Operations In Urban Environments Research Articles

Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights

<div>Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time, minimum rescue force, and fire loss parameters contribute to the enhancement of urban fire rescue operations and the refinement of practical decision support systems.</div>

Obstacle encounter probability dependent local path planner for UAV operation in urban environments

Unmanned aerial vehicles (UAVs) are well-suited for various short-distance missions in urban environments. However, the path planner of such UAV is constantly challenged with the choice between avoiding obstacles horizontally or vertically. If the path planner relies on sensor information only, i.e. the path planner is a local planner, usually predefined manoeuvres or preferences are used to find a possible way. However, this method is stiff and inflexible. This work proposes a probabilistic decision-maker to set the control parameters of a classic local path planner during a flight mission. The decision-maker defines whether performing horizontal or vertical avoidance is preferable based on the probability of encountering a given number of obstacles. Here, the decision-maker considers predictions of possible future avoidance manoeuvres. It also defines an ideal flight altitude based on the probability of encountering obstacles. This work analyses the building height of all European capital cities and the probability of encountering obstacles at different altitudes to feed the decision-maker. We tested the feasibility of the proposed decision-maker with the 3DVFH*, a commonly used local path planner, in multiple simulations. The proposed probabilistic decision-maker allows the local path planner to reach the goal point significantly more often than the standard version of the 3DVFH*.

Lower-body muscular power predicts performance on urban combat simulation.

Military operations in urban environments requires faster movements and therefore may place greater demands on soldier strength and anaerobic ability. The aim was to study how physical fitness and body composition are associated with occupational test for urban combat soldiers before and after a 5-day military field exercise (MFE). Twenty-six conscripts (age = 20±1 yrs.) volunteered, of which thirteen completed the study. Occupational performance was determined by using the newly developed Urban Combat Simulation test (UCS); which included 50-m sprinting, moving a truck tire (56 kg) 2 meters with a sledgehammer, a 12-m kettlebell carry (2×20 kg) up the stairs with a 3-m ascent, 4-time sandbag lifts (20 kg) with obstacle crossing, and a 20-m mannequin (85 kg) drag. Aerobic and muscle fitness, as well as anaerobic capacity were measured, and, body composition was assessed with multifrequency bioimpedance analysis. The UCS performance correlated significantly with standing long jump performance, as well as lower and upper body maximal strength before (r = -0.56 to -0.66) and after (r = -0.59 to -0.68) MFE, and, with body mass and FFM before (r = -0.81 to -0.83) and after (r = -0.86 to -0.91) MFE. In the regression analyses, fat free mass (R2 = 0.50, p = 0.01) and counter movement jump in combat load (R2 = 0.46, p = 0.009) most strongly explained the UCS performance. This study demonstrated that muscle mass and lower body explosive force production together with maximal strength are key fitness components related to typical urban combat soldiers' military tasks. Physical training developing these components are recommended.

Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture

Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.

Assessment of 5G Connectivity for UAV Operations in Urban Environments: An Analysis of Network Performance and UAV Communication Architecture

This article presents an in-depth analysis of the integration of 5G connectivity for unmanned aerial vehicle (UAV) operations in urban environments, focusing on network performance and the architectural framework necessary for optimal UAV communication. As UAVs become increasingly prevalent in urban applications, from surveillance to delivery services, the demand for robust and reliable communication systems has become paramount. This study explores the unique challenges posed by urban settings, such as signal obstruction and high-density network traffic, and examines how 5G technology offers solutions through advanced features like beamforming. By detailing the setup and evaluation of a UAV communication architecture that incorporates 5G modules, including the use of the NVIDIA Jetson AGX Orin Developer Kit for real-world testing, this article highlights the significant improvements in data rate, latency, and reliability achieved with 5G networks compared to traditional communication technologies. The findings underscore the transformative potential of 5G connectivity in enhancing UAV operational efficiency and reliability in complex urban landscapes. Through case studies and practical insights, the article also provides a glimpse into the future of urban UAV applications, paving the way for further research and development in this dynamic field.

