Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates

Abstract:We study multi-agent terminal interception when one of several moving candidates is the unknown target and both target identity and candidate terminal locations are uncertain. We develop a receding-horizon deferred-decision trajectory optimization (DDTO) controller that combines conformal terminal-location prediction, sequential candidate rejection, capacity-constrained assignment, and candidate-contingent planning. Under exchangeability conditions for terminal and identity residuals, with predictable querying, we establish fixed-time terminal-location coverage and anytime-valid target retention. At the final replan, when one control step remains, we bound the joint probability that a tightened-set DDTO plan passes nonlinear rollout verification but capture fails. The closed-loop controller applies fallback guidance to any agent without a verified DDTO plan and, across 100 paired scenarios, captures 79% of targets under the strict control-effort budget and 85% when that budget is relaxed.

8 pages, 3 figures, 3 tables. Submitted to IEEE Robotics and Automation Letters

Systems and Control (eess.SY); Machine Learning (cs.LG); Robotics (cs.RO)

arXiv:2610.17003 [eess.SY]

(or arXiv:2610.17003v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2610.17003 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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