首页|Polygon decomposition for obstacle representation in motion planning with Model Predictive Control

Polygon decomposition for obstacle representation in motion planning with Model Predictive Control

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  • NETL
  • NSTL
  • Elsevier
Model Predictive Control (MPC) is a powerful tool for planning the local trajectory of autonomous mobile robots. The paper considers a new algorithm for trajectory planning and obstacle avoidance based on the MPC technique known in Artificial Intelligence (AI) planning and robotics. We have proposed an original method for decomposing obstacles to form a potential field, which in turn is used as an additional component in MPC. Thus, we propose a new intelligent trajectory planning method that takes into account the special shape of obstacles, which in turn significantly improves the metrics of intelligent agent movement on the well-known Moving AI benchmark. The challenging aspect of MPC planning is collision avoidance on large and complicated grid maps. We propose the Polygon Segmentation for obtaining Artificial Potential Field (PolySAP). This local planner approximates the obstacles on the map with a set of polygons. We address the question of how to partition a map with polygons to make it fast and effective for a practical MPC planner. We propose a decomposition algorithm based on Straight Skeleton. Our algorithm returns a set of polygons, which are then convexified. Numerical experiments show that our method outperforms basic algorithms in performance and provides sufficient partition quality for effective planning. We propose an artificial potential function calculated for polygonal obstacles and added to the MPC objective for collision avoidance. We evaluate our approach on city map dataset and on a real robotic platform. Numerical experiments show that PolySAP allows for polygon decomposition that is five times faster than Interior Extensions. Our MPC solver provides a fast solution for the MPC task compared to the state-of-the-art MPC planners. Our planner ensured the safe motion of the real mobile robot through a narrow indoor environment. Our code is available at.

Model predictive controlPolygon decompositionArtificial potential functionTrajectory planningMobile robots

Aleksey Logunov、Muhammad Alhaddad、Konstantin Mironov、Konstantin Yakovlev、Aleksandr Panov

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Moscow Institute of Physics and Technology, Moscow, Russia

University of Aleppo, Aleppo, Syria

Moscow Institute of Physics and Technology, Moscow, Russia||AIRI, Moscow, Russia||Ufa University of Science and Technology, Ufa, Russia

Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia

Moscow Institute of Physics and Technology, Moscow, Russia||AIRI, Moscow, Russia||Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia

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2025

10.1016/j.engappai.2025.110690

Engineering applications of artificial intelligence: The international journal of intelligent real-time automation

Engineering applications of artificial intelligence: The international journal of intelligent real-time automation

ISSN：0952-1976
年,卷(期)：2025.153(Aug.)
  • 88