Exploration Strategy Research Articles

Multi-objective optimization with Q-learning for cruise and power allocation control parameters of connected fuel cell hybrid vehicles

Fuel cell hybrid vehicles (FCHVs) are significant for achieving zero carbon emissions. Connected FCHVs can leverage traffic information to collaboratively optimize cruise and power allocation control, enhancing various performance aspects. For urban driving scenarios, this paper introduces a multi-strategy series control architecture for longitudinal cruise and power allocation control in connected FCHVs. However, particle swarm optimization (PSO) algorithms face challenges in high-dimensional decision and objective spaces when optimizing multiple strategies. Additionally, manually preset PSO parameters hinder particle evolution from dynamically adapting to unknown multi-objective spaces, thereby limiting the development of multiple performance metrics. To address this issue, this paper proposes a Q-learning multi-objective PSO (QMOPSO) algorithm. This algorithm tackles high-dimensional optimization challenges by improving population initialization distribution and subpopulation division, and enables particles to dynamically adjust exploration strategies, thereby maximizing multiple objective performances. The results indicate that compared to a control scheme optimized with PSO under predefined driving conditions, the multi-strategy series control framework optimized with the QMOPSO algorithm improves tracking stability by 50.20%, driving comfort by 1.77%, fuel economy by 6.10%, and reduces power source degradation by 2.04% in urban driving scenarios. Compared to PSO and multi-objective PSO algorithms, the QMOPSO algorithm demonstrates superior trade-offs. This research provides a collaborative optimization solution for FCHVs in connected environments.

Assessment of the source rock potential in the Sirka and Giddi collieries of South Karanapura coalfield, Jharkhand, India: Insights from megaflora, palynology, and geochemistry

Permian deposits in the Indian Peninsula have long been a significant source of coal and have great potential for hydrocarbon exploration. Here we present results of megafloral, palynological, and geochemical analysis of the Late Artinskian-Kungurian sediments in the South Karanpura coalfield to assess hydrocarbon generation potential, kerogen types, depositional settings, and thermal maturation. The results suggest anoxic to oxic depositional environments with fluctuating water levels, influenced by terrestrial inputs. The Sirka colliery is considered to be most favorable for hydrocarbon generation due to the palaeodepositional setting dominated by flooded palaeomires. The dominance of degraded organic matter and the rarity of opaque phytoclasts suggest type II/III to type III kerogen material in the palaeomire of the Srika succession, characterized by low-energy dysoxic to anoxic conditions. The thermal maturation values (Tmax 429℃) and the production index (0.01–0.02) indicate that the Sirka area has immature kerogen, but the Giddi colliery has a relatively higher Tmax (average 435℃) placing the studied sample within the mature zone. However, due to deposition in the oxidized swamp, Giddi C has poor potential for hydrocarbon generation, showing that type III/IV material has charcoal input into the sediments. Our findings contribute to global knowledge of coal formations’ oil and gas storage capacity, which has implications for energy resource assessment and exploration strategies.

Impact of clay minerals on reservoir sandstone properties: comparative study in Colombian eastern cordillera and middle Magdalena valley basins

The aim of this study is to examine how mineralogy influences the petrophysical properties, particularly porosity and permeability, of potential sandstone reservoirs in Colombia. It seeks to comprehensively understand how the presence of clay minerals impacts the overall quality of hydrocarbon reservoirs in the country. Samples of sandstones from reservoirs at various outcrops in the Eastern Cordillera and Middle Magdalena Valley basins were collected. Detailed analysis of mineralogy and petrographic characteristics of the samples was conducted through various analytical techniques such as transmitted light microscopy, X-ray diffraction, and scanning electron microscopy. Porosity and permeability were measured using automated permeametry and porosimetry equipment. The predominant composition of the analyzed reservoir rocks comprises quartz (45-50%), feldspar (35-40%), and clays (10-20%). These rocks were categorized into two distinct groups based on their permeability (K) and porosity (Φ), ranging from 0.009 to 29.220 mD and 1.88 to 20.75%, respectively. The presence of illite correlated with a reduction in both porosity and permeability, highlighting its negative impact on reservoir quality. Conversely, an elevated concentration of kaolinite was associated with favorable porosity and permeability. Samples with feldspar sericitization demonstrated inferior hydrocarbon storage quality. This study provides a deeper understanding of how mineralogy affects the petrophysical properties of sandstone reservoirs in Colombia. These findings are crucial for guiding exploration and production strategies in the Colombian oil industry, especially in challenging geological environments like those studied.

