Robustified Time-optmal Motion Planning under Disturbance Conditions
Shuhao Zhang, KU Leuven
The European manufacturing industry is trending towards the production of highly customized complex products in small quantities, for which a flexible production system is key to be cost-effective. Flexible automation demands robotic systems such as mobile platforms and serial manipulators to perform multiple and highly complex tasks in unstructured and uncertain environments, hereby involving extensive sensing such as vision and force, and learning on-the-spot through active sensing. MPC holds great potential for controlling such systems since they are characterized by highly nonlinear and coupled dynamics as well as hard operational constraints.
The overall goal of this project is to develop optimisation-based control approaches that increase the performance and flexibility of robotic systems. The first objective is to develop appropriate robot models and tailored embedded optimisation algorithms that are capable of solving the highly nonlinear optimisation problems arising in robotics MPC at a rate in the range of 100-1000 Hertz.
The second objective is to effectively deal with uncertainties: MPC will be merged with active-sensing strategies to learn properties of the environment and reduce uncertainty in the environment while executing the task, and augmented with risk-averse policies to handle the remaining uncertainty. The third objective is to embed the control algorithms in an automated tool chain that facilitates the robot (re)programming for different tasks. In flexible automation, the programming effort is a major cost factor and it is decisive to the economic viability of a robot application.
Robustified Time-optimal Collision-free Motion Planning for Autonomous Mobile Robots under Disturbance Conditions
This work presents a robustified time-optimal motion planning approach for navigating an Autonomous Mobile Robot (AMR) from an initial state to a terminal state without colliding with obstacles, even when subjected to disturbances, which are modeled as random process noise and measurement noise. The approach iteratively solves the robustified problem by incorporating updated state-dependent safety margins for collision avoidance, the evolution of which is derived separately from the robustified problem. Additionally, a strategy for selecting an alternative terminal state to reach is introduced, which comes into play when the desired terminal state becomes infeasible considering the disturbances. Both of these contributions are integrated into a robustified motion planning and control pipeline, the efficacy of which is validated through simulation experiments.
Time-optimal Point-to-point Motion Planning: A Two-stage Approach
This paper proposes a two-stage approach to formulate the time-optimal point-to-point motion planning problem, involving a first stage with a fixed time grid and a second stage with a variable time grid. The proposed approach brings benefits through its straightforward optimal control problem formulation with a fixed and low number of control steps for manageable computational complexity and the avoidance of interpolation errors associated with time scaling, especially when aiming to reach a distant goal. Additionally, an asynchronous nonlinear model predictive control (NMPC) update scheme is integrated with this two-stage approach to address delayed and fluctuating computation times, facilitating online replanning. The effectiveness of the proposed two-stage approach and NMPC implementation is demonstrated through numerical examples centered on autonomous navigation with collision avoidance.
