Safe and Robust Planning for Uncertain Robots: A Closed-Loop State Sensitivity Approach
Amr Afifi, Tommaso Belvedere, Andrea Pupa, Paolo Robuffo Giordano, Antonio Franchi · IEEE Robotics and Automation Letters · 2024
In this letter, we detail a comprehensive framework for safe and robust planning for robots in presence of model uncertainties. Our framework is based on the recent notion ofclosed-loop state sensitivity, which is extended in this work to also include uncertainties in the initial state. The proposed framework, which considers the sensitivity of the nominal closed-loop system w.r.t.bothmodel parameters and initial state mismatches, is exploited to computetubesthat accurately capture the worst-case effects of the considered uncertainties. In comparison to the current state-of-the-art for safe and robust planning, the proposedclosed-loop state sensitivityframework has the important advantage of computational simplicity and minimal assumptions (and simplifications) regarding the underlying robotclosed-loopdynamics. The approach is validated via both extensive simulations and real-world experiments. In the experiments we consider as case study a nonlinear trajectory optimization problem aimed at generating anintrinsically robust and safe trajectoryfor an aerial robot for safely performing an obstacle avoidance maneuver despite the uncertainties. Simulation and experimental results further confirm the viability and interest of the proposed approach.