Toddlers strategically adapt their information search

Adaptive information seeking is essential for humans to effectively navigate complex and dynamic environments. Here, we developed a gaze-contingent eye-tracking paradigm to examine the early emergence of adaptive information-seeking. Toddlers (N = 60, 18-36 months) and adults (N = 42) either learnt that an animal was equally likely to be found in any of four available locations, or that it was most likely to be found in one particular location. Afterwards, they were given control of a torchlight, which they could move with their eyes to explore the otherwise pitch-black task environment. Eye-movement data and Markov models show that, from 24 months of age, toddlers become more exploratory than adults, and start adapting their exploratory strategies to the information structure of the task. These results show that toddlers’ search strategies are more sophisticated than previously thought, and identify the unique features that distinguish their information search from adults’.

Unbiased analysis of spatial learning strategies in a modified Barnes maze using convolutional neural networks

Assessment of spatial learning abilities is central to behavioral neuroscience and a useful tool for animal model validation and drug development. However, biases introduced by the apparatus, environment, or experimentalist represent a critical challenge to the test validity. We have recently developed the Modified Barnes Maze (MBM) task, a spatial learning paradigm that overcomes inherent behavioral biases of animals in the classical Barnes maze. The specific combination of spatial strategies employed by mice is often considered representative of the level of cognitive resources used. Herein, we have developed a convolutional neural network-based classifier of exploration strategies in the MBM that can effectively provide researchers with enhanced insights into cognitive traits in mice. Following validation, we compared the learning performance of female and male C57BL/6J mice, as well as that of Ts65Dn mice, a model of Down syndrome, and 5xFAD mice, a model of Alzheimer’s disease. Male mice exhibited more effective navigation abilities than female mice, reflected in higher utilization of effective spatial search strategies. Compared to wildtype controls, Ts65Dn mice exhibited delayed usage of spatial strategies despite similar success rates in completing this spatial task. 5xFAD mice showed increased usage of non-spatial strategies such as Circling that corresponded to higher latency to reach the target and lower success rate. These data exemplify the need for deeper strategy classification tools in dissecting complex cognitive traits. In sum, we provide a machine-learning-based strategy classifier that extends our understanding of mice’s spatial learning capabilities while enabling a more accurate cognitive assessment.

Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.

A Review of Integration Techniques of Multi-Geoscience Data-Sets in Mineral Prospectivity Mapping

In every sphere and utility aspects of human life, there is need of metals and construction materials. Minerals which are below the near subsurface is almost explored on the basis of direct geospatial evidences. There is high demand of metals and other materials which are mined below the surface of earth In the current landscape, there's a demand for faster and more precise exploration strategies, particularly emphasizing Greenfield exploration and deep-seated mineralization. This paper conducts a comprehensive review of existing methodologies for integrating multi-geoscience datasets aimed at mineral prognostication, with a focus on identifying the most precise and authentic Artificial Intelligence (AI) - based data integration techniques. Additionally, it offers insights into the current status of mineral exploration in India and the global evolution of data integration practices. Several types of geoscientific datasets i.e. geological, geophysical, geochemical and geospectral data have to be organized in geospatial domain for meaningful mineral exploration outcome. These datasets have been processed to extract exploratory indicator layers for data integration are called Mineral Prospectivity Mapping (MPM). Indeed, MPM is a multiple criterion decision making (MCDM) task which provide a predictive model for categorizing of sought areas in terms of ore mineralization. There after based upon Geological factors i.e. lithology, structure, shear & fault zones, alteration zones etc. of sought mineralized area, selection of drilling parameters (depth, angle, level, type, rpm, feed) is done for resource assessment. Literature survey suggests that minerals exploration by integrated approach on the basis of these datasets is still poorly performed. It has been gathered that knowledge-driven data integration using Fuzzy Gamma Operator and Multiclass Index Overlay method is best suited for mineral exploration. In past, few researchers of other countries have exploited data integration approach with encouraging results. Despite the abundance of data available in India, this approach has not been utilized very successfully and no standard protocols exist even for decision making for drilling operation. Thus, it's evident that employing the Fuzzy Inference System (FIS) algorithm, particularly utilizing the Fuzzy Gamma Operator and Multiclass Index Overlay integration method, remains underutilized in designing standardized operating procedures (SOP) for mineral exploration in India and decision-making for drilling operations. This approach holds promise for minimizing time lag and optimizing resources such as manpower, instruments, and finances.

