报告题目：A hierarchical control frmework for autonomous decision-making vehicles (i.e. Ground Vehicles, Unmanned Aerial Vehicles)
报告人：王雪芳， University of Leicester, UK
报告时间：2023年10月14日10:00-11:00
报告地点：海山楼二楼会议室（B0204）
内容简介：
This paper proposes a comprehensive hierarchical control framework for autonomous decision-making arising in robotics and autonomous systems. In a typical hierarchical control architecture, high level decision making is often characterised by discrete state and decision/control sets. However, a rational decision is usually affected by not only the discrete states of the autonomous system, but also the underlying continuous dynamics even the evolution of its operational environment. This paper proposes a holistic and complete design process and framework for this type of challenging problems, from new modelling and design problem formulation to control design and stability analysis. It addresses the intricate interplay between traditional continuous systems dynamics utilized at the low levels for control design and discrete Markov decision processes (MDP) for facilitating high-level decision making. We model the decision making system in complex environments as a hybrid system consisting of a controlled MDP and autonomous (i.e. uncontrolled) continuous dynamics. The design problem is formulated with a focus on ensuring both safety and optimality while taking into account the influence of both the discrete and continuous state variables of different levels. With the help of the model predictive control (MPC) concept, a decision maker design scheme is proposed for the proposed hybrid decision making model. By carefully designing key ingredients involved in this scheme, it is shown that the recursive feasibility and stability of the proposed autonomous decision making scheme are guaranteed. The proposed framework is applied to develop an autonomous lane changing system for intelligent vehicles. Simulation shows it exhibits a promising ability to handle diverse behaviors in dynamic and complex environments. It is envisaged that the proposed framework is applicable to autonomous decision making for a wide range of robotics and autonomous systems where both safety and optimality are key considerations.
报告人简介：
Xue-Fang Wang, received the B.S. degree from the Ocean university of China, Qingdao college, in 2013. She obtained her Ph.D. degree in Control Theory and Control Engineering from Dalian University of Technology, Liaoning, China, in 2019. From 2017 to 2019, she was a visiting scholar to work with Prof. Andrew R. Teel at the University of California, Santa Barbara, US. From 2020 to 2023, she was a Research Associate at Dalian University of Technology, China, and Loughborough University, UK, respectively. She joined the School of Engineering, University of Leicester, UK, as a Lecturer in Control Engineering in 2023. Her main research interests are focused on multiagent systems, hybrid systems, distributed optimization problems, autonomous systems and model predictive control.
