Hasil untuk "Regional planning"

Kan Li, Chongyu Zhang, Wei Zhang et al.

To address the challenge of rapidly selecting optimal numerical model products for weather forecasting in critical applications such as aviation route planning, this study proposes an enhanced object-based methodology comprising individual object scoring matching and a regional overall forecast selection scheme, building upon previous research. The method focuses on radar reflectivity forecasts within critical areas along air routes. Individual thunderstorm cells are evaluated using weighted scores for multiple parameters, including the Threat Score (TS), center-of-mass position, maximum radar reflectivity intensity, and shape forecasting accuracy. The regional overall score is then calculated by applying different weights to each convective cell within the area. After examining case studies of various convection types and bulk tests from June to September of 2024 and 2025, the results demonstrate that this method effectively selects the optimal convective forecasts from among the numerical models initiated at different times. The methodology shows promising applications in aviation weather forecasting. Different optimal selection schemes yield varying results: for large-scale convective weather, various test schemes generally align with TS score selection; for small-scale convective weather, schemes emphasizing radar reflectivity intensity show better performance; for scattered convection, schemes prioritizing center-of-mass position forecasting demonstrate superior results. These findings provide valuable insights for precision weather forecasting in both aviation and the agricultural–ecological sectors, in which accurate convective weather prediction is crucial for operational safety and resource management.

Laban Kayitete, Elie Sinayitutse, Matthew Dennis

The Hooded Vulture (Necrosyrtes monachus) plays a vital role in environmental cleaning and disease control. However, its population is rapidly declining across its range, especially in East Africa. Despite conservation efforts invested in its protection, its spatial distribution in East Africa remains understudied. Utilising ensemble Species Distribution Models, this study leverages the response of N. monachus to bioclimatic factors, elevation, and land cover to predict the species' current distribution across Kenya, Rwanda, Tanzania, and Uganda, and assess potential climate change impacts. Findings reveal that only 11.813 % of the study area represents suitable habitat for N. monachus, with 35.954 % of this falling within protected areas. The Hooded Vulture exhibited strong dependence on climatic conditions, with variables of large influence to its distribution being isothermality, annual mean temperature, precipitation seasonality, elevation, and annual precipitation, while the urban land exhibited moderate influence. Climate change projections indicate regional habitat stability, but varying spatial and climatic pathway-based trajectories, with habitat expansions under sustainable development pathways (SSP126) and mixed outcomes under fossil-fuelled scenarios (SSP585), particularly affecting Kenya with consistent declines, while Tanzania, Uganda, and Rwanda showed expansions. The proportion of suitable habitat within protected areas remained stable across scenarios, though substantial national disparities persist. This research underscores the role of modelling in informed conservation and urgency in transboundary conservation strategies extending beyond currently protected areas and provides critical insights for adaptive conservation planning to safeguard the Hooded Vulture's future in East Africa.

Zhichen Dong, Zhanhui Zhou, Zhixuan Liu et al.

In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations encode future outputs beyond the next token}$. Through simple probing, we demonstrate that LLM prompt representations encode global attributes of their entire responses, including $\textit{structure attributes}$ (e.g., response length, reasoning steps), $\textit{content attributes}$ (e.g., character choices in storywriting, multiple-choice answers at the end of response), and $\textit{behavior attributes}$ (e.g., answer confidence, factual consistency). In addition to identifying response planning, we explore how it scales with model size across tasks and how it evolves during generation. The findings that LLMs plan ahead for the future in their hidden representations suggest potential applications for improving transparency and generation control.

André Schakkal, Ben Zandonati, Zhutian Yang et al.

Enabling humanoid robots to reliably execute complex multi-step manipulation tasks is crucial for their effective deployment in industrial and household environments. This paper presents a hierarchical planning and control framework designed to achieve reliable multi-step humanoid manipulation. The proposed system comprises three layers: (1) a low-level RL-based controller responsible for tracking whole-body motion targets; (2) a mid-level set of skill policies trained via imitation learning that produce motion targets for different steps of a task; and (3) a high-level vision-language planning module that determines which skills should be executed and also monitors their completion in real-time using pretrained vision-language models (VLMs). Experimental validation is performed on a Unitree G1 humanoid robot executing a non-prehensile pick-and-place task. Over 40 real-world trials, the hierarchical system achieved a 73% success rate in completing the full manipulation sequence. These experiments confirm the feasibility of the proposed hierarchical system, highlighting the benefits of VLM-based skill planning and monitoring for multi-step manipulation scenarios. See https://vlp-humanoid.github.io/ for video demonstrations of the policy rollout.

