Development of a model predictive controller for motion planning in a dynamic urban search and rescue environment

Development of a model predictive controller for motion planning in a dynamic urban search and rescue environment

Rado, Karlo (TU Delft Aerospace Engineering)

Jamshidnejad, A. (mentor)
Baglioni, M. (graduation committee)

Delft University of Technology

Aerospace Engineering | Control & Simulation

2023-05-31

A key challenge for SaR robotics is to avoid dynamic obstacles in cluttered environments, with limited and noisy information. In this research, a controller for SaR robots is developed by coupling a local heuristic motion planner with a model predictive control (MPC) based trajectory tracker. Constraint tightening and tube-based control are used to make the MPC robust to model mismatch and additive measurement noise, while the motion planner is integrated with the MPC. The motion planner periodically supplies a reference trajectory to the trajectory tracker, but the MPC can request additional updates in case of a noticeable mismatch between the predicted and measured environment, based on a user-defined threshold. A case study is designed in MATLAB where a single robot needs to reach a goal through a cluttered environment with dynamic obstacles. Results from the case study show that the MPC method outperforms two state-of-the-art control approaches that are based on the rapidly-exploring random tree (RRT) and artificial potential function (APF) methods. In particular, the heuristic and MPC coupled controller showed a higher success rate in reaching the goal without collisions, and displayed a lower path length in cases with both low and high computational budget.

Motion planning
Mobile Robots
local trajectory generation
dynamic obstacles
obstacle avoidance
tube-based model predictive control
Robust control
urban search and rescue

http://resolver.tudelft.nl/uuid:1b42ff61-1194-44b3-b303-7d36f3933be3

Student theses

master thesis

© 2023 Karlo Rado