Centralized MPC-Based Mixed-Integer Programming for Cooperative Trajectory Planning in Open-Pit Mines

The rapid evolution of artificial intelligence and Internet of Things (IoT) technology is making autonomous driving an emerging critical trend in open-pit mining operations. However, current systems still suffer from heavy reliance on manual safety interventions, as well as limitations in inertial c...

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Vydáno v:	IEEE internet of things journal Ročník 12; číslo 23; s. 51064 - 51076
Hlavní autoři:	Zhu, Desheng, Huang, Zhipeng, Xiong, Yijin, Wang, Chunhui, Yang, Kehu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accidents Artificial intelligence Autonomous mining vehicle Autonomous vehicles Collision avoidance Conflict resolution Constraints Data mining Driving Dynamic models Freezing Integer programming Internet of Things intersection management Linear programming Loading Mines Mixed integer model predictive control (MPC) nonlinear optimization method Open pit mining Pits (excavations) Planning Predictive control Real time Real-time systems Roads Safety Smoothness Speed limits Strategy Trajectory planning
ISSN:	2327-4662, 2327-4662
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Abstract	The rapid evolution of artificial intelligence and Internet of Things (IoT) technology is making autonomous driving an emerging critical trend in open-pit mining operations. However, current systems still suffer from heavy reliance on manual safety interventions, as well as limitations in inertial control of large vehicles and responses to uncertain environments. To address these issues, this study develops an autonomous driving planning framework based on model predictive control-mixed-integer programming (MPC-MIP). First, it defines three conflict modes for two key conflict areas in open-pit mines-intersections and overlapping sections. By encoding passage order with binary variables and integrating a collision detection algorithm via elliptical contour fitting, conflict resolution conditions are constructed. Second, leveraging a spatial-domain dynamic model and a discretized multivehicle path representation, an objective function for multivehicle cooperative trajectory planning is established, considering total passage time, speed limit loss, and motion smoothness. Conflict resolution conditions are embedded as constraints, forming an MIP model that optimizes both mining trucks' passage order and speed. Finally, an online solution method based on MPC is proposed, incorporating an integer constraint-freezing strategy for state evolution and an accelerated computing strategy to enable real-time multitruck cooperative trajectory planning. Simulations show that in a 12-truck scenario, compared with existing methods, this method has a 10.68% optimality loss rate and online planning time within 500 ms, enhancing efficiency and reducing complexity. Laboratory vehicle tests confirm 100% collision-free performance in typical loading cycle tasks, even with 5% positioning error interference.
AbstractList	The rapid evolution of artificial intelligence and Internet of Things (IoT) technology is making autonomous driving an emerging critical trend in open-pit mining operations. However, current systems still suffer from heavy reliance on manual safety interventions, as well as limitations in inertial control of large vehicles and responses to uncertain environments. To address these issues, this study develops an autonomous driving planning framework based on model predictive control-mixed-integer programming (MPC-MIP). First, it defines three conflict modes for two key conflict areas in open-pit mines-intersections and overlapping sections. By encoding passage order with binary variables and integrating a collision detection algorithm via elliptical contour fitting, conflict resolution conditions are constructed. Second, leveraging a spatial-domain dynamic model and a discretized multivehicle path representation, an objective function for multivehicle cooperative trajectory planning is established, considering total passage time, speed limit loss, and motion smoothness. Conflict resolution conditions are embedded as constraints, forming an MIP model that optimizes both mining trucks' passage order and speed. Finally, an online solution method based on MPC is proposed, incorporating an integer constraint-freezing strategy for state evolution and an accelerated computing strategy to enable real-time multitruck cooperative trajectory planning. Simulations show that in a 12-truck scenario, compared with existing methods, this method has a 10.68% optimality loss rate and online planning time within 500 ms, enhancing efficiency and reducing complexity. Laboratory vehicle tests confirm 100% collision-free performance in typical loading cycle tasks, even with 5% positioning error interference.
Author	Zhu, Desheng Huang, Zhipeng Wang, Chunhui Yang, Kehu Xiong, Yijin
Author_xml	– sequence: 1 givenname: Desheng orcidid: 0000-0001-6524-7600 surname: Zhu fullname: Zhu, Desheng organization: School of Artificial Intelligence, China University of Mining and Technology, Beijing, China – sequence: 2 givenname: Zhipeng surname: Huang fullname: Huang, Zhipeng organization: School of Artificial Intelligence, China University of Mining and Technology, Beijing, China – sequence: 3 givenname: Yijin orcidid: 0000-0002-9733-6875 surname: Xiong fullname: Xiong, Yijin organization: School of Vehicle and Mobility and the State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China – sequence: 4 givenname: Chunhui orcidid: 0009-0004-3967-8799 surname: Wang fullname: Wang, Chunhui organization: School of Artificial Intelligence, China University of Mining and Technology, Beijing, China – sequence: 5 givenname: Kehu orcidid: 0000-0001-9163-2713 surname: Yang fullname: Yang, Kehu email: ykh@cumtb.edu.cn organization: School of Artificial Intelligence, China University of Mining and Technology, Beijing, China
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SubjectTerms	Accidents Artificial intelligence Autonomous mining vehicle Autonomous vehicles Collision avoidance Conflict resolution Constraints Data mining Driving Dynamic models Freezing Integer programming Internet of Things intersection management Linear programming Loading Mines Mixed integer model predictive control (MPC) nonlinear optimization method Open pit mining Pits (excavations) Planning Predictive control Real time Real-time systems Roads Safety Smoothness Speed limits Strategy Trajectory planning
Title	Centralized MPC-Based Mixed-Integer Programming for Cooperative Trajectory Planning in Open-Pit Mines
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