Intelligent Agent-Based Hierarchical Task Planning and Robust Control for Autonomous Multi-UAV Transmission Line Inspection
TL;DR
This work proposes a hierarchical framework that connects task-level plans with motion-level control via intelligent agents operating at two different timescales and achieves higher inspection coverage and lower energy consumption in simulation on a 15-km corridor with three UAVs.
Abstract
Most UAV-based transmission line inspection systems still use preset waypoints and treat task planning as separate from motion control. Single-layer methods, such as waypoint-based, learning-based or MPC-based methods, generally handle planning or control independently; none coordinate task reallocation with trajectory adjustment in the event of wind disturbances or sensor faults. We propose a hierarchical framework that connects task-level plans with motion-level control via intelligent agents operating at two different timescales. A high-level strategic agent divides the task into multiple areas. Several low-level execution agents plan local trajectories and coordinate via a distributed consensus protocol. A conditional value-at-risk (CVaR)-aware model predictive controller is employed for motion control to address wind disturbances, obstacle constraints and actuator limits; fault detection activates an adaptive replanning mechanism to maintain operation under degraded sensor conditions or communication loss. In simulation on a 15-km corridor with three UAVs, the framework achieved 15.7% higher inspection coverage than conventional waypoint-based methods, 17.5% lower energy consumption, and a tracking error of less than 0.35 m at crosswinds of up to 12 m/s. Multi-UAV coordination was validated in simulation; a single-UAV field experiment on a 110 kV line segment validated autonomous inspection with 93.3% coverage and approximately 0.5 m positioning accuracy.