Sense and Avoid Considerations for Safe sUAS Operations in Urban Environments

Sense and Avoid Considerations for Safe sUAS Operations in Urban Environments

Assessment of a wind-tunnel test-bed for drone aeroacoustics in simulated flight conditions

This study presents the motivation and preliminary assessment of a test bed for the aeroacoustic characterization of Scaled-Flight models and Unmanned Aerial Vehicles (UAVs). The acceptance of urban air mobility and drone operations in urban environments depends on the noise emitted by these vehicles. For that, it is fundamental to create means to assess these emissions reliably in a controlled environment. The highly transient nature of the operations of these vehicles in the urban environment, caused by gusts, incoming turbulence, and maneuvers, requires measurements of the vehicle's acoustic emissions during realistic flight conditions. In this study, the importance of assessing the acoustic emissions under transient conditions is discussed. Measurements of a multirotor vehicle under a transient operation are used to highlight the discrepancies between the quasi-steady assumption and the real operation on the noise emissions. The study is concluded with a discussion on the possible realization of a wind-tunnel test-bed facility at TU Delft that allows the assessment of vehicles in conditions of maneuvering and response to gusts.

A Drone Operation Plan model to support the effect of uncertainty in advanced U-Space Capacity Planning Process

Enabling high numbers of drone operations in urban environments will require an efficient and highly automated drone traffic management system that can help balance demand against novel capacity measures. The research project DACUS designed a new, social impact-and risk-based Dynamic Capacity Management (DCM) service. As a core element, Drone Operation Plans (DOP) as the central and reliable point-of-information must include all necessary information for the DCM service to evaluate and manage the demand and the associated uncertainty of those operations at various phases of the operations planning process.This paper introduces a model to define and share DOPs that are enriched with information about technical capabilities of the drone, variations in the operating environment and the operational characteristics of each particular mission which helps facilitating the drone traffic management in capacity-constrained scenarios. The model aims to capture varying degrees and sources of uncertainty regarding the planned operations. Additionally, the model supports the integration of resolution plans to cope with a wide range of off-nominal scenarios and contingency events.For evaluation, the proposed DOP model is implemented and used in experiments embedded in Sopra Steria’s UTM or U-space Collaborative Interface System (UCIS). A social impact model and a collision risk model are used in simulations with high traffic demand and the information of the DOPs is either updated or directly applied within the DCM processes. The influence of operational uncertainty is assessed in this work using the collision risk model, which evaluates the risk of drone operations to the public and identifies potential collision risk hotspots.The simulation results show that the DOP model is suitable for the requirements of several U-space services and functions.

Experimental investigations into the effect of urban airflow characteristics on urban air mobility applications

Unmanned Aircraft Systems (UAS) operations in urban environment are significantly challenged by the complex airflow structures and high levels of turbulence owing to the densely built-up regions and the presence of tall buildings. This paper investigates the urban airflow characteristics that are expected to pose significant challenges to the flight of unmanned aircraft systems and impair their aerodynamic stability. Using experimental methods, a 1:400 scale city model was tested in an Atmospheric Boundary Layer (ABL) wind tunnel at various wind incidence angles to capture a representative range of wind speed changes, wind direction changes, wind shear and turbulence intensity flow features. The results can be used as a first step in informing regulations for UAS operations in urban flows, where the most severe flow conditions are those which contribute to develop operational limits. This paper reveals the severity of the flow conditions in representative urban environments, particularly around and between the buildings.

Atmospheric flow simulation strategies to assess turbulent wind conditions for safe drone operations in urban environments

As the technology of small aircraft systems continues to improve and the number of their possible applications grows, they are likely to be used more frequently in urban environments soon. Such environments are especially dangerous due to their high population and structural density in combination with challenging atmospheric conditions. Particularly the local wind field with its often unknown wind shear and turbulence characteristics endangers manned and unmanned aerial vehicles. Therefore, knowledge of the local turbulent wind is essential to ensure safety for the aircraft and the people onboard as well as on ground and should thus be incorporated in mission planning. This study presents a framework of how atmospheric flow analyses can contribute to safe drone operations in urban environments. High-resolution simulations are carried out, utilizing the large-eddy simulation model PALM, which can resolve turbulent flow and building structures down to the meter scale. Our results highlight the advantages and the necessity of using turbulence-resolving models to reasonably arrange a future drone operation network within cities. Because large-eddy simulations of urban environments are still computationally expensive, a meteorological data base for each urban setup should be established to obtain the relevant wind information for mission planning.

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of ‘AirMatrix’, the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multi-path planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators’ requests are both considered. A ‘cross-off’ strategy is developed to calculate the fitness value, and a ‘distributed-centralized’ strategy is applied considering the task priorities of drones. A genetic algorithm GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.

