Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

@en

Operations of Automated Guided Vehicles (AGVs) are desired to be more energy-efficient while maintaining high transport productivity, motivated by the green production requirements. This paper investigates a new energy-efficient planning problem for determining conflict-free paths of the AGVs in its transport roadmap. In this problem, the vehicle path and transport time in the roadmap are jointly optimized, based on a flexible time–space network (FTSN). We provide the mathematical problem formulation of the energy-efficient path planning problem. The resulting optimization problem is proved to be a non-convex mixed-integer nonlinear programming which is computationally intractable. We further propose a hybrid metaheuristic that integrates the genetic algorithm and estimation of the distribution algorithm to improve its computational efficiency. Numerical results show the effectiveness of the developed algorithm based on the FTSN framework, compared to the existing metaheuristics, the conventional path planning method, and a commercial solver. The proposed method has a wide application in improving energy use of material handling, providing a guiding significance on promoting cleaner production of flexible manufacturing systems.

@en

Existing motion planning algorithms for multi-robot systems must be improved to address poor coordination and increase low real-time performance. This paper proposes a new distributed real-time motion planning method for a multi-robot system using Model Predictive Contouring Control (MPCC). MPCC allows separating the tracking accuracy and productivity, to improve productivity better than the traditional Model Predictive Control (MPC) which follows a time-dependent reference. In the proposed distributed MPCC, each robot exchanges the predicted paths of the other robots and generates the collision-free motion in a parallel manner. The proposed distributed MPCC method is tested in industrial operation scenarios in the robot simulation platform Gazebo. The simulation results show that the proposed distributed MPCC method realizes real-time multi-robot motion planning and performs better than three commonly-used planning methods (dynamic window approach, MPC, and prioritized planning).@en