What does this research mean for the field?

The proposed Adaptive Differential Evolution-enhanced Distributed Stochastic Algorithm (ADE-DSA) within a joint trajectory optimization framework significantly improves search efficiency and communication robustness for heterogeneous UAV teams in dense urban environments compared to state-of-the-art distributed solvers. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a distributed joint trajectory optimization framework for heterogeneous UAV teams that autonomously balances search coverage and communication connectivity under urban non-line-of-sight conditions.

September 14, 2026Robotics and Autonomous SystemsOpen Access

Distributed joint trajectory optimization for search and relay UAVs under urban NLoS communication constraints

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Authors

PXPeng XiaoNorthwestern Polytechnical University NLNi LiNorthwestern Polytechnical University YPYu PanNorthwestern Polytechnical University

Discussion

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Overview

Simulation study demonstrates improved search efficiency and communication robustness in heterogeneous UAV teams, highlighting effective trajectory optimization under urban signal blockages.

Key Points

To develop a distributed joint trajectory optimization framework for heterogeneous UAV teams that autonomously balances search coverage and communication connectivity under urban non-line-of-sight conditions.
Formulated a communication-aware objective function combining search rewards with urban occlusion models within a Distributed Model Predictive Control (DMPC) framework.
Reformulated the receding-horizon trajectory coordination problem as a Continuous Distributed Constraint Optimization Problem (C-DCOP).
Developed the Adaptive Differential Evolution–enhanced Distributed Stochastic Algorithm (ADE-DSA) to facilitate decentralized, high-dimensional online optimization.
ADE-DSA consistently outperformed existing state-of-the-art distributed solvers on both standard C-DCOP benchmark functions and the joint search-and-relay formulation.
The integrated search and relay framework substantially increased search coverage efficiency and enhanced communication robustness in dense urban non-line-of-sight settings.

Cite This Study

Xiao et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3850926e14a848b28ae https://doi.org/10.1016/j.robot.2026.105735

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September 14, 2026Robotics and Autonomous SystemsOpen Access

Distributed joint trajectory optimization for search and relay UAVs under urban NLoS communication constraints

View Full Paper

Ask AI

Bookmark

Authors

PXPeng XiaoNorthwestern Polytechnical University NLNi LiNorthwestern Polytechnical University YPYu PanNorthwestern Polytechnical University

Discussion

Member takes

Overview

Simulation study demonstrates improved search efficiency and communication robustness in heterogeneous UAV teams, highlighting effective trajectory optimization under urban signal blockages.

Key Points

To develop a distributed joint trajectory optimization framework for heterogeneous UAV teams that autonomously balances search coverage and communication connectivity under urban non-line-of-sight conditions.
Formulated a communication-aware objective function combining search rewards with urban occlusion models within a Distributed Model Predictive Control (DMPC) framework.
Reformulated the receding-horizon trajectory coordination problem as a Continuous Distributed Constraint Optimization Problem (C-DCOP).
Developed the Adaptive Differential Evolution–enhanced Distributed Stochastic Algorithm (ADE-DSA) to facilitate decentralized, high-dimensional online optimization.
ADE-DSA consistently outperformed existing state-of-the-art distributed solvers on both standard C-DCOP benchmark functions and the joint search-and-relay formulation.
The integrated search and relay framework substantially increased search coverage efficiency and enhanced communication robustness in dense urban non-line-of-sight settings.

Cite This Study

Xiao et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3850926e14a848b28ae https://doi.org/10.1016/j.robot.2026.105735

View Full Paper

Ask AI

Bookmark