Task execution latency minimization for energy-sensitive IoTs in wireless powered mobile edge computing: A DRL-based method

Wireless powered mobile edge computing (MEC) has become a vital component of future 6G networks, offering efficient computational capabilities to internet of things (IoT) devices and ensuring a stable energy supply. In this paper, we propose an innovative design for a wireless powered MEC-assisted energy-sensitive IoT systems. The system integrates wireless power technology (WPT), allowing IoT devices to efficiently perform tasks under wireless power supply without consuming their battery energy. Therefore, we formulate a dynamic computational task execution latency minimization (DCTELM) problem to tackle the impacts of unpredictable energy requirements and potential communication latency on resource allocation. We also analyze the differences between time division multiple access and non-orthogonal multiple access communication protocols. Given the complexity and dynamic nature of this problem, we define it as a mixed integer nonlinear programming problem. To address this problem, we introduced an enhanced deep reinforcement learning (DRL) algorithm. First, the DCTELM problem is transformed into a Markov decision process (MDP), which simplifies the structure of the problem. Then, after determining the offloading decision, we reveal the convex relationship between resource allocation and task latency. Furthermore, the action space of MDP is further reduced, and a multi-head proximal policy optimization (PPO) algorithm is proposed to deal with the simplified MDP problem. This process reduces the complexity of problem solving, decreases the consumption of computational resources. Simulation results demonstrate that our method outperforms other baselines in terms of computation latency, particularly with the introduction of an exploration strategy during the training phase, which significantly reduces task latency.

Intelligent decision-making for a “Three-Variable” frequency-hopping pattern based on OC-CDRL

The frequency hopping pattern of the existing frequency hopping communication system is not designed according to the electromagnetic interference environment, resulting in blind anti-jamming. Therefore, to address this problem, a “three-variable” frequency-hopping pattern is proposed, where the frequency, hopping rate, and instantaneous bandwidth of the frequency-hopping signal vary randomly based on the background electromagnetic interference. The decision-making problem of the “three-variable” frequency-hopping pattern is modeled as a Markov decision process (MDP) by constructing the state-action-reward tuple. The designed frequency varies continuously within a small frequency band selected from a pseudo-random sequence to alleviate the problem of dimension explosion in decision-making. At the same time, discrete values for the hopping rate and instantaneous bandwidth are designed. To solve this MDP problem efficiently, a combined deep reinforcement learning algorithm (OC-CDRL) based on optimistic exploration and conservative estimation is proposed, which combines the features of TD3 and D3QN algorithms and designs the corresponding states, actions, and rewards to deal with continuous and discrete action spaces, respectively. To address the problem that the D3QN algorithm tends to fall into local optimal solutions, an optimistic exploration strategy (OES) for action selection is proposed to improve the degree of exploration. Moreover, the loss function is improved by conservatively estimating state–action pairs outside the experience replay buffer, reducing the overestimation of the optimistic action-value function and increasing the stability and convergence of the algorithm. Comparative simulation results of the algorithms in different electromagnetic interference environments show that the OC-CDRL algorithm effectively avoids most regions with higher interference and has better adaptability and anti-jamming capability.

