Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:36:39.336909Z
Paper Citation Record · LEDGER
Hybrid Artificial Intelligence Strategies for Drone Navigation
As of 23 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2501.04472.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
- verified exact2
- verified fuzzy36
- unresolved14
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1af736ba-1bfb-4f98-afa1-7def34ed3fd4 · outbound
This paper cites Bernardos 2 1 Speech Technology and Machine Learning Group, Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av.
Hybrid Artificial Intelligence Strategies for Drone Navigation Bernardos 2 1 Speech Technology and Machine Learning Group, Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:36:38.929183Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15976f26-c018-4ebe-ac84-9fd49ef8511e · outbound
This paper cites This way, it is possible to simulate 2D and 3D navigation scenarios.
Hybrid Artificial Intelligence Strategies for Drone Navigation This way, it is possible to simulate 2D and 3D navigation scenarios
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.984229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation af70f6af-abad-432d-9b31-acee1ca081fc · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.910103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3d10cd68-814f-41a8-8afd-ca80d9a63ec6 · outbound
This paper cites In this work, two navigation tasks (reaching located targets and searching for new targets) have been considered with several scenarios per task.
Hybrid Artificial Intelligence Strategies for Drone Navigation In this work, two navigation tasks (reaching located targets and searching for new targets) have been considered with several scenarios per task
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.952448Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4fc15c71-bb47-4361-81c3-9245d5a16f3a · outbound
This paper cites • AI 2024, 5 2110 The next sections describe the different hybrid strategies depending on the navigation task.
Hybrid Artificial Intelligence Strategies for Drone Navigation • AI 2024, 5 2110 The next sections describe the different hybrid strategies depending on the navigation task
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.888517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fc423753-5896-4cb5-bcc9-6c249d34fe34 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 6
- Resolution
- verified exact
- doi, observed 2026-08-10T21:36:41.007188Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4390338d-5847-46ca-8ffd-763cf24f4d91 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.864536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f1a8f58a-95ea-46cb-9fe8-e8582848f299 · outbound
This paper cites AI 2024, 5 2112 As shown, the algorithm is continuously (at each step) evaluating the existence of an obstacle between the drone and the target.
Hybrid Artificial Intelligence Strategies for Drone Navigation AI 2024, 5 2112 As shown, the algorithm is continuously (at each step) evaluating the existence of an obstacle between the drone and the target
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.809393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 05f1ed4b-c9c0-4156-b203-68d0b0b808d6 · outbound
This paper cites Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization.
Hybrid Artificial Intelligence Strategies for Drone Navigation Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T21:36:39.500574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 48e8737a-bc7c-4a99-8bd0-e449ab46d56b · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.786342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3ff8335e-45a3-4aaf-b612-189a7ba1c0fa · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.754062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a3d2b281-2d66-466d-9547-80fbc3783ac1 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 12
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.720518Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2c4e0c23-7643-4124-afef-162c299612e9 · outbound
This paper cites A hybrid optimization framework for UAV reconnaissance mission planning.
Hybrid Artificial Intelligence Strategies for Drone Navigation A hybrid optimization framework for UAV reconnaissance mission planning
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.007875Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 9e716c8b-fb8e-4780-9b72-23e6d9e343f2 · outbound
This paper cites Information window.
Hybrid Artificial Intelligence Strategies for Drone Navigation Information window
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.672996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 21292504-6199-4c9c-9e5f-f22925db013c · outbound
This paper cites It is important to remark that human interaction strategies (described in the previous section) have not been used during the system evaluation and testing.
Hybrid Artificial Intelligence Strategies for Drone Navigation It is important to remark that human interaction strategies (described in the previous section) have not been used during the system evaluation and testing
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.608222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e971d383-10fd-498f-a682-dc3f16bc997b · outbound
This paper cites Human interaction strategies have been used for recreating specific situations that can be debugged using the explainability mechanisms described above.
Hybrid Artificial Intelligence Strategies for Drone Navigation Human interaction strategies have been used for recreating specific situations that can be debugged using the explainability mechanisms described above
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.640043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 93b9e6a2-0fff-4b75-a82a-9545829e0135 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.542700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 53bd6ff9-a905-4cde-aafe-628db53f0047 · outbound
This paper cites Different number of groups.
Hybrid Artificial Intelligence Strategies for Drone Navigation Different number of groups
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.476091Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5c67cadd-dd75-4e77-969d-94de990ce0a9 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.448075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0d4b586c-4024-47a9-817e-74b77f1cac24 · outbound
This paper cites The hybrid AI combines deep learning models with rule-based strategies to generate the agent action based on the agent state.