Small Unmanned Aerial Vehicle Flight Planning in Urban Environments

This work proposes a fast algorithm for generating obstacle-free and wind-efficient flight paths at a constant above-ground-level altitude in urban environments because a fast flight path planning algorithm is an essential function or service needed for enabling small unmanned aerial vehicle (sUAV) to operate in urban environments within Class G airspace. The proposed method first converts the 3D path planning problem to a 2D problem by constructing an obstacle map at a given above-ground-level altitude. A quad-tree decomposition is then used to build a search space in terms of obstacle occupancy and wind difference. The wind cost of traveling through each cell is defined based on energy consumption under various wind conditions. A repulsive potential is also adopted to make sure that the flight plans stay away from obstacles. The Theta* search algorithm, a variant of A* algorithm, is applied to mitigate the path angle change constraints introduced by grid-based graphs. With the Theta* and postsmoothing techniques, an obstacle-free, wind efficient, and constant above-ground-level flight plan can be quickly generated for sUAV operations in urban environments while meeting the lateral path angle constraints. The results showed that the path planning algorithm is efficient and can be finished within several seconds. With a proper choice of wind coefficient, the proposed path planning algorithm outperforms the multiple-shooting trajectory optimization method even in an obstacle-free environment. With the flexibility of incorporating other geo-related costs and computation efficiency, the proposed algorithm shows the potential for real-time flight path planning in complex urban environments.

Potentialities of drones and ground autonomous delivery devices for last-mile logistics

The e-commerce boom has increased the complexity of last-mile logistics operations in urban environments. In this context, unmanned aerial vehicles (UAVs), also known as delivery drones, and ground autonomous delivery devices (GADDs) show great potentialities. The objective of this paper is to provide strategic insights to adequately match these autonomous technologies with some given characteristics of cities and help define relevant decision variables. Using continuous approximation equations, the operations costs as well as the externalities induced by a) GADDs in association with an urban consolidation center (UCC) and b) truck-launched UAVs are estimated. Then, the developed mathematical formulations are applied in two different use cases: a part of the Paris suburbs (France) and the historical center of Barcelona (Spain). In less dense and larger service regions such as the Paris suburbs, truck-launched delivery drones seem more suitable to reduce the carriers’ operations costs. In denser neighborhoods such as the Barcelona historical center, GADDs are expected to be more economically profitable. In both use cases, GADDs would generate less externalities. Finally, considering the high uncertainty of some input parameters, a sensitivity analysis of the models is done.

Fundamental Elements of an Urban UTM

Urban airspace environments present exciting new opportunities for delivering drone services to an increasingly large global market, including: information gathering; package delivery; air-taxi services. A key challenge is how to model airspace environments over densely populated urban spaces, coupled with the design and development of scalable traffic management systems that may need to handle potentially hundreds to thousands of drone movements per hour. This paper explores the background to Urban unmanned traffic management (UTM), examining high-level initiatives, such as the USA’s Unmanned Air Traffic (UTM) systems and Europe’s U-Space services, as well as a number of contemporary research activities in this area. The main body of the paper describes the initial research outputs of the U-Flyte R&D group, based at Maynooth University in Ireland, who have focused on developing an integrated approach to airspace modelling and traffic management platforms for operating large drone fleets over urban environments. This work proposes pragmatic and innovative approaches to expedite the roll-out of these much-needed urban UTM solutions. These approaches include the certification of drones for urban operation, the adoption of a collaborative and democratic approach to designing urban airspace, the development of a scalable traffic management and the replacement of direct human involvement in operating drones and coordinating drone traffic with machines. The key fundamental elements of airspace architecture and traffic management for busy drone operations in urban environments are described together with initial UTM performance results from simulation studies.

Unmanned aerial vehicle control costs mirror bird behaviour when soaring close to buildings

Small unmanned aerial vehicles (SUAVs) are suitable for many low-altitude operations in urban environments due to their manoeuvrability; however, their flight performance is limited by their on-board energy storage and their ability to cope with high levels of turbulence. Birds exploit the atmospheric boundary layer in urban environments, reducing their energetic flight costs by using orographic lift generated by buildings. This behaviour could be mimicked by fixed-wing SUAVs to overcome their energy limitations if flight control can be maintained in the increased turbulence present in these conditions. Here, the control effort required and energetic benefits for a SUAV flying parallel to buildings whilst using orographic lift was investigated. A flight dynamics and control model was developed for a powered SUAV and used to simulate flight control performance in different turbulent wind conditions. It was found that the control effort required decreased with increasing altitude and that the mean throttle required increased with greater radial distance to the buildings. However, the simulations showed that flying close to the buildings in strong wind speeds increased the risk of collision. Overall, the results suggested that a strategy of flying directly over the front corner of the buildings appears to minimise the control effort required for a given level of orographic lift, a strategy that mirrors the behaviour of gulls in high wind speeds.

Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data.

GNSS Performance Modelling and Augmentation for Urban Air Mobility.

One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.

Curb Detection and Tracking in Low-Resolution 3D Point Clouds Based on Optimization Framework

Curb detection and tracking is an essential component of autonomous vehicle operation in urban environments. Detecting curbs is a particularly challenging task in urban environments that contain countless dynamic objects. Previous studies have approached curb detection using different types of sensor such as cameras, radar, and LIDAR. Among these, LIDAR sensors have superior advantages in regard to detecting curbs because of their robustness in different weather conditions and they can provide accurate distance measurements. Previous methods based on LIDAR have exploited high-resolution 3D point clouds using high-cost LIDARs. However, the processing of large volumes of information is inefficient for autonomous driving technologies because of real time constraints. This paper presents a novel real-time curb detection and tracking algorithm that makes use of a low-resolution LIDAR. The proposed method consists of three steps. First is the extraction of curb candidates based on Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Second is the selection of the optimal candidate using an optimization framework. Lastly is the tracking of the detected curbs. In the tracking module, we use a combination of spatial consistency and validation gate to track the curb in the occlusion region with dynamic objects. Experiments on a public dataset show that the proposed method achieved 91.54% and 89.76% $F_{1}$ score on the straight and curved road while running at about 18 ms per frame, thereby outperforming the state-of-the-art by a large margin. In addition, we integrated the proposed method with the localization module of our autonomous driving platform. The localization module integrated with the proposed method reduces the positional and lateral root mean square (RMS) error of the vehicle localization by 3.58% and 6.68% respectively. In addition, we compare this method with a deep neural network based method from the perspective of a safety-critical system such as self-driving cars.

Automated Model Construction for Combined Sewer Overflow Prediction Based on Efficient LASSO Algorithm

The prediction of combined sewer overflow (CSO) operation in urban environments presents a challenging task for water utilities. The operation of CSOs (most often in heavy rainfall conditions) prevents houses and businesses from flooding. However, sometimes, CSOs do not operate as they should, potentially bringing environmental pollution risks. Therefore, CSOs should be appropriately managed by water utilities, highlighting the need for adapted decision support systems. This paper proposes an automated CSO predictive model construction methodology using field monitoring data, as a substitute for the commonly established hydrological-hydraulic modeling approach for time-series prediction of CSO statuses. It is a systematic methodology factoring in all monitored field variables to construct time-series dependencies for CSO statuses. The model construction process is largely automated with little human intervention, and the pertinent variables together with their associated time lags for every CSO are holistically and automatically generated. A fast least absolute shrinkage and selection operator solution generating scheme is proposed to expedite the model construction process, where matrix inversions are effectively eliminated. The whole algorithm works in a stepwise manner, invoking either an incremental or decremental movement for including or excluding one model regressor into, or from, the predictive model at every step. The computational complexity is thereby analyzed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 min) and multistep ahead predictions are finally produced and analyzed on a U.K. pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach.

Preparedness against mobility disruption by floods

Civil responders currently have limited information available to them to support flood incident planning. A new generation of tools are emerging that produce more detailed understanding of flood impacts on people and accessibility during floods. These are typically applied in isolation, proving only a partial assessment of impacts. This paper integrates analysis of flood hydraulics, transport accessibility and human safety to explore the impact of flooding on pedestrians and drivers, and its implications on emergency routes and service areas. A reference scenario, developed and used by the local Civil Protection Agency, is applied to Galluzzo in Florence (Italy). Results shows that 37% of inhabitants live close to roads where they can be swept away, and 78% live in locations where parked vehicles can be transported by floodwaters. Furthermore, at its worst 22.5% of road extension is inaccessible; and all hospitals, fire and police stations cannot be reached, highlighting the need to take preventative action from the outset of an event that is predicted to lead to substantial inundation. Integration of multiple indicators of flood impacts, especially those most relevant to human safety, is fundamental to civil responders if they are to successfully planning and implement emergency response operations in urban environments.