Integrating soil geochemistry and machine learning for enhanced mineral exploration at the dayu gold deposit, south China block

Combining traditional geochemical methods with advanced analytical techniques is a hallmark of contemporary exploration efforts. This study explores the intricate geological dynamics of the Dayu gold deposit, located in the Dayao Uplift of the South China Block. Using a multidisciplinary approach that includes soil geochemistry, conventional geochemical methods and advanced computational techniques such as machine learning and Discriminant Projection Analysis (DPA), we aim to uncover the deposit formation information. Our results reveal a complex pattern of element anomalies, which serve as a geochemical fingerprint of the Au mineralization processes that shaped the deposit over geological time. Principal Component Analysis (PCA) and cluster analysis on soil samples highlight significant correlation among Au and its pathfinder elements. By leveraging the predictive capabilities of machine learning algorithms, particularly Convolutional Neural Networks (CNN), we improve exploration strategies, enhance the precision of target delineation and guide sampling efforts. DPA further identifies distinct discriminant functions, aiding in group differentiation and providing insights into prospective mineralization zones. This study exemplifies the integration of traditional and innovative methodologies, offering a pathway to a deeper understanding of mineralization processes and improving the effectiveness of exploration in complex geological terrains. The findings advance our knowledge of the Dayu gold deposit and demonstrate the potential of these integrated approaches in similar geological settings.

Reinforced robotic bean optimization algorithm for cooperative target search of unmanned aerial vehicle swarm

Increasing attention has been given to the utilization of swarm intelligent optimization algorithms to facilitate cooperative target search of unmanned aerial vehicle swarm (UAVs). However, there exist common issues associated with swarm intelligent optimization algorithms, which are low search efficiency and easy to trap in local optima. Simultaneously, the concentrated initial positioning of UAVs increase the probability of collisions between UAVs. To address these issues, this paper proposes a reinforced robotic bean optimization algorithm (RRBOA) aimed at enhancing the efficiency of UAVs for cooperative target search in unknown environments. Firstly, the algorithm employs a region segmentation exploration strategy to enhance the initialization of UAVs, ensuring a uniform distribution of UAVs to avoid collisions and the coverage capability of UAVs search. Subsequently, a neutral evolution strategy is incorporated based on the spatial distribution pattern of population, which aims to enhance cooperative search by enabling UAVs to freely explore the search space, thus improving the global exploration capability of UAVs. Finally, an adaptive Levy flight strategy is introduced to expand the search range of UAVs, enhancing the diversity of UAVs search and then preventing the UAVs search from converging to local optima. Experimental results demonstrate that RRBOA has significant advantages over other methods on nine benchmark simulations. Furthermore, the extension testing, which focuses on simulating pollution source search, confirms the effectiveness and applicability of RRBOA

Tác động của chiến lược tìm việc đến kết quả tìm việc của thanh niên trên địa bàn Bình Dương

This study explores the impact of exploratory, haphazard, and focused strategies on three aspects of re-employment outcomes, including income satisfaction, compatibility between abilities, and organizational commitment. Data were collected from 384 young people who were unemployed and found work in the past three months in Binh Duong. The results of hypothesis testing show that the exploratory strategy has a positive impact on organizational commitment and a negative effect on the level of income satisfaction. A focused strategy positively impacts all three aspects of re-employment outcomes. The haphazard strategy only hurt organizational commitment. The results suggest implications for policymakers and unemployed individuals in building intelligent job search strategies that are ready to adapt to changes in the labor market.

A review of coordinated multi-robot exploration

Abstract A multiple robots system provides advantages over a single robot when it comes to exploring an unfamiliar area. Due to its applications in localization and mapping, search and rescue, etc. This technology has attracted a lot of study interest. The main challenge is properly distributing target points among several robots so that they can each investigate a separate section of the environment simultaneously and ensure the shortest possible total exploration time. However, more work still needs to be done to incorporate research issues in a real setting, such as how to eliminate duplicate exploration areas by giving robots multiple target locations or how to avoid unanticipated barriers in new situations. The allocation algorithms, motion planning techniques, and exploration strategies used in multi-robot collaboration (CME) are reviewed in this paper. Additionally, it provides an explanation of the methods research teams employ to optimize traditional whale optimization algorithm (WOA), how they enhance exploration performance and speed, and a comparative analysis of these methods. We also discussed a cooperative exploration method based on dynamic Voronoi partitioning. However, we also need some obstacle avoidance algorithms to help understand the gaps and issues that will need to be solved in the upcoming years, namely how to handle unexpected impediments in unfamiliar surroundings. Lastly, a summary of potential future lines of inquiry and uses is given.

