PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/pro-spect-probabilistically-safe-scalable-planning-for-energy-aware-coordinated-uav-ugv-teams-in-stochastic-environments

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Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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Canonical ID pro-spect-probabilistically-safe-scalable-planning-for-energy-aware-coordinated-uav-ugv-teams-in-stochastic-environments | Route /signal-canvas/pro-spect-probabilistically-safe-scalable-planning-for-energy-aware-coordinated-uav-ugv-teams-in-stochastic-environments

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

PDF: https://arxiv.org/pdf/2604.02142v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.241Z

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Watch and verify: PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

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Subject: PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

Verdict

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We formulate the problem as a Mixed-Integer Program and propose PRO-SPECT, a polynomial-time algorithm that generates risk-bounded plans.
Implicationpartial
Directly stated in abstract with clear description of algorithm properties
Verificationpartial
partial
Evidencepartial
bound the probability of failure (energy depletion) across the entire mission to a user-specified risk level.
Implicationpartial
Explicitly stated in abstract with clear technical specification
Verificationpartial
partial
Evidencepartial
The algorithm supports both offline planning and online re-planning, enabling the team to adapt to disturbances while preserving the risk bound.
Implicationpartial
Directly stated in abstract with clear functional description
Verificationpartial
partial
Evidencepartial
Unlike prior work that assumed deterministic travel times or used fixed robustness margins, we model travel times as random variables
Implicationpartial
Explicit comparison to prior work with clear technical distinction
Verificationpartial
partial
Evidencepartial
The UAV must visit a set of air points in minimum time while respecting energy constraints
Implicationpartial
Directly stated problem formulation in abstract
Verificationpartial
partial
Evidencepartial
relying on the UGV as a mobile charging station.
Implicationpartial
Directly stated role definition in abstract
Verificationpartial
partial
Evidencepartial
We provide theoretical results on solution feasibility and time complexity.
Implicationpartial
Directly stated in abstract with clear research contribution
Verificationpartial
partial
Evidencepartial
We also demonstrate the performance of our method via numerical comparisons and simulations.
Implicationpartial
Directly stated evaluation methodology in abstract
Verificationpartial
partial

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PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

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