, the result suggests the use of SVM algorithm using Sentinel-2 for building footprint extraction with 92% accuracy. The result also showed that the outcomes of most algorithms were better using Sentinel-2 rather than Landsat 8, making Sentinel-2 more suitable for accurate LULC mapping. The outcomes of the research assessed different classification algorisms to find the best algorithms and methods that can be used to generate accurate and efficient LULC maps.

Enhancing Adaptability and Autonomy in Cooperative Selective Compliance Assembly Robot Arm Robots: Implementation of Coordination and Rapidly Exploring Random Tree Algorithms for Safe and Efficient Manipulation Tasks

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots’ performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics.

Bio-inspired machine-learning aided geo-magnetic field based AUV navigation system

The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers.

Real-time path planning for autonomous vehicle off-road driving.

Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others. In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning. The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Research on Path Planning of a Mining Inspection Robot in an Unstructured Environment Based on an Improved Rapidly Exploring Random Tree Algorithm

To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (PRM) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the PRM algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm.

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In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRTX\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\ ext {X}$$\\end{document}, which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time.