Application of an improved simulated annealing algorithm in satellite network routing

Abstract Satellite communication is integral to modern communication systems, yet navigating the intricacies of routing in networks characterized by high loads and dynamic evolution poses a significant challenge. This research is dedicated to enhancing satellite communication routing algorithms, with the goal of bolstering network efficiency and amplifying the success rate of business transmissions. Our approach introduces an improved simulated annealing algorithm incorporating adaptive temperature control and innovative path neighborhood exploration strategies. This novel algorithm is adept at adjusting to ever-shifting network topologies and meeting various business requirements. Our simulation results reveal that this refined algorithm outperforms traditional annealing methods in several critical aspects. It elevates the success rate of business transmissions, reduces the average number of forwardings by 6.546% across various load scenarios, and diminishes system response time by 15.05%, thereby marking a substantial improvement in network performance.

Effects of stepwise depressurisation rate on methane hydrate dissociation dynamics at pore scale using microfluidic experiments

Methane hydrate, recognized as a potential alternative energy source, faces challenges in achieving efficient production. Stepwise depressurisation has emerged as a viable technique for enhancing productivity, yet optimizing the depressurisation rate remains a complex issue. This study employs pore-scale experiments using microfluidic chips to examine the dissociation characteristics of methane hydrate under varying stepwise depressurisation rates. At a high depressurisation rate of 0.4 MPa/15 min, the dissociation process exhibits a three-stage pattern: stabilisation, rapid dissociation, and slow dissociation. During these stages, the mass transfer limitation in the water phase significantly impedes the dissociation rate. The gas-water migration triggered by depressurisation can mitigate this limitation. As the depressurisation rate is reduced to 0.2 MPa/15 min, the rapid dissociation stage splits into two due to a decrease in gas-water migration intensity. The dissociation rate decreases by 45% compared to the 0.4 MPa/15 min case. This results from an insufficient driving force for dissociation, necessitating another depressurisation step. Further reduction of the depressurisation rate to 0.1 MPa/15 min leads to a less pronounced gas-water migration, which is inadequate to significantly counteract the mass transfer limitation. As a result, the rapid dissociation phase occurring at a depressurisation rate of 0.4 MPa/15 min, which exhibits an average dissociation rate of 0.2%/s, subsequently transitions into a more uniform and slower dissociation stage, where the average rate of dissociation declines to below 0.07%/s. The experimental results offer valuable insights for guiding hydrate exploration strategies during the stepwise depressurisation process by adjusting the depressurisation rate to regulate production.

Dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms because it requires the population to quickly track the changing pareto-optimal set (PS) under constrained conditions while maintaining the feasibility and good distribution of the population. To address these challenges, this paper proposes a dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement (CEDE), in which we have made improvements to both the static optimization and dynamic response parts, innovatively utilizing the valuable information latent in the optimization process to help the population evolve more comprehensively. The static optimization involves the co-evolution of three populations, through which their mutual synergy can more comprehensively identify potential true PS and provide more useful historical information for dynamic response. Additionally, to prevent the elimination of potentially valuable infeasible individuals (i.e., individuals that are not dominated by feasible individuals) due to pareto domination, we employ an archive set to store and update these individuals. When the environment changes, to effectively enhance population diversity under complex dynamic constraints and help the population to respond quickly to changes, we propose a diversity enhancement strategy, which includes a diversity maintenance strategy and a center point-based exploration strategy. This strategy effectively enhances population diversity in complex and changing environments, helping the population respond quickly to changes. The effectiveness of the algorithm is validated through two test sets. The experimental results show that CEDE can effectively use valuable information to cope with complex dynamic constraint environments. Compared with several of the most advanced algorithms, it is superior in 94% of the test problems, demonstrating strong competitiveness in handling DCMOPs.