Hybrid Artificial Intelligence Strategies for Drone Navigation The hybrid AI combines deep learning models with rule-based strategies to generate the agent action based on the agent state
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.424650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 709a1347-d84f-4ca6-b847-6f3455909a88 · outbound
This paper cites UAV-assisted data collection for internet of things: A survey.
Hybrid Artificial Intelligence Strategies for Drone Navigation UAV-assisted data collection for internet of things: A survey
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.395865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b60f86c3-61b6-4c41-b9b1-b0543a5bc00d · outbound
This paper cites Vision Based Drone Obstacle Avoidance by Deep Reinforcement Learning.
Hybrid Artificial Intelligence Strategies for Drone Navigation Vision Based Drone Obstacle Avoidance by Deep Reinforcement Learning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.359929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 18ab2f59-1033-492e-a026-e4cdc1e97bd1 · outbound
This paper cites Routing protocols for Unmanned Aerial Vehicle Networks: A survey.
Hybrid Artificial Intelligence Strategies for Drone Navigation Routing protocols for Unmanned Aerial Vehicle Networks: A survey
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.330715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1341ca6d-b19c-415f-b4ed-12fab43fe87e · outbound
This paper cites Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey.
Hybrid Artificial Intelligence Strategies for Drone Navigation Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.299277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3fbeb451-8af2-4c42-b057-06216cb1c255 · outbound
This paper cites Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges.
Hybrid Artificial Intelligence Strategies for Drone Navigation Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.271229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4937fe25-adea-44b1-9231-01f61742b59b · outbound
This paper cites AI Advancements: Comparison of Innovative Techniques.
Hybrid Artificial Intelligence Strategies for Drone Navigation AI Advancements: Comparison of Innovative Techniques
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.244537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation dbbd2f8c-8bda-41e3-ba39-ca76cb6dfa09 · outbound
This paper cites Face mask detection in smart cities using deep and transfer learning: Lessons learned from the COVID-19 pandemic.
Hybrid Artificial Intelligence Strategies for Drone Navigation Face mask detection in smart cities using deep and transfer learning: Lessons learned from the COVID-19 pandemic
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.217438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6c0956ac-b333-4e31-b228-fce8b3ed2ec9 · outbound
This paper cites Available online: https://pettingzoo.farama.org/index.html (accessed on 7 September 2024).
Hybrid Artificial Intelligence Strategies for Drone Navigation Available online: https://pettingzoo.farama.org/index.html (accessed on 7 September 2024)
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.625423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 31e467e7-6176-487a-9e61-d582b1e88141 · outbound
This paper cites Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities.
Hybrid Artificial Intelligence Strategies for Drone Navigation Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.089942Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation da2f6c01-df6f-4e33-a554-54b4039f53c0 · outbound
This paper cites Proximal Policy Optimization Algorithms.
Hybrid Artificial Intelligence Strategies for Drone Navigation Proximal Policy Optimization Algorithms
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:36:39.323385Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d357c01-fce1-4355-b63c-a752193b0750 · outbound
This paper cites Smart Speed Camera Based on Automatic Number Plate Recognition for Residential Compounds and Institutions Inside Qatar.
Hybrid Artificial Intelligence Strategies for Drone Navigation Smart Speed Camera Based on Automatic Number Plate Recognition for Residential Compounds and Institutions Inside Qatar
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.058348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6aed0558-e33d-407b-8cb1-83a25df14707 · outbound
This paper cites On the achievability of submeter-accurate UAV navigation with cellular signals exploiting loose network synchronization.
Hybrid Artificial Intelligence Strategies for Drone Navigation On the achievability of submeter-accurate UAV navigation with cellular signals exploiting loose network synchronization
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.029701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 21159530-3f6f-4bd5-a97d-6b4f7399e38d · outbound
This paper cites Panoptic Segmentation: A Review.
Hybrid Artificial Intelligence Strategies for Drone Navigation Panoptic Segmentation: A Review
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:36:39.211234Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34984ac5-77c2-4567-9c0a-de516785ed7e · outbound
This paper cites A review of GNSS-independent UAV navigation techniques.
Hybrid Artificial Intelligence Strategies for Drone Navigation A review of GNSS-independent UAV navigation techniques
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.973778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d02b26b6-20e8-4451-bdf2-3ff36dbbb3c1 · outbound
This paper cites A Formal Basis for the Heuristic Determination of Minimum Cost Paths.
Hybrid Artificial Intelligence Strategies for Drone Navigation A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.943829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fe9ee8d0-b2fa-4d5b-80cd-e970c6b1c155 · outbound
This paper cites Rapidly-exploring random trees: Progress and prospects.
