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.

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.

Existing target pursuit systems are able to coordinate a team of mobile agents to capture or intercept unauthorized targets. Multi-agent reinforcement learning (MARL) further empowers pursuit strategies with the potential to emerge complex behaviors. However, existing solutions lack the ability to handle the perception uncertainty caused by relative position measurement noises, which blurs the understanding of the target's state and complicates the pursuit strategy learning process. This study proposes PUARL, which enhances the learning under the perception uncertainty process by guiding exploration with probabilistic estimation and adapting the policy based on awareness of perception uncertainty. We validate its performance in terms of both accuracy and efficiency. PUARL achieves a success rate increase of 12.3%+ and a reduction in total steps by 58.3%+, outperforming both state-of-the-art heuristic and learning-based solutions.

This study explores a novel approach to reevaluating urban safety using Vision Large Language Models (VLLMs) integrated with digital twin technology. Our methodology involves randomly selecting street views across various U.S. cities and employing VLLMs to detect and analyze street safety. We incorporate existing user-reported data through an API to validate our findings. Preliminary results indicate that this new approach significantly enhances the accuracy and reliability of urban safety assessments. The integration of VLLMs with digital twin frameworks presents a promising avenue for urban planners and policymakers to achieve more dynamic and real-time insights into city safety, ultimately contributing to smarter and safer urban environments. Our findings suggest that this method holds substantial potential for broader applications in digital twin initiatives, facilitating more informed decision-making processes.

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.