Weiying KONG, Yizhuo LIU, Sichun DONG et al.

ObjectiveIn the contemporary global context, urban areas are increasingly confronted with the dual pressures of global climate change and rapid urbanization. These pressures have led to a significant rise in urban temperature, thereby amplifying the importance of blue-green spaces in mitigating the urban heat island (UHI) effect. Blue-green spaces, which include natural water bodies, parks, green corridors, and other vegetated areas, play a crucial role in regulating urban microclimates. As cities enter an era of stock development, where the focus shifts from expansion to optimization of existing resources, the strategic configuration of these spaces has become a cornerstone for enhancing urban thermal environments. Understanding the cooling mechanisms of blue-green spaces at various spatial scales is essential for improving urban thermal comfort and guiding the planning and construction of urban blue-green infrastructure.MethodsThis research focuses on the central urban area of Xi’an, a city that has experienced substantial urban growth over the past decade. By employing a combination of spatial autocorrelation analysis and a multi-scale geographically weighted regression (MGWR) model, the research examines the change characteristics of blue-green spaces and their impact on land surface temperature from 2013 to 2023. The findings reveal the spatial heterogeneity of cooling effects and offer tailored optimization strategies for blue-green spaces across diverse urban contexts. The research methodology involves selecting six representative landscape indices to evaluate the changes in blue-green space patterns in the central urban area of Xi’an. These indices are carefully chosen to capture the nuances of spatial configuration, fragmentation, and connectivity of blue-green spaces. Spatial autocorrelation analysis is utilized to identify spatial clustering and patterns extracted from the data collected, while the MGWR model is adopted for a more granular examination of the relationship between landscape indices and land surface temperature levels. This integrated approach not only reveals the factors influencing the cooling effects of blue-green spaces but also highlights their spatial variability across the urban landscape.ResultsThe results of the research are both revealing and instructive. 1) The blue-green space patterns in the central urban area of Xi’an underwent significant changes over the research period, reflecting the dynamic interplay between urban development and environmental management. 2) The spatial distribution of land surface temperature exhibits a distinct pattern of being “high in the north and low in the south”. The central area, characterized by dense urban fabric, shows minimal fluctuations in land surface temperature, whereas low-temperature zones are predominantly concentrated in the southern part of Baqiao District. This uneven thermal distribution underscores the complexity of urban heat dynamics and the need for targeted interventions. 3) The relationship between landscape indices and land surface temperature changes displays notable spatial heterogeneity. In high-density urban areas, small and complex blue-green patches demonstrate stronger cooling effects, emphasizing the importance of intricate designs in densely built environments where space is limited but the need for effective cooling is significant. In contrast, suburban areas benefit from avoiding the aggregation of large blue-green patches, which may otherwise hinder effective cooling due to reduced air circulation and increased shading. Near large water bodies, regularly shaped and highly connected blue-green patches are found to be particularly effective in reducing land surface temperature, highlighting the synergistic effects of water and vegetation in enhancing cooling performance and suggesting that integrated blue-green networks can maximize thermal benefits.ConclusionThe research concludes that the relationship between temperature changes and blue-green space changes in the central urban area of Xi’an is significant and characterized by strong spatial heterogeneity during the period from 2013 to 2023, with the cooling effects of blue-green spaces found varying by their spatial attributes and the characteristics of the surrounding urban environment. These findings highlight the necessity for region-specific optimization strategies to maximize the cooling potential of blue-green spaces. By integrating spatial analysis and regression modeling, the research provides a detailed understanding of the cooling mechanisms of blue-green spaces across diverse urban contexts. The results emphasize the importance of tailoring blue-green space designs to local conditions, considering factors such as urban density, proximity to water bodies, and regional climatic characteristics. This approach enhances the effectiveness of blue-green spaces in mitigating the urban heat island effect and contributes to the creation of more sustainable and thermally comfortable urban environments. The research advocates a holistic and adaptive urban planning strategy, where blue-green spaces are strategically designed and managed to address the unique thermal challenges of different urban areas. This research offers valuable guidance for policymakers and urban planners aiming to optimize blue-green infrastructure and improve urban resilience in the face of climate change and urbanization.

Oleksandr Karasov, Evelyn Uuemaa, Olle Järv et al.

