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

TL;DR

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, and an optimization framework generates dynamically feasible trajectories while considering the MRAV's physical constraints.

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.

Figures (14)

• Figure 1: Illustration of an MRAV facilitating tool delivery to a human worker in a power line scenario.
• Figure 2: Schematic depiction of the object handover scenario, highlighting the operator's preferred handover region (yellow), the designated location (blue), and obstacles along with the restricted area behind the operator (red).
• Figure 3: Schematic representation of a GTMR system with its world $\mathcal{F}_W = \{O_W, \mathbf{x}_W, \mathbf{y}_W, \mathbf{z}_W\}$ and body $\mathcal{F}_B = \{O_B, \mathbf{x}_B, \mathbf{y}_B, \mathbf{z}_B\}$ reference frames.
• Figure 4: Illustration of the expected value $\mathbb{E}(Z)$, $\beta$-Value-at-Risk $\mathrm{VaR}_\beta(Z)$, and Conditional $\beta$-Value-at-Risk $\mathrm{CVaR}_\beta(Z)$ for a specified risk level $\beta \in (0,1)$. The axes represent the stochastic variable $z$ and its CDF $F_Z(z)$. The shaded area corresponds to $\%\beta$ of the total area under $F_Z(z)$. $\mathrm{VaR}_\beta(Z)$ represents the value of $z$ at the $\beta$-tail of the distribution, while $\mathrm{CVaR}_\beta(Z)$ averages the worst-case values of $z$ in the $\beta$-tail. A negative $\mathrm{CVaR}_\beta(Z)$ indicates unsafe behavior.
• Figure 5: Object handover scenario with highlighted approaching regions (yellow) representing the ergonomic preferences from top-to-bottom and left-to-right. Reference axes aid in visualizing the drone's maneuverability margin ($\psi_\mathrm{vis} \pm \gamma$) in the displacement direction.