Hybrid Artificial Intelligence Strategies for Drone Navigation Rapidly-exploring random trees: Progress and prospects
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.904266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3f15b7dc-0485-4452-8cb2-d253e4eb37cf · outbound
This paper cites Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review.
Hybrid Artificial Intelligence Strategies for Drone Navigation Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.868144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 91a210ab-7e57-4d1d-94b5-ac0b5d6e82db · outbound
This paper cites Reinforcement learning: A survey.
Hybrid Artificial Intelligence Strategies for Drone Navigation Reinforcement learning: A survey
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.819402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2fdfc53f-e4a6-48d0-b5e0-c5ad45c92c80 · outbound
This paper cites Ostrovski Human-level control through deep reinforcement learning.
Hybrid Artificial Intelligence Strategies for Drone Navigation Ostrovski Human-level control through deep reinforcement learning
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.794892Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a876deae-dd29-4ac6-b5c1-475b7685c8f7 · outbound
This paper cites Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models.
Hybrid Artificial Intelligence Strategies for Drone Navigation Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.754911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation cd1a7897-3f5d-4611-beeb-c3dbaaa0f528 · outbound
This paper cites Autonomous UAV Navigation Using Reinforcement Learning.
Hybrid Artificial Intelligence Strategies for Drone Navigation Autonomous UAV Navigation Using Reinforcement Learning
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:36:39.276648Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b369f5b3-29b6-4cab-9d90-29a20f88b745 · outbound
This paper cites Deep reinforcement learning for drone navigation using sensor data.
Hybrid Artificial Intelligence Strategies for Drone Navigation Deep reinforcement learning for drone navigation using sensor data
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.724599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 136feecd-c207-43ac-8509-8df29c1eac16 · outbound
This paper cites Deep reinforcement learning for drone delivery.
Hybrid Artificial Intelligence Strategies for Drone Navigation Deep reinforcement learning for drone delivery
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.700277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 540e9634-0e6c-494e-8ee5-18a3a974999f · outbound
This paper cites Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning.
Hybrid Artificial Intelligence Strategies for Drone Navigation Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:36:39.294862Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22b9dd1c-7ff0-41bb-a084-dfecf6e095ae · outbound
This paper cites Beyond Static Obstacles: Integrating Kalman Filter with Reinforcement Learning for Drone Navigation.
Hybrid Artificial Intelligence Strategies for Drone Navigation Beyond Static Obstacles: Integrating Kalman Filter with Reinforcement Learning for Drone Navigation
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.678421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 169fefd8-7d11-4315-951f-f51aebb94ca1 · outbound
This paper cites Multi-objective reinforcement learning for autonomous drone navigation in urban areas with wind zones.
Hybrid Artificial Intelligence Strategies for Drone Navigation Multi-objective reinforcement learning for autonomous drone navigation in urban areas with wind zones
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.653988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f50f01d0-3d28-4655-a5a4-ed1e1dc0034d · outbound
This paper cites Available online: https://stable-baselines3.readthedocs.io/en/master/ (accessed on 7 September 2024).
Hybrid Artificial Intelligence Strategies for Drone Navigation Available online: https://stable-baselines3.readthedocs.io/en/master/ (accessed on 7 September 2024)
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.595559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1370ff77-9148-4733-a6f8-623eb9124d79 · outbound
This paper cites Available online: https://github.com/marcotcr/lime (accessed on 7 September 2024).
Hybrid Artificial Intelligence Strategies for Drone Navigation Available online: https://github.com/marcotcr/lime (accessed on 7 September 2024)
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.562436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f8016a2d-ee6c-425d-a90f-9666d62a9bc3 · outbound
This paper cites Available online: https://github.com/shap/shap/blob/master/docs/index.rst (accessed on 7 September 2024).
Hybrid Artificial Intelligence Strategies for Drone Navigation Available online: https://github.com/shap/shap/blob/master/docs/index.rst (accessed on 7 September 2024)
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:39.534427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c0cd6784-ab98-4c3b-a852-1ab187326191 · outbound
This paper cites Including a negative reward when hitting an obstacle is crucial to reduce the number of situations where one drone hits an obstacle (second row).
Hybrid Artificial Intelligence Strategies for Drone Navigation Including a negative reward when hitting an obstacle is crucial to reduce the number of situations where one drone hits an obstacle (second row)
Reference 106
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:36:40.577674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f47b3151-9849-4ea2-8d1f-bd1564678737 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 107
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.183092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1eaef97a-3986-46d6-885c-3adb31ff80c1 · outbound
This paper cites an unresolved cited work.
Hybrid Artificial Intelligence Strategies for Drone Navigation Unresolved cited work
Reference 200
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:36:40.502761Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
No inbound Pith citation observations are available.