In the context of urbanisation and growing disparities, timely and detailed spatial data on income inequality in cities is essential. We combined satellite imagery with streetlevel photographs provided by Google Street View to reveal the spatial distribution of household income. For this, we suggest a harmonised framework for median household income modelling based on deconstructing landscape patterns using a machine-learning approach, applied across three ’global cities’: Amsterdam, New York, and Sydney. First, we classified Sentinel-1 and Sentinel-2 mosaics and Google Street View scenes to detect functional elements of the built environment. Second, we calculated spatial indices for Sentinel imagery and visual indices for Google Street View scenes to characterise the urban landscape. Third, by combining various indicators, we trained city-specific income prediction models according to ground truth census data. The correlation between actual and predicted income in New York and Sydney reached 0.76 and 0.78, respectively. The accuracy of income prediction in Amsterdam reached 51.13%. We revealed relationships between spatial indicators of landscape patterns and spatial income distribution and recommend using Sentinel-1 and Sentinel-2 imagery as the primary data choice for income modelling in datarestricted regions. Google Street View data can be used complementarily when available.

R. F. Gataullin, E. R. Chuvashaeva

The purpose of the study is to reveal the existing trends, contradictions, and patterns in functioning and development of interregional and inter-municipal cooperation institutions. Contradictions in the encountered institutions functioning have been highlighted in their powers and performance. Among the trends in the institutions development the following have been noted: management decentralization, strengthening of the regional and municipal authorities role in planning and implementation of spatial development projects, vector for strengthening sustainability, sustainable development principles integration in strategies and projects that allows to consider not only economic and environmental aspects in planning, but also social ones, strengthening of innovativeness and financing of interregional and inter-municipal cooperation institutions, spatial development management digitalization, public participation in solving problems of improving interregional and inter-municipal cooperation, and strengthening of international cooperation in relevant projects implementation. Among the patterns in the institutions development it is necessary to define their focus on functioning in the context of horizontal links globalization, growth of development sustainability and population mobility, digitalization and innovativeness, ensuring security on the basis of risk management, which implies legal integration and standards harmonization, intensive exchange of the best projects, activation of local authorities and population in projects implementation. The author’s proposals for improving institutions in modern conditions include transition to the project approach, reorientation of the policy in this area to ensure mutual benefit, and priority in supporting projects of inter-territorial importance, capable of ensuring territorial connectivity and unity of economic space.

Jingwei Song, Jianhui Cong, Yuqing Liu et al.

In the context of sustainable development, water resources, energy, and carbon emissions are pivotal factors influencing the rational planning of economic development and the secure establishment of ecological barriers. As a core food production area, how can the Great River Basin balance the pressure on the “water–energy–carbon” system (WEC) to realize the coordinated development of “nature–society–economy”? Taking the Yellow River Basin in China as the research object, this paper explores the coupling characteristics and virtual transfer trends of WEC in the agricultural sector under the condition of mutual constraints. The results show the following: (1) On the dynamic coupling characteristics, W-E and E-C are strongly coupled with each other. The optimization of water resource allocation and the development of energy-saving water use technology make the W-E consumption show a downward trend, and the large-scale promotion of agricultural mechanization makes the E-C consumption show an upward trend. (2) On the spatial distribution of transfer, there is an obvious path dependence of virtual WEC transfer, showing a trend of transfer from less developed regions to developed regions, and the coupling strength decreases from developed regions to less developed regions. The assumption of producer responsibility serves to exacerbate the problem of inter-regional development imbalances. (3) According to the cross-sectoral analysis, water resources are in the center of sectoral interaction, and controlling the upstream sector of the resource supply will indirectly affect the synergistic relationship of WEC, and controlling the downstream sector of resource consumption will indirectly affect the constraint relationship of WEC. This study provides theoretical and methodological references for the Great River Basin to cope with the resource and environmental pressure brought by global climate change and the effective allocation of inter-regional resources.

Anthony Chen, Jianjin Xu, Wenzhao Zheng et al.

Diffusion models have demonstrated excellent capabilities in text-to-image generation. Their semantic understanding (i.e., prompt following) ability has also been greatly improved with large language models (e.g., T5, Llama). However, existing models cannot perfectly handle long and complex text prompts, especially when the text prompts contain various objects with numerous attributes and interrelated spatial relationships. While many regional prompting methods have been proposed for UNet-based models (SD1.5, SDXL), but there are still no implementations based on the recent Diffusion Transformer (DiT) architecture, such as SD3 and FLUX.1.In this report, we propose and implement regional prompting for FLUX.1 based on attention manipulation, which enables DiT with fined-grained compositional text-to-image generation capability in a training-free manner. Code is available at https://github.com/antonioo-c/Regional-Prompting-FLUX.

Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan et al.

This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives, such as Double DQN, even though the former uses significantly more memory and computation. The fundamental problem is that learned models can be inaccurate and often generate invalid states, especially when iterated many steps. In this paper, we avoid this limitation by constraining background planning to a set of (abstract) subgoals and learning only local, subgoal-conditioned models. This goal-space planning (GSP) approach is more computationally efficient, naturally incorporates temporal abstraction for faster long-horizon planning and avoids learning the transition dynamics entirely. We show that our GSP algorithm can propagate value from an abstract space in a manner that helps a variety of base learners learn significantly faster in different domains.

Sukai Huang, Trevor Cohn, Nir Lipovetzky

The capability of Large Language Models (LLMs) to plan remains a topic of debate. Some critics argue that strategies to boost LLMs' reasoning skills are ineffective in planning tasks, while others report strong outcomes merely from training models on a planning corpus. This study reassesses recent strategies by developing an end-to-end LLM planner and employing diverse metrics for a thorough evaluation. We find that merely fine-tuning LLMs on a corpus of planning instances does not lead to robust planning skills, as indicated by poor performance on out-of-distribution test sets. At the same time, we find that various strategies, including Chain-of-Thought, do enhance the probability of a plan being executable. This indicates progress towards better plan quality, despite not directly enhancing the final validity rate. Among the strategies we evaluated, reinforcement learning with our novel `Longest Contiguous Common Subsequence' reward emerged as the most effective, contributing to both plan validity and executability. Overall, our research addresses key misconceptions in the LLM-planning literature; we validate incremental progress in plan executability, although plan validity remains a challenge. Hence, future strategies should focus on both these aspects, drawing insights from our findings.

Karl Gummesson, Karin Forsell, Stefan Johansson et al.

The aim of this study was to explore how people with impairments perceived the accessibility of information regarding the COVID-19 pandemic in Sweden and what improvements they suggest to ensure accessibility of information in future societal crises. The study had a descriptive design, involving interviews and focus group discussions with people with impairments and their representative organisations, alongside analysis of public crisis information websites. The results showed that while many people with impairments could use their usual information channels, other found that the adapted information they needed was missing and that the government agencies, regional healthcare organisations and local municipalities were unprepared to produce accessible information. In conclusion, society exhibited shortcomings in providing accessible information to people with impairments during the COVID-19 pandemic. The responsible authorities seemed unprepared to provide accessible information. Proactive planning and training are imperative to ensure the provision of accessible information in future crises.

Valentin N. Hartmann, Marc Toussaint

We propose an approach to find low-makespan solutions to multi-robot multi-task planning problems in environments where robots block each other from completing tasks simultaneously. We introduce a formulation of the problem that allows for an approach based on greedy descent with random restarts for generation of the task assignment and task sequence. We then use a multi-agent path planner to evaluate the makespan of a given assignment and sequence. The planner decomposes the problem into multiple simple subproblems that only contain a single robots and a single task, and can thus be solved quickly to produce a solution for a fixed task sequence. The solutions to the subproblems are then combined to form a valid solution to the original problem. We showcase the approach on robotic stippling and robotic bin picking with up to 4 robot arms. The makespan of the solutions found by our algorithm are up to 30% lower compared to a greedy approach.

Lei Zhang, Guodong Guan, Zilong Yang et al.

The flexibility modification of Combined Heat And Power (CHP) units can enable the units to have fast electric output regulation capability, respond to grid auxiliary frequency regulation, and enhance the operational flexibility of regional electric and thermal coupling systems. Compared with the CHP unit constraint model established by the vertex convex combination method, the CHP dynamic constraint in the form of differential equations established by the mechanism modeling can reflect the unit variable states in real time and facilitate the mastering of the unit operation status. With the objective of optimal dispatch cost of the regional electro-thermal coupled system considering the carbon trading process, a low-carbon economic dispatch model of the regional electro-thermal coupled system considering the CHP dynamic constraint is established. Based on the sequential method framework, the finite element orthogonal configuration method is used to discrete the differential algebraic equations of the simulation layer, and the improved adaptive differential evolution algorithm is used to solve the nonlinear planning problem of the optimization layer. The algorithm verifies that the finite element orthogonal configuration method can obtain more accurate results with fewer discrete points, improve the solution speed, and verify that the CHP unit with fast electric output regulation can effectively improve the low-carbon operation of the system, which is also beneficial to the safe and stable operation of the unit.

Abel Centella-Artola, Arnoldo Bezanilla-Morlot, Roberto Serrano-Notivoli et al.

The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1. The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources. The original station data series were quality controlled using a spatial coherence process of the data, and the missing values were estimated on each day and location independently. Using the filled data series, a grid of 3 × 3 km spatial resolution was constructed by estimating daily precipitation and their corresponding uncertainties at each grid box. This new product represents a precise spatiotemporal distribution of precipitation in Cuba and provides a useful baseline for future studies in hydrology, climatology, and meteorology. The data collection described is available on zenodo: https://doi.org/10.5281/zenodo.7847844

Rohit Menon, Tobias Zaenker, Nils Dengler et al.

Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time. State-of-the-art viewpoint planning approaches utilize computationally expensive ray casting operations to find good viewpoints aiming at maximizing information gain and covering the fruits in the scene. In this paper, we present a novel viewpoint planning approach that explicitly uses information about the predicted fruit shapes to compute targeted viewpoints that observe as yet unobserved parts of the fruits. Furthermore, we formulate the concept of viewpoint dissimilarity to reduce the sampling space for more efficient selection of useful, dissimilar viewpoints. Our simulation experiments with a UR5e arm equipped with an RGB-D sensor provide a quantitative demonstration of the efficacy of our iterative next best view planning method based on shape completion. In comparative experiments with a state-of-the-art viewpoint planner, we demonstrate improvement not only in the estimation of the fruit sizes, but also in their reconstruction, while significantly reducing the planning time. Finally, we show the viability of our approach for mapping sweet peppers plants with a real robotic system in a commercial glasshouse.

N. Cavrel, D. Pellier, H. Fiorino

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems in terms of decompositions of tasks into subtaks. Many techniques have been proposed to solve such hierarchical planning problems. A particular technique is to encode hierarchical planning problems as classical STRIPS planning problems. One advantage of this technique is to benefit directly from the constant improvements made by STRIPS planners. However, there are still few effective and expressive encodings. In this paper, we present a new HTN to STRIPS encoding allowing to generate concurrent plans. We show experimentally that this encoding outperforms previous approaches on hierarchical IPC benchmarks.

Michael Pantic, Cesar Cadena, Roland Siegwart et al.

This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning. We show that by adding the capacity to infer occupancy in a radius to a pre-trained NeRF, we are effectively learning an approximation to a Euclidean Signed Distance Field (ESDF). Using backward differentiation of the augmented network, we obtain an obstacle gradient that is integrated into an obstacle avoidance policy based on the Riemannian Motion Policies (RMP) framework. Thus, our findings allow for very fast sampling-free obstacle avoidance planning in the implicit representation.

Rui Wang, Kai Gao, Jingjin Yu et al.

Object rearrangement is important for many applications but remains challenging, especially in confined spaces, such as shelves, where objects cannot be accessed from above and they block reachability to each other. Such constraints require many motion planning and collision checking calls, which are computationally expensive. In addition, the arrangement space grows exponentially with the number of objects. To address these issues, this work introduces a lazy evaluation framework with a local monotone solver and a global planner. Monotone instances are those that can be solved by moving each object at most once. A key insight is that reachability constraints at the grasps for objects' starts and goals can quickly reveal dependencies between objects without having to execute expensive motion planning queries. Given that, the local solver builds lazily a search tree that respects these reachability constraints without verifying that the arm paths are collision free. It only collision checks when a promising solution is found. If a monotone solution is not found, the non-monotone planner loads the lazy search tree and explores ways to move objects to intermediate locations from where monotone solutions to the goal can be found. Results show that the proposed framework can solve difficult instances in confined spaces with up to 16 objects, which state-of-the-art methods fail to solve. It also solves problems faster than alternatives, when the alternatives find a solution. It also achieves high-quality solutions, i.e., only 1.8 additional actions on average are needed for non-monotone instances.

Linfeng Zhao, Xupeng Zhu, Lingzhi Kong et al.

We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms when symmetry appears in decision-making tasks. Motivated by equivariant convolution networks, we treat the path planning problem as \textit{signals} over grids. We show that value iteration in this case is a linear equivariant operator, which is a (steerable) convolution. This extends Value Iteration Networks (VINs) on using convolutional networks for path planning with additional rotation and reflection symmetry. Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry. The experiments are performed on four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs) configuration space and workspace manipulation. Our symmetric planning algorithms improve training efficiency and generalization by large margins compared to non-equivariant counterparts, VIN and GPPN.