Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
The use of rubber-tapping robots capable of autonomous navigation in place of manual rubber-tapping is a growing trend, but the challenging multi-objective navigation task in forest environments impedes their autonomous operation. To tackle this issue, an autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation is designed. This navigation decision mechanism is comprised of obtaining coordinates of target points (OCTP), selecting the next coordinate (SNC), generating the additional coordinates (GAC), and optimizing the planned paths (OPP). By utilizing this mechanism, the robot can autonomously select the next target point based on its current position and the actual operating logic while navigating in the forest areas, adding additional coordinates during row or column changes, and planning and optimizing the path. The on-site experiments demonstrate that during autonomous navigation, the positioning accuracy is favorable and supports subsequent operations. The overall rationality of the planned path reaches 92.14%, further confirming its effectiveness....
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent’s decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation. To bridge this gap, we propose a novel task called Dynamic Vision-and-Language Navigation (DynamicVLN), incorporating various dynamic scenarios to enhance the agent’s decisionmaking abilities and adaptability. By redefining the VLN task, we emphasize that a robust and generalizable agent should not rely solely on predefined instructions but must also demonstrate reasoning skills and adaptability to unforeseen events. Specifically, we have designed ten scenarios that simulate the challenges of dynamic navigation and developed a dedicated dataset of 11,261 instances using the CARLA simulator (ver.0.9.13) and large language model to provide realistic training conditions. Additionally, we introduce a baseline model that integrates advanced perception and decision-making modules, enabling effective navigation and interpretation of the complexities of dynamic road conditions. This model showcases the ability to follow natural language instructions while dynamically adapting to environmental cues. Our approach establishes a benchmark for developing agents capable of functioning in real-world, dynamic environments and extending beyond the limitations of static VLN tasks to more practical and versatile applications....
The progress in artificial intelligence (AI) technology has greatly changed various facets of society. This study aimed to explore aspects that need to be considered in developing AI curriculum for senior high schools in Indonesia. The qualitative approach employed in this study. The researchers utilized focus group discussions with schools’ management and students at seven cities and group interviews with students at three cities. The results show that some schools want AI as an extracurricular activity, while others want it as a mandatory subject. School management and teachers aim for 2-3 competent AI instructors in each school. If no teachers are available, training will be provided to ICT, mathematics, or physics teachers for about a year to become AI educators. All participants agree on the importance of teaching students about AI applications and discussing ethical issues related to AI....
Global efforts to reduce emissions are hampered by incomplete and unreliable data from coal power plants regarding their release of greenhouse gases. This inability to acquire data, particularly relevant in regions lacking robust reporting mechanisms, hinders evidence-based policy-making and proper action against prominent emissions contributors. To address this issue, we propose using a transformer-based approach to determine the coal plant’s operational status – entirely through satellite images. This method leverages the readily available global coverage of satellite imagery to provide independent, real-time monitoring of coal plant operations, even in areas where traditional data collection methods are challenging. The model learns to identify visual indicators, such as cooling tower plumes in unseen test images. Our transformer-based approach outperforms the best prior work with statistical significance, reaching an 80% accuracy on a held-out set of unseen coal plant satellite images. These results have far-reaching implications for climate monitoring and regulations. By providing more accurate and independently sourced information on coal plant operations worldwide, our approach can support more effective emissions tracking, inform climate negotiations, and introduce greater transparency into the process of climate regulation....
In recent years, significant advancements have been made in robotics, yet challenges remain in navigation, particularly for autonomous vehicles and bipedal robots. This paper provides a comprehensive review of three critical components in robotic navigation: YOLO neural networks, depth cameras, and A* path planning. Existing studies often address these aspects independently, lacking an integrated approach. Here, we systematically summarize and analyze the effectiveness of these techniques in navigation tasks. Through a literature review of recent publications, we compare and categorize various methods to assess trends in YOLO research and explore potential for integrated research in navigation. Furthermore, we analyze the strengths and limitations of each method in dynamic environments. The findings suggest that while YOLO and depth camera-based systems excel in real-time object detection and spatial awareness, they face challenges related to light sensitivity and high computational demands. Future research directions are proposed to enhance adaptability in complex environments, improve efficiency, and support costeffective navigation solutions in robotics....
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