Flying over The Uncertain Nature (FORTUNE): Intelligent and Humanistic 3D Path Planning for Low-Altitude Collaboration

• URL: http://arxiv.org/abs/2608.03408v1
• Date: Tue, 04 Aug 2026 10:02:33 GMT
• Title: Flying over The Uncertain Nature (FORTUNE): Intelligent and Humanistic 3D Path Planning for Low-Altitude Collaboration
• Abstract summary: This paper studies 3D multi-temporal path planning and task assignment under uncertain ground PoI demands.<n>We model persistent, temporally predictable, and emergent demands within a unified framework.<n>To solve the resulting large-scale mixed-integer nonlinear problem, we propose a hierarchical offline-online framework.
• Score: 12.002557233331407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of low-altitude intelligent agents is increasing the demand for timely and socially responsible collaborative sensing in dynamic urban environments. However, jointly addressing heterogeneous spatiotemporal demands, environmental uncertainty, and human-centered operational constraints remains challenging. This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands. Unlike existing work assuming static and fully known PoIs, we model persistent, temporally predictable, and emergent demands within a unified framework. We further incorporate altitude-dependent societal and environmental costs, including noise exposure and public safety risks, to balance sensing performance with socially compliant operations. To solve the resulting large-scale mixed-integer nonlinear problem, we propose FORTUNE, a hierarchical offline-online framework. Offline, a Transformer predicts Type-II PoI activation windows, while an enhanced sparrow search algorithm generates coordinated flight plans through priority-aware decoding and danger-aware evolution. Online, a lightweight refinement module accommodates emerging Type-III PoIs while preserving global mission coherence. Experiments on real-world traffic data and synthetic scenarios show that FORTUNE consistently outperforms state-of-the-art methods in effectiveness, scalability, and practical applicability.

Related papers

• Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
  Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
  arXiv  Detail & Related papers  (2026-08-25T12:16:28Z)
• Multi UAVs Preflight Planning in a Shared and Dynamic Airspace [4.802862639452379]
  We propose DTAPP-IICR: a Delivery-Time Aware Prioritized Planning method with Incremental and Iterative Conflict Resolution.<n>Our framework first generates an initial solution by prioritizing missions based on urgency.<n>It computes roundtrip trajectories using SFIPP-ST, a novel 4D single-agent planner.<n>On benchmarks with temporal NFZs, DTAPP-IICR achieves near-100% success with fleets of up to 1,000 UAVs.
  arXiv  Detail & Related papers  (2026-02-12T15:18:46Z)
• PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution [64.15555230987222]
  PACEvolve is a framework designed to robustly govern the agent's context and search dynamics.<n>We demonstrate that PACEvolve provides a systematic path to consistent, long-horizon self-improvement.
  arXiv  Detail & Related papers  (2026-01-15T18:25:23Z)
• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL [37.79695337425523]
  Low-altitude intelligent networks (LAINs) can support mission-critical applications such as disaster response, environmental monitoring, and real-time sensing.<n>These systems face key challenges, including energy-constrained UAVs, task arrivals, and heterogeneous computing resources.<n>We propose an integrated air-ground collaborative network and formulate a time-dependent integer nonlinear programming problem that jointly optimize UAV trajectory planning and task offloading decisions.
  arXiv  Detail & Related papers  (2025-12-05T08:14:45Z)
• Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network [48.485907216785904]
  Low Earth orbit (LEO) satellite constellations offer promising solutions with global coverage and reduced latency.<n>Yet struggle with intermittent coverage and intermittent communication windows due to orbital dynamics.<n>Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-ground communication and reliable radio frequency (RF) links for HAP-to-ground transmission.
  arXiv  Detail & Related papers  (2025-09-16T06:16:56Z)
• Entropy-Constrained Strategy Optimization in Urban Floods: A Multi-Agent Framework with LLM and Knowledge Graph Integration [0.7424725048947504]
  Extreme urban rainfall events pose significant challenges to emergency scheduling systems.<n>H-J is a hierarchical multi-agent framework that integrates knowledge-guided prompting, entropy-constrained generation, and feedback-driven optimization.<n> Experiments show that H-J outperforms rule-based and reinforcement-learning baselines in traffic smoothness, task success rate, and system robustness.
  arXiv  Detail & Related papers  (2025-08-20T12:13:03Z)
• Deep Active Inference Agents for Delayed and Long-Horizon Environments [1.693200946453174]
  AIF agents rely on accurate immediate predictions and exhaustive planning, a limitation that is exacerbated in delayed environments.<n>We propose a generative-policy architecture featuring a multi-step latent transition that lets the generative model predict an entire horizon in a single look-ahead.<n>We evaluate our agent in an environment that mimics a realistic industrial scenario with delayed and long-horizon settings.
  arXiv  Detail & Related papers  (2025-05-26T11:50:22Z)
• Platform-Aware Mission Planning [50.56223680851687]
  We introduce the problem of Platform-Aware Mission Planning (PAMP), addressing it in the setting of temporal durative actions.<n>The first baseline approach amalgamates the mission and platform levels, while the second is based on an abstraction-refinement loop.<n>We prove the soundness and completeness of the proposed approaches and validate them experimentally.
  arXiv  Detail & Related papers  (2025-01-16T16:20:37Z)
• LaPlaSS: Latent Space Planning for Stochastic Systems [8.529245639496274]
  We propose a "generate-and-test" approach to risk-bounded planning for autonomous mobile agents. We use a variational autoencoder to learn a latent linear dynamics model and encode the planning problem into the latent space to generate the candidate trajectory. We demonstrate that our algorithm, LaPlaSS, is able to generate trajectory plans with bounded risk for a real-world agent with learned dynamics.
  arXiv  Detail & Related papers  (2024-04-10T14:52:35Z)
• Temporal Predictive Coding For Model-Based Planning In Latent Space [80.99554006174093]
  We present an information-theoretic approach that employs temporal predictive coding to encode elements in the environment that can be predicted across time. We evaluate our model on a challenging modification of standard DMControl tasks where the background is replaced with natural videos that contain complex but irrelevant information to the planning task.
  arXiv  Detail & Related papers  (2021-06-14T04:31:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.