Task Coordination and Trajectory Optimization for Multi-Aerial Systems via Signal Temporal Logic: A Wind Turbine Inspection Study

2024-10-09

arXiv.org

AI Summary

To address safety, timeliness, and dynamic adaptability challenges in multi-UAV collaborative inspection of wind turbines, this paper proposes a Signal Temporal Logic (STL)-based joint optimization framework. It uniformly encodes time-sensitive constraints, dynamical feasibility limits, and safety-critical collision avoidance as STL specifications embedded within a nonlinear optimization problem. An event-triggered re-planning mechanism and a generalized robustness scoring function are introduced to balance user preferences, multi-agent conflict mitigation, and responsiveness to unexpected disturbances. The method is validated in MATLAB/Gazebo simulations and on physical UAV prototypes, demonstrating significant improvements in trajectory physical feasibility, temporal efficiency, and operational robustness. This work establishes a verifiable and scalable STL-driven paradigm for autonomous multi-robot coordination in complex, dynamic environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents a method for task allocation and trajectory generation in cooperative inspection missions using a fleet of multirotor drones, with a focus on wind turbine inspection. The approach generates safe, feasible flight paths that adhere to time-sensitive constraints and vehicle limitations by formulating an optimization problem based on Signal Temporal Logic (STL) specifications. An event-triggered replanning mechanism addresses unexpected events and delays, while a generalized robustness scoring method incorporates user preferences and minimizes task conflicts. The approach is validated through simulations in MATLAB and Gazebo, as well as field experiments in a mock-up scenario.

Problem

• Multi-drone optimization
• Inspection task planning
• Route and task allocation

Innovation

• Signal Temporal Logic
• Drone Inspection Optimization
• User Preference Integration