Claustrum neurons projecting to the anterior cingulate restrict engagement during sleep and behavior

The claustrum has been linked to attention and sleep. We hypothesized that this reflects a shared function, determining responsiveness to stimuli, which spans the axis of engagement. To test this hypothesis, we recorded claustrum population dynamics from male mice during both sleep and an attentional task (‘ENGAGE’). Heightened activity in claustrum neurons projecting to the anterior cingulate cortex (ACCp) corresponded to reduced sensory responsiveness during sleep. Similarly, in the ENGAGE task, heightened ACCp activity correlated with disengagement and behavioral lapses, while low ACCp activity correlated with hyper-engagement and impulsive errors. Chemogenetic elevation of ACCp activity reduced both awakenings during sleep and impulsive errors in the ENGAGE task. Furthermore, mice employing an exploration strategy in the task showed a stronger correlation between ACCp activity and performance compared to mice employing an exploitation strategy which reduced task complexity. Our results implicate ACCp claustrum neurons in restricting engagement during sleep and goal-directed behavior.

A novel domain adaptation method with physical constraints for shale gas production forecasting

Effective forecasting of shale gas production is essential for optimizing exploration strategies and guiding subsequent fracturing. However, in the new development of shale gas blocks, two main challenges are encountered: (1) data is insufficient, and (2) the dynamic production characteristics of shale gas wells, influenced by factors such as reservoirs and engineering, exhibit complex non-linear and non-stationary features. The inherent black-box nature of deep learning models raises concerns among decision-makers about the reliability of results. The current artificial intelligence model overlooks these factors, resulting in limitations in the accuracy and interpretability of the model. To address these problems, a novel domain adaptation methodology is proposed using physical constraints. First, production data from the source domain is segmented into multiple subdomains to enhance sample diversity. Subsequently, the positive transfer learning subdomains are identified by comparing maximum mean discrepancy (MMD) and global average distance metrics. Then, we integrate all transferable knowledge to create a more comprehensive target model. Finally, by incorporating drilling, completion, and geological data as physical constraints, we develop a hybrid model consisting of a multi-layer perceptron (MLP) and a Transformer, aiming to maximize interpretability, which is proved through comparison of symbolic transfer entropy (STE). The performance of the proposed method is experimentally validated on shale gas production data from two blocks in China. The average RMSE, MAE, and R2 on the target domain are 0.2454 (104 m3/d), 0.1552 (104 m3/d), and 0.88, respectively. These values are significantly superior to the traditional methods. Additionally, we demonstrate the superiority of our method in terms of causality through a comparison with Granger causality. The interpretability of static and dynamic data in the prediction process was studied using zero-value masking and attention mechanisms, respectively. Experimental results demonstrate the effectiveness and superiority of the proposed method for forecasting shale gas production under data insufficiency.

Pengaruh Digital Marketing terhadap Minat Beli pada 3 Cafe di Kabupaten Jember

This research aims to assess the impact of continued promotions on purchasing interest at three bistros operating in Jember, namely Rangkum Espresso, At maison, and Mr. Rokar Bistro. This exploration strategy includes investigating information from advanced promotional connections, for example, use of virtual entertainment, online missions, and customer reactions. Measurable test results show that computerized promotions have an impact on purchasing interest in the three bistros, with an importance level of 0.001. Overall, computerized promotions play a big role in shaping buyers' buying interest in these bistros. This exploration adds additional understanding of how the use of computerized media can strengthen advertising techniques and influence purchasing choices in the local bistro industry. The functional consequence of this discovery is that restaurateurs and exhibition specialists can most likely use computerized stages to increase customer purchasing interest and create areas of strength for attendance. Keywords: Buying Interest, Cafe, Digital Marketing, Social Media

Autonomous Exploration Method of Unmanned Ground Vehicles Based on an Incremental B-Spline Probability Roadmap.

Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration.