1. Planning, Planners and Plans 2. The Origins: The Urban Growth, From 1800 to 1940 3. The Seers: Pioneer Thinkers in Urban Planning, From 1880 to 1945 4. The Creation of the Postwar Planning Machine, From 1940 to 1952 5. National/Regional Planning, From 1945 to 2010 6. Planning for Cities and City Regions, From 1945 to 2010 7. Planning in Western Europe Since 1945 8. Planning in the United States Since 1945 9. The Planning Process
Reinforcement Learning (RL) has shown remarkable success in real-world applications, particularly in robotics control. However, RL adoption remains limited due to insufficient safety guarantees. We introduce Nightmare Dreamer, a model-based Safe RL algorithm that addresses safety concerns by leveraging a learned world model to predict potential safety violations and plan actions accordingly. Nightmare Dreamer achieves nearly zero safety violations while maximizing rewards. Nightmare Dreamer outperforms model-free baselines on Safety Gymnasium tasks using only image observations, achieving nearly a 20x improvement in efficiency.
Giuseppe Silano, Daniel Bonilla Licea, Hajar El Hammouti
et al.
A new class of Multi-Rotor Aerial Vehicles (MRAVs), known as omnidirectional MRAVs (o-MRAVs), has gained attention for their ability to independently control 3D position and orientation. This capability enhances robust planning and control in aerial communication networks, enabling more adaptive trajectory planning and precise antenna alignment without additional mechanical components. These features are particularly valuable in uncertain environments, where disturbances such as wind and interference affect communication stability. This paper examines o-MRAVs in the context of robust aerial network planning, comparing them with the more common under-actuated MRAVs (u-MRAVs). Key applications, including physical layer security, optical communications, and network densification, are highlighted, demonstrating the potential of o-MRAVs to improve reliability and efficiency in dynamic communication scenarios.
We present a framework for uncovering and exploiting dependencies among tools and documents to enhance exemplar artifact generation. Our method begins by constructing a tool knowledge graph from tool schemas,including descriptions, arguments, and output payloads, using a DeepResearch-inspired analysis. In parallel, we derive a complementary knowledge graph from internal documents and SOPs, which is then fused with the tool graph. To generate exemplar plans, we adopt a deep-sparse integration strategy that aligns structural tool dependencies with procedural knowledge. Experiments demonstrate that this unified framework effectively models tool interactions and improves plan generation, underscoring the benefits of linking tool graphs with domain knowledge graphs for tool-augmented reasoning and planning.
Daniel M. Cherenson, Devansh R. Agrawal, Dimitra Panagou
Mission planning can often be formulated as a constrained control problem under multiple path constraints (i.e., safety constraints) and budget constraints (i.e., resource expenditure constraints). In a priori unknown environments, verifying that an offline solution will satisfy the constraints for all time can be difficult, if not impossible. We present ReRoot, a novel sampling-based framework that enforces safety and budget constraints for nonlinear systems in unknown environments. The main idea is that ReRoot grows multiple reverse RRT* trees online, starting from renewal sets, i.e., sets where the budget constraints are renewed. The dynamically feasible backup trajectories guarantee safety and reduce resource expenditure, which provides a principled backup policy when integrated into the gatekeeper safety verification architecture. We demonstrate our approach in simulation with a fixed-wing UAV in a GNSS-denied environment with a budget constraint on localization error that can be renewed at visual landmarks.
Michael Wehrli, Alicia Durrer, Paul Friedrich
et al.
To treat Trochlear Dysplasia (TD), current approaches rely mainly on low-resolution clinical Magnetic Resonance (MR) scans and surgical intuition. The surgeries are planned based on surgeons experience, have limited adoption of minimally invasive techniques, and lead to inconsistent outcomes. We propose a pipeline that generates super-resolved, patient-specific 3D pseudo-healthy target morphologies from conventional clinical MR scans. First, we compute an isotropic super-resolved MR volume using an Implicit Neural Representation (INR). Next, we segment femur, tibia, patella, and fibula with a multi-label custom-trained network. Finally, we train a Wavelet Diffusion Model (WDM) to generate pseudo-healthy target morphologies of the trochlear region. In contrast to prior work producing pseudo-healthy low-resolution 3D MR images, our approach enables the generation of sub-millimeter resolved 3D shapes compatible for pre- and intraoperative use. These can serve as preoperative blueprints for reshaping the femoral groove while preserving the native patella articulation. Furthermore, and in contrast to other work, we do not require a CT for our pipeline - reducing the amount of radiation. We evaluated our approach on 25 TD patients and could show that our target morphologies significantly improve the sulcus angle (SA) and trochlear groove depth (TGD). The code and interactive visualization are available at https://wehrlimi.github.io/sr-3d-planning/.
Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where intricate instructions meet cluttered or ambiguous scenes. We introduce RePlan (Region-aligned Planning), a plan-then-execute framework that couples a vision-language planner with a diffusion editor. The planner decomposes instructions via step-by-step reasoning and explicitly grounds them to target regions; the editor then applies changes using a training-free attention-region injection mechanism, enabling precise, parallel multi-region edits without iterative inpainting. To strengthen planning, we apply GRPO-based reinforcement learning using 1K instruction-only examples, yielding substantial gains in reasoning fidelity and format reliability. We further present IV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outperforms strong baselines trained on far larger datasets, improving regional precision and overall fidelity. Our project page: https://replan-iv-edit.github.io
Mulyandari Erni, Hadiani Rr. Rintis, Sobriyah
et al.
This research focuses on the verification of rainfall estimation data from satellites CHIRPS and TRMM in relation to mapping flood-prone areas in Surakarta City, Indonesia. Validation was conducted by evaluating satellite data with rainfall records from monitoring stations and calculating their Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC). The findings suggest that CHIRPS exhibited more accurate results than TRMM, as evidenced by the higher correlation values obtained and the lower error rates. Flood-prone areas were defined through spatial analysis making use of GIS, including all relevant criteria such as annual rainfall, altitude, land use, soil classification, and river distance. A weighted overlay analysis was performed whereby each region was classified according to susceptibility to flooding into one of the following grades of low, moderate, high, and very high. The flood vulnerability map was subsequently validated against historical flood event data whereby 97% of recorded flood events occurred within areas rated vulnerable by the model, thereby validating the credibility and robustness of the model. Information thus generated is pertinent for risk-sensitive urban planning policies and infrastructure development within the context of Surakarta City and for the optimization of disaster risk reduction actions.
Lokacije avtobusnih postaj so ključne za učinkovito storitev potniškega prometa in trajnostno mobilnost v mestih. Določajo možnost dostopa obiskovalcev do mesta ter varnost, dostopnost in ekonomičnost javnega prevoza. Poleg tega vplivajo na dostop prebivalcev do delovnih mest, šol, zdravstvenih in drugih osnovnih storitev ter posledično na splošno družbenogospodarsko blaginjo mesta. V članku je določena optimalna lokacija nove avtobusne postaje v turškem mestu Uşak, za kar so avtorji uporabili hibridni model večkriterijskega odločanja, ki vključuje presojo dvajsetih kriterijev in celovito analizo dostopnosti lokacij na podlagi mnenj strokovnjakov ter upošteva raznovrstne potrebe prebivalcev in lokalnih oblasti. S postopkom analitične hierarhije so merilom pripisali uteži, na podlagi česar so nato z metodami MOOSRA, ARAS in VIKOR sedem možnih lokacij razvrstili po primernosti. Izsledki so pokazali, katera lokacija bi bila najprimernejša, hkrati pa so potrdili robustnost uporabljenega modela in njegovo uporabnost v urbanističnem načrtovanju
With the recent influx in demand for multi-robot systems throughout industry and academia, there is an increasing need for faster, robust, and generalizable path planning algorithms. Similarly, given the inherent connection between control algorithms and multi-robot path planners, there is in turn an increased demand for fast, efficient, and robust controllers. We propose a scalable joint path planning and control algorithm for multi-robot systems with constrained behaviours based on factor graph optimization. We demonstrate our algorithm on a series of hardware and simulated experiments. Our algorithm is consistently able to recover from disturbances and avoid obstacles while outperforming state-of-the-art methods in optimization time, path deviation, and inter-robot errors. See the code and supplementary video for experiments.
The contemporary city is built around open spaces, which have always
contributed to the improvement of the image of the city and the living
environment of its inhabitants. These open spaces, also called public
spaces, remain neglected spaces in urban planning in Algeria. Our article
focuses on the assessment of public spaces based on a multi-criteria
analysis able to provide a comprehensive assessment of the quality of open
spaces. Using structured observations throughout the day over a period of 15
days following an observation grid, we calculated a Public Space Index
(PSI) across five dimensions, including inclusiveness, meaningful
activities, safety, comfort, and pleasurability. The Public Space Index
(PSI) is constructed from 45 variables to assess the five dimensions of
public space. Our choice focused on the new city (Ali Mendjeli) located at
Constantine, Algeria, a city that has been built recently (about thirty
years). In this city, we have neglected the issue of public spaces in its
planning. Recently, there has been a growing awareness of the need to
improve the image of the city and the living environment of its inhabitants.
So, the aim of this research is to assess the quality of its public spaces.
The results of our research showed that the design of its public spaces had
not taken into consideration many things in relation to the users of the
space. Despite this, the public space studied meets the quality criteria for
the five dimensions studied with a score of 69 out of 100.
In the fields of planning theory and human geography, there is a growing discussion of the just city. The impression is that in order to continue the discussion of the crucial issue of the just city, certain methodological considerations and precautions are necessary. The article is focused on three in particular: (a) (urban) institutions as the first subject of justice, (b) the incomplete overlap between social justice and distributive justice, (c) the distinction between the concept and the conceptions of social justice. The impression is that these three issues are not always recognised, or at least not always to the fullest extent, in the current debate in planning theory.
Vasileios Vasilopoulos, Sebastian Castro, William Vega-Brown
et al.
We describe a task and motion planning architecture for highly dynamic systems that combines a domain-independent sampling-based deliberative planning algorithm with a global reactive planner. We leverage the recent development of a reactive, vector field planner that provides guarantees of reachability to large regions of the environment even in the face of unknown or unforeseen obstacles. The reachability guarantees can be formalized using contracts that allow a deliberative planner to reason purely in terms of those contracts and synthesize a plan by choosing a sequence of reactive behaviors and their target configurations, without evaluating specific motion plans between targets. This reduces both the search depth at which plans will be found, and the number of samples required to ensure a plan exists, while crucially preserving correctness guarantees. The result is reduced computational cost of synthesizing plans, and increased robustness of generated plans to actuator noise, model misspecification, or unknown obstacles. Simulation studies show that our hierarchical planning and execution architecture can solve complex navigation and rearrangement tasks, even when faced with narrow passageways or incomplete world information.
In this short paper, we outline nine classical benchmarks submitted to the first hierarchical planning track of the International Planning competition in 2020. All of these benchmarks are based on the HDDL language. The choice of the benchmarks was based on a questionnaire sent to the HTN community. They are the following: Barman, Childsnack, Rover, Satellite, Blocksworld, Depots, Gripper, and Hiking. In the rest of the paper we give a short description of these benchmarks. All are totally ordered.
Considering an environment containing polygonal obstacles, we address the problem of planning motions for a pair of planar robots connected to one another via a cable of limited length. Much like prior problems with a single robot connected via a cable to a fixed base, straight line-of-sight visibility plays an important role. The present paper shows how the reduced visibility graph provides a natural discretization and captures the essential topological considerations very effectively for the two robot case as well. Unlike the single robot case, however, the bounded cable length introduces considerations around coordination (or equivalently, when viewed from the point of view of a centralized planner, relative timing) that complicates the matter. Indeed, the paper has to introduce a rather more involved formalization than prior single-robot work in order to establish the core theoretical result -- a theorem permitting the problem to be cast as one of finding paths rather than trajectories. Once affirmed, the planning problem reduces to a straightforward graph search with an elegant representation of the connecting cable, demanding only a few extra ancillary checks that ensure sufficiency of cable to guarantee feasibility of the solution. We describe our implementation of A${}^\star$ search, and report experimental results. Lastly, we prescribe an optimal execution for the solutions provided by the algorithm.
Background: Coronaviruses caused three pandemics and impact public health globally in the 21st century. However, limited data were for the evaluation of the trend of coronavirus researches. We aimed to analyze quantitatively, qualitatively, and visually evaluate global scientific publications on coronavirus by using bibliometric analysis.
Methods: Coronavirus-related research from 1990-2019 was retrieved from the Web of Science Core Collection database (WoS). Microsoft Excel and VOS viewer software were used to assess the characteristics of publications.
Results: Overall, 9,553 publications on coronavirus were retrieved on 12 Mar 2020. The United States took a leading position in coronavirus-related research and accounted for more than one-thirds (36.7%) of all publications. The most productive journal in this field was Journal of Virology (1,056, 11.1%), and the most productive institution was University of Hong Kong (394, 4.1%). The main hot topics in coronavirus field were virus infection and protein. Active collaborations between countries were observed.
Conclusion: Over the past three decades, coronavirus research has gradually increased due to two global outbreaks. Through this global bibliometric evaluation, some relevant evidence could be provided. Corresponding to the impact of novel coronavirus (COVID-19), a large number of articles can be expected to appear in the next few years, and international cooperation should be strengthened to solve the problem.
The unrestrained and constant inflow of tourists to some cities can provoke
the discomfort of residents. The term ‘overtourism’ describes a condition in
which the limits are exceeded to the point that regular everyday urban life
suffers. The quantity of visitors and resulting crowds create burdens on the
functioning of some city services, and they simultaneously result in a
deterioration in the quality of sightseeing. This paper gives a review of
the academic literature concerning this issue, especially studies
concentrating on cruising and alternative short-term renting. Major
complaints raised by locals on the negative impact of tourism are not only
about overcrowding, but also include the serious social and economic
questions of the endangerment of rights. In order to regulate and mitigate
conflicts, preserve identity and allow decent urban life in all its aspects,
city governments are forced to introduce some limitations and rules in the
sphere of tourism, and also with regard to the real-estate market and urban
planning. The level of measures and policies required depend on the type of
problem, size of the city and dispersion of the attractions.