Research paper

Stochastic avoidance MPC · Combining policy search and uncertainty

Bayesian policy optimization and MPC jointly address control and uncertain dynamic obstacles. The paper reports a real-time quadrotor implementation evaluated in simulation and flight experiments.

Reviewed 2026-11-05 · Source and bibliography checked

Bibliographic details and the editorial overview were checked against the original source. Access conditions and editions are identified with the source.

Original title: Model-predictive control with stochastic collision avoidance using Bayesian policy optimization
Publisher: IEEE
Authors: Olov Andersson, Mariusz Wzorek, Piotr Rudol, Patrick Doherty
DOI: 10.1109/icra.2016.7487661
Publication date: 2016-05
Source language: en
Edition: 2016 IEEE International Conference on Robotics and Automation (ICRA); 2016-05; DOI 10.1109/icra.2016.7487661
Access: Publisher metadata and open author manuscript
Publication status: Published

Related resources and engineering notes

Source overview

Bayesian policy optimization and MPC jointly address control and uncertain dynamic obstacles. The paper reports a real-time quadrotor implementation evaluated in simulation and flight experiments.

Use and review

Author abstract and bibliography checked. Collision-probability approximation and obstacle-prediction assumptions require target-environment validation.

Sources & editions

Model-predictive control with stochastic collision avoidance using Bayesian policy optimization ↗

IEEE · 2016 IEEE International Conference on Robotics and Automation (ICRA); 2016-05; DOI 10.1109/icra.2016.7487661

ICRA2016 author abstract https://www.ida.liu.se/divisions/aiics/publications/ICRA-2016-Model-Predictive-Control.pdf.