Local trajectory planning for mobile robot in cluttered environment based on Model Predictive Control


Panov A. Muravyov K. Dergachyov S.


The task of local trajectory planning for an autonomous wheeled robotic platform in cluttered indoor environment is considered. Such environment might include narrow passages, which width is less than the length of the platform. Therefore, it is not possible to apply standard approach, when the obstacles are inflated with the maximum radius of the platform. We propose a novel approach based on numerical solution of nonlinear model predictive control task. Oblong shape of the platform is approximated with a high-order ellipse. We define differentiable sigmoid potential function, which may be computed for any point of the workspace given position and orientation of the platform. This function is small far from the platform, and very high inside the platform; it increases when moving towards the robot. The value of this potential function is computed for the set of the support obstacle points and added to the cost function. This function serve as a penalty for collision with obstacles or coming too close to them. We develop an algorithm for the mapping support points onto occupancy grid, which provide collision avoidance. We apply Acados open library, which implement numerical solution of nonlinear model predictive task with sequential quadratic programming. Our approach is implemented as a local planner for the collaborative mobile platform. The experiments were made in artificial maze and in real office environment with narrow passages. Proposed approached allowed the robot to come through the passages that were 10-20 cm wider than the platform. Computation time was around 20 milliseconds.

External links

DOI: 10.31776/RTCJ.11306

eLibrary: https://www.elibrary.ru/item.asp?id=54610202

At the Higher School of Economics website: https://publications.hse.ru/en/articles/858068240

Reference link

Muhammad Alhaddad et al. (2023). Local trajectory planning for mobile robot in cluttered environment based on Model Predictive Control // Robotics and Technical Cybernetics, vol. 11, no. 3, pp. 205-214, DOI: 10.31776/RTCJ.11306.