Paper Citation Record · LEDGER

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.05996.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05996 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:10:22.747620Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy26
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 135ee845-2ef2-4bf9-9c51-ce7f4dfa8aa7 · outbound

This paper cites an unresolved cited work.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:10:23.234529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f04ab08f-f095-4ae5-8344-e2cafc16fe21 · outbound

This paper cites Aghaee, L.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Aghaee, L

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.220276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cdc3c72f-b1b6-448b-8fe5-9f4cbb47a743 · outbound

This paper cites Stamatopoulos, A.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Stamatopoulos, A

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.205435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 10615556-1218-4824-aa9c-467fc8efd65f · outbound

This paper cites an unresolved cited work.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:10:23.190413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5b94b63-4217-43e9-954d-79f1a15fe2b9 · outbound

This paper cites Chermprayong, ”Enabling Technologies for Precise Aerial Manu- facturing with Unmanned Aerial Vehicles,” Imperial College London, 2019.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Chermprayong, ”Enabling Technologies for Precise Aerial Manu- facturing with Unmanned Aerial Vehicles,” Imperial College London, 2019

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.176347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ba343bb-1cfe-4d95-ac3d-e5ebcd4a6973 · outbound

This paper cites an unresolved cited work.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:10:23.161864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ceebf60-e00b-4c05-8d19-6ea3a8a5b4f7 · outbound

This paper cites an unresolved cited work.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:10:23.148018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f2e100cb-413d-41f7-ab41-517b7c77be86 · outbound

This paper cites Stochastic model predictive control-based countermeasure methodology for satellites against indirect kinetic cyber-attacks,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Stochastic model predictive control-based countermeasure methodology for satellites against indirect kinetic cyber-attacks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.134530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66f7fd4f-1919-4d7d-911e-eb3a95182645 · outbound

This paper cites Patchett, ”On the derivation and analysis of decision architectures for unmanned aircraft systems,” 2013.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Patchett, ”On the derivation and analysis of decision architectures for unmanned aircraft systems,” 2013

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.120422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8c865f4e-e137-4321-8362-05d934d3c29b · outbound

This paper cites Sliding mode control of electro- magnetic tethered satellite formation,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Sliding mode control of electro- magnetic tethered satellite formation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.106097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f234bafa-fb2b-4dc4-9666-7280a97b32f9 · outbound

This paper cites Ramezani and M.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Ramezani and M

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.092589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50dd9768-7a8f-420d-999e-14d782288bd6 · outbound

This paper cites Ramezani, M.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Ramezani, M

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.078788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fdb4c54c-352a-4fd7-8ca5-5d74d3419a97 · outbound

This paper cites Ramezani, M.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Ramezani, M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.065478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a9041a3e-dfc1-46c5-ab83-32c0d497f542 · outbound

This paper cites Ramezani, H.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Ramezani, H

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.051598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5071fde4-fc0e-4055-93a7-7831579e1d15 · outbound

This paper cites Ramezani, M.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Ramezani, M

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.036554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2698b83a-db21-4cb9-8203-263db1353b47 · outbound

This paper cites an unresolved cited work.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:10:23.022216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33337142-de37-4ff1-bcf5-c7708097d5ee · outbound

This paper cites Teixeira, G.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Teixeira, G

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:23.007538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14f19250-b942-45b6-8990-950d6830d2b0 · outbound

This paper cites Song et al., ”From deterministic to stochastic: an interpretable stochastic model-free reinforcement learning framework for portfolio optimization,” Applied Intelligence, vol.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Song et al., ”From deterministic to stochastic: an interpretable stochastic model-free reinforcement learning framework for portfolio optimization,” Applied Intelligence, vol

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.993920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 267b9c08-1c77-4483-bf47-df30d5655d42 · outbound

This paper cites Human-centric aware UA V trajectory planning in search and res- cue missions employing multi-objective reinforcement learning with AHP and similarity-based experience replay,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Human-centric aware UA V trajectory planning in search and res- cue missions employing multi-objective reinforcement learning with AHP and similarity-based experience replay,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.980037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e42d5d7-1797-4419-88b6-fc417fc1dd06 · outbound

This paper cites Towards autonomous multi-UA V wireless network: A survey of reinforcement learning-based approaches,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Towards autonomous multi-UA V wireless network: A survey of reinforcement learning-based approaches,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.965973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7ce35805-45ea-4113-b76e-9ebfd322b89d · outbound

This paper cites Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.950988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e8ff016-90cf-4e46-bca3-8b343fb3bf9b · outbound

This paper cites A survey on curriculum learning,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning A survey on curriculum learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.935730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90555504-43ec-47e5-9224-2b85b45cd684 · outbound

This paper cites Safe and adaptive autonomous navigation under uncertainty based on sequen- tial waypoints and reachability analysis,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Safe and adaptive autonomous navigation under uncertainty based on sequen- tial waypoints and reachability analysis,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.921986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0873a4fb-7470-429e-972b-07b80d84d0f1 · outbound

This paper cites Markov decision processes,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Markov decision processes,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.906982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7cac752a-3b5b-4d50-a9fd-6e083dae3237 · outbound

This paper cites ”Trajectory Generation and Control for Precise Aggressive Maneuvers with Quadrotors.” The International Journal of Robotics Research , 2012, pp.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning ”Trajectory Generation and Control for Precise Aggressive Maneuvers with Quadrotors.” The International Journal of Robotics Research , 2012, pp

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.892264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a3feb56d-8c10-4e4a-9431-ec072a29b1c2 · outbound

This paper cites Aerial additive manu- facturing with multiple autonomous robots,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Aerial additive manu- facturing with multiple autonomous robots,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.878375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 73743ff9-7b93-4a27-b5a2-f56ee79afa62 · outbound

This paper cites Challeng- ing common assumptions in convex reinforcement learning,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Challeng- ing common assumptions in convex reinforcement learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.862477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8a43b6a5-0b24-4f86-9f0b-254e70e4fffd · outbound

This paper cites Pulse-width modulation,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Pulse-width modulation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.848023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89f6577c-79da-4f03-b18f-283b06439a4a · outbound

This paper cites A novel DDPG method with prioritized experience replay,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning A novel DDPG method with prioritized experience replay,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.832740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 511bc80d-fe45-493a-8aa1-99f5a3391582 · outbound

This paper cites Real-time au- tonomous residential demand response management based on twin delayed deep deterministic policy gradient learning,.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Real-time au- tonomous residential demand response management based on twin delayed deep deterministic policy gradient learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.817596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 15cbe47a-d206-4cfe-b17c-0c2d9de8538c · outbound

This paper cites Lin, ”Self-improving reactive agents based on reinforcement learning, planning, and teaching,” Machine Learning , vol.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Lin, ”Self-improving reactive agents based on reinforcement learning, planning, and teaching,” Machine Learning , vol

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:10:22.801287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ec285d6-e99e-4a66-84ba-c9d5f54d170c · outbound

This paper cites A Survey on Activation Functions and their relation with Xavier and He Normal Initialization.

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning A Survey on Activation Functions and their relation with Xavier and He Normal Initialization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T17:10:22.747620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.