Giuseppe Silano presents STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a M

Standard seminar length ~ 20 min talk, 10 min discussion

ABSTRACT:

This paper presents a motion planning and risk analysis framework for enhancing
human-robot collaboration with a Multi-Rotor Aerial Vehicle. The proposed
method
employs Signal Temporal Logic to encode key mission objectives, including
safety, temporal requirements, and human preferences, with particular emphasis
on ergonomics and comfort. An optimization-based planner generates dynamically
feasible trajectories while explicitly accounting for the vehicle’s nonlinear
dynamics and actuation constraints. To address the resulting non-convex and
non-smooth optimization problem, smooth robustness approximations and
gradient-based techniques are adopted. In addition, an uncertainty-aware risk
analysis is introduced to quantify the likelihood of specification violations
under human-pose uncertainty. A robustness-aware event-triggered replanning
strategy further enables online recovery from disturbances and unforeseen
events
by preserving safety margins during execution. The framework is validated
through MATLAB and Gazebo simulations on an object handover task inspired by
power line maintenance scenarios. Results demonstrate the ability of the
proposed method to achieve safe, efficient, and resilient human-robot
collaboration under realistic operating conditions.

PAPER:

Silano, G., Afifi, A., Saska, M. et al. STL-Based Motion Planning and
Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor
Aerial Vehicle. J Intell Robot Syst (2026).
https://doi.org/10.1007/s10846-026-02403-y

Paper: https://link.springer.com/article/10.1007/s10846-026-02403-y
Videos: https://mrs.fel.cvut.cz/stl-ergonomy-risk-analysis