When entering an unfamiliar environment, animals usually sweep off their surroundings to identify points of interest. In search and rescue robotics, autonomous exploration requires both coarse mapping of unknown areas and detailed target detection, which poses a significant challenge in balancing these tasks. To that end, we propose a target-aware robotic exploration framework that prioritizes both exploration efficiency and search completeness through three components: First, considering the computational limitations of robotic platforms, a lightweight 3D target detection method with post-fusion is introduced to detect target positions in real time. Secondly, we propose a target-aware viewpoint generation approach that integrates information gain and inspection gain to identify promising viewpoints for thorough target searches. Lastly, since a detailed examination of the environment demands numerous viewpoints, we propose a heuristic-based active exploration framework that employs a hierarchical structure to optimize exploration gain, traveling distance, and path smoothness to maximize the utility function of viewpoint sequences and ultimately find the optimal path. Extensive simulations and real-world experiments demonstrate our framework significantly enhances target search capabilities, achieving a 13 % average improvement in exploration efficiency over existing methods.

This paper proposes a model-free steering control method to address the path tracking challenges of Hydraulic Center-articulated Scooptrams (HCS) in narrow underground mining environments. Due to the nonlinear and time-delay characteristics of the hydraulic steering system, the HCS exhibits response lag when executing control commands. The lag time demonstrates dynamic uncertainty influenced by operating conditions, hydraulic pressure, and load variations. To address this challenge, an adaptive steering control strategy is designed. This strategy leverages the geometric relationship between the HCS and the reference path to dynamically adjust the look-ahead distance, thereby compensating for the uncertainty caused by the hydraulic system lag. Additionally, the error is mapped to the actual control input in real-time through a feedback error controller, effectively correcting control errors caused by lag without relying on a complex hydraulic system model. The proposed method was experimentally validated in a full-scale simulated mining tunnel, demonstrating considerable robustness and precise path tracking performance under uneven terrain, heavy loads, significant initial error, and bidirectional movement. This method provides a viable solution for the autonomous navigation of the HCS.

Identifying traversable areas is a critical task for unmanned vehicles exploring safely through unstructured environments. In practice, the ambiguity in perceiving terrain traversability usually brings great challenges for autonomous exploration in unknown and uneven terrain, which often leads to conservative strategies or potential risk of vehicle damage, resulting in many unexplored areas in the environment. To that end, this paper proposes a plane-assisted autonomous robot exploration framework (PARE) to achieve maximum volume and safe autonomous exploration. The process is carried out by a three-step dual-layer framework: constructing a local tree using Plane-Assisted RRT* (PA-RRT*), calculating exploration gain based on terrain information, and maintaining a global search graph. Firstly, the planar feature metrics (flatness, sparsity, elevation variation, slope and slope variation) are introduced to determine the terrain traversability. Secondly, to completely explore the rugged environment, we propose a dual-layer exploration framework comprising local and global strategies. A local planner based on PA-RRT* is proposed to find the best path by evaluating the planar information and the volumetric gain within the local exploration tree. Meanwhile, a global planner constructed by graph is proposed to record unexplored nodes with high exploration gain from the local tree to ensure a high level of exploration volume. Extensive simulation and real-world experiments demonstrate that our method significantly outperforms existing frameworks, with an average improvement of more than 12% in exploration volume.

Summary With the vigorous development of the financial sector, financial risks are showing a tendency toward diversification, particularly regarding the customer credit risk of commercial banks. Therefore, the customer's credit risk is being considered by financial institutions, and a credit evaluating model has emerged as a result. Currently, research has concentrated on enhancing the precision of the model, ignoring the interpretability, which makes it difficult to apply in the industry. Compared to precision, studies related to the interpretable model are limited. In our previous work, we did not consider model operation time and stability. Therefore, this study proposes a hybrid model based on the RIPPER algorithm. First, according to the characteristics of credit card data sets, targeted special data pretreatment methods are proposed. Next, the RELIEF method for feature selection removes the redundant features and further improves the interpretability of the model. Then, to address the problem of the imbalanced distribution of credit card data sets, a synthetic minority class sampling algorithm is used to equalize the samples. Finally, default credit card users are predicted by taking advantage of the rules generated by the RIPPER algorithm. To test the performance of the model, we used Taiwanese credit card customer data for empirical research. We considered model accuracy and interpretability when comparing the proposed SPR‐RIPPER model with the existing mainstream models. The results of the experiments indicate that the proposed model achieves acceptable results. This study demonstrates that the proposed credit card user default prediction model, SPR‐RIPPER, has practical application value.