Work

Resilient Multi-UAV Mission Execution Under Partial Capability Degradation via Event-Triggered Task Reallocation

Year: 2026

Type: article

Abstract: A UAV may remain able to service targets after its motion-execution capability declines, yet its current assignment can become inefficient. We propose Event-Triggered Capability-Aware Task Reallocation (ETCR), a centralized task layer coupled to a standard multi-agent proximal policy optimization (MAPPO) motion policy. ETCR uses a speed-based travel-cost proxy and switching and workload penalties to reassess current assignments at mission events. In planar missions with four UAVs and six targets, the original five-pair confirmation yielded a severe-degradation success gain of 5.10 percentage points (pp; bootstrap 95% interval 2.80–6.60). Five additional training pairs yielded +7.70 pp on the original cases; all ten pairs on a new fixed case bank yielded +9.05 pp (7.20–10.80). With identical frozen actors, replacing distance allocation with ETCR improved severe success by 4.3 and 4.1 pp for MAPPO-origin and ETCR-origin actors, respectively. A separate contrast holding penalties and triggers fixed attributed 6.2 and 5.9 pp to the speed-aware cost. Isolated degradation tests support speed-related adaptation; stable response-only and additional soft-trigger benefits were not established. The results demonstrate the value of updating task guidance from speed-related capability information within the tested hierarchical execution setting.

Source: Machines

DOI: 10.3390/machines14111326

Authors: Qingyun Yang, Yewei Zhang, Kun Yang, Zhenyang Xu, Shuyi Shao

Institutions: Zaozhuang University, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China