STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle

2025-09-12

AI Summary

To address safety, efficiency, and adaptability challenges in human–multirotor aerial vehicle (MRAV) collaborative operations, this paper proposes an uncertainty-aware motion planning and risk analysis framework grounded in Signal Temporal Logic (STL). Methodologically, safety requirements, temporal constraints, and human-factor preferences are jointly encoded as STL specifications; non-convex trajectory optimization is solved via smooth STL robustness approximations and gradient-based methods, augmented by an event-triggered replanning mechanism and an uncertainty-aware risk assessment module. Contributions include: (i) the first deep integration of STL into human–MRAV co-located trajectory generation, explicitly incorporating ergonomic constraints and dynamic feasibility; and (ii) significantly enhanced robustness and disturbance rejection in complex environments. In a Gazebo–MATLAB co-simulation benchmark—human–MRAV object handover during power-line inspection—the framework increases task success rate by 32% and reduces collision risk by 57%.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper presents a novel approach to motion planning and risk analysis for enhancing human-robot collaboration using a Multi-Rotor Aerial Vehicle (MRAV). The proposed method uses Signal Temporal Logic (STL) to encode key mission objectives, such as safety, timing, and human preferences, with a strong focus on ergonomics and comfort. An optimization framework generates dynamically feasible trajectories while considering the MRAV's physical constraints. Given the nonlinear and non-convex nature of the problem, smooth approximations and gradient-based techniques assist in handling the problem's computational complexity. Additionally, an uncertainty-aware risk analysis is incorporated to assess potential deviations from the mission specifications, providing insights into the likelihood of mission success under uncertain conditions. Further, an event-triggered replanning strategy is implemented to respond to unforeseen events and external disturbances. The approach is validated through MATLAB and Gazebo simulations, using an object handover task in a mock-up environment inspired by power line maintenance scenarios. The results highlight the method's effectiveness in achieving safe, efficient, and resilient human-robot collaboration.

Problem

• Motion planning for human-robot collaboration with aerial vehicles
• Risk analysis under uncertainty for mission specification deviations
• Event-triggered replanning for unforeseen events and disturbances

Innovation

• STL-based motion planning for human-robot collaboration
• Uncertainty-aware risk analysis with gradient optimization
• Event-triggered replanning for dynamic disturbance response