As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, EventPro. EventPro features two components: Count Every Rotation achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. Every Rotation Counts leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that EventPro achieves a sensing latency of 3 ms and a rotational speed estimation error of merely 0.23%. Additionally, EventPro infers drone flight commands with 96.5% precision and improves drone tracking accuracy by over 22% when combined with other sensing modalities. Demo: https://eventpro25.github.io/EventPro/.

This paper addresses the challenges of achieving optimal quality of information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) system, where vehicles not originally designed for sensing are leveraged to collect real-time data as they traverse urban environments. These challenges are exacerbated by the interrelated issues of sensing coverage, sensing reliability, and the inherently dynamic nature of participating vehicles. To tackle these challenges, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and sensing reliability under budget constraints in NVMCS systems. QUIDS improves QoI by introducing a novel metric, Aggregated Sensing Quality (ASQ), designed to quantitatively capture the concept of QoI by integrating both sensing coverage and sensing reliability. Moreover, we develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates monetary incentives under uncertain vehicle conditions, thereby further improving ASQ. Evaluation using real-world data collected from a deployed NVMCS system in a metropolitan area demonstrates the effectiveness of QUIDS. The ASQ metric shows a 38% improvement over non-dispatching scenarios and a 10% enhancement over state-of-the-art methods. Additionally, QUIDS reduces reconstruction map errors by 39–74% across various reconstruction algorithms, validating its efficacy in improving QoI within NVMCS systems. Addressing the often-overlooked issue of sensing reliability in existing studies, the QUIDS system leverages non-dedicated vehicles and incorporates a quality-informed incentive-driven dispatching system to jointly optimize sensing coverage and sensing reliability. This enables low-cost, high-quality, and scalable urban environmental monitoring without the need for dedicated sensing infrastructure, and makes the system applicable to diverse smart-city scenarios such as traffic monitoring and environmental sensing.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit thetemporal consistencyandspatial complementaritybetween these two modalities, we propose two innovative modules:(i)the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and(ii)the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083$m$localization accuracy and 5.12$ms$end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, translating this insight into a practical system is challenging due to issues in estimating locations with diverse and unknown localization errors of BMAVs, and allocating resources of AMAVs considering interconnected influential factors. This work introduces TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We design an error-aware joint location estimation model to perform intermittent joint estimation for BMAVs and introduce a similarity-instructed adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement and validate TransformLoc on industrial drones. Results show it outperforms all baselines by up to 68% in localization performance, improving navigation success rates by 60%. Extensive robustness and ablation experiments further highlight superiority of its design.

Gas source localization is pivotal for the rapid mitigation of gas leakage disasters, where mobile robots emerge as a promising solution. However, existing methods predominantly schedule robots’ movements based on reactive stimuli or simplified gas plume models. These approaches typically excel in idealized, simulated environments but fall short in real-world gas environments characterized by their patchy distribution. In this work, we introduce SniffySquad , a multi-robot olfaction-based system designed to address the inherent patchiness in gas source localization. SniffySquad incorporates a patchiness-aware active sensing approach that enhances the quality of data collection and estimation. Moreover, it features an innovative collaborative role adaptation strategy to boost the efficiency of source-seeking endeavors. Extensive evaluations demonstrate that our system achieves an increase in the success rate by \(20\%+\) and an improvement in path efficiency by \(30\%+\) , outperforming state-of-the-art gas source localization solutions.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

Urban particulate pollution presents considerable public health hazards, underscoring the need for effective control measures in various cities. This paper proposes SmartSpr, a physics-informed urban mobile sprinkler scheduling system designed for enhanced efficiency in reducing particulate pollution. SmartSpr incorporates a Physics-Informed Neural Network (PINN)-based model, enriched with Bayesian optimization, to accurately simulate the impact of mobile sprinklers on particulate matter (PM) dispersion. Building on this sprinkling effect model, a selective sprinkling strategy considering the replenish process is proposed. This strategy employs a sparsity-driven decoupling simulated annealing algorithm to refine sprinkler routes, prioritizing areas with substantial environmental benefits. Extensive field experiments and simulations have validated SmartSpr, demonstrating a 64.8% reduction in prediction error of SmartSpr's sprinkling model compared to the leading baseline and an 18% enhancement in pollutant reduction efficiency of the proposed scheduling algorithm.

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs’ unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource- constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68% in localization performance, motivating up to 60% navigation success rate improvement.

Urban particulate pollution presents considerable public health hazards, underscoring the need for effective control measures in various cities. A prevalent approach involves employing mobile sprinkling trucks. This paper proposes a Sprinkler-UAV Cooperative Active Scheduling System for enhanced efficiency in reducing particulate pollution. The system employs ground-based sprinkler trucks and airborne air pollution detection drones to actively explore and reduce PM2.5 in environments with dynamic and unknown pollution distributions. Preliminary experiments have demonstrated the effectiveness of using sprinklers for urban particulate matter control.

As unmanned aerial vehicles (UAVs) become more prevalent in smart cities, their capacity for visual language navigation (VLN) is garnering increasing interest. VLN in cities has significant applications in delivery, rescue, and security patrol, among other fields. One of the most representative tasks is to navigate to specific locations following the language instructions. While some current methods have achieved notable results in indoor settings, challenges persist outdoors, including agents’ inaccurate spatial understanding and ambiguous language instructions. In this work, we explore an embodied navigation agent design, in which a fine-grained spatial verbalizer and a history path memory are proposed to guarantee accurate VLN in open 3D urban environments.

Multi-UAV systems have shown immense potential in handling complex tasks in large-scale, dynamic, and cold-start (i.e., limited prior knowledge) scenarios, such as wildfire suppression. Due to the dynamic and stochastic environmental conditions, the scheduling for sensing tasks (i.e., fire monitoring) and operation tasks (i.e., fire suppression) should be executed concurrently to enable real-time information collection and timely intervention of the environment. However, the planning inclinations of sensing and operation tasks are typically inconsistent and evolve over time, complicating the task of identifying the optimal strategy for each UAV. To solve this problem, this paper proposes SOScheduler, a collaborative multi-UAV scheduling framework for integrated sensing and operation in large-scale and dynamic wildfire environments. We introduce a spatio-temporal confidence-aware assessment model to dynamically and directly pinpoint locations that can optimally enhance the understanding of environmental dynamics and operational effectiveness, as well as a priority graph-instructed scalable scheduler to coordinate multi-UAV in an efficient manner. Experiments on real multi-UAV testbeds and large-scale physical feature-based simulations show that our SOScheduler reduces the fire expansion ratio by 59% and enhances the fire coverage ratio by 190% compared to state-of-the-art (SOTA) solutions.

Emergency rescue scenarios are considered to be high-risk scenarios. Using a micro air vehicle (MAV) swarm to explore the environment can provide valuable environmental information. However, due to the absence of localization infrastructure and the limited on-board capabilities, it's challenging for the low-cost MAV swarm to maintain precise localization. In this paper, a collaborative localization system for the low-cost heterogeneous MAV swarm is proposed. This system takes full advantage of advanced MAV to effectively achieve accurate localization of the heterogeneous MAV swarm through collaboration. Subsequently, H-SwarmLoc, a reinforcement learning-based planning method is proposed to plan the advanced MAV with a non-myopic objective in real-time. The experimental results show that the localization performance of our method improves 40% on average compared with baselines.