Model Predictive collision-free path following control for nonholonomic mobile robots

IF 1.2 Q3 ENGINEERING, MECHANICAL
T. Hiep, V. Cong, L. Phuong
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引用次数: 0

Abstract

In this research, a model predictive collision-free path following controller is developed and applied for an omnidirectional mobile robot (OMR). The mobile robot is controlled to track a reference path while avoiding collision with obstacles. The path-following problem is reformulated into the regulation problem of an extended plant by introducing a virtual degree of freedom, the path parameter of a geometric reference curve. Then a Model Predictive Controller (MPC) is then applied to steer the mobile robot. The optimization cost function is established from the difference between the state of the robot and the parameter path. The solution of MPC can be obtained by repeatedly solving an optimal control problem (OCP) to reduce the optimization cost function to a minimum value, making the robot state as close to the state of the path as possible. Obstacle avoidance is considered by adding terms as a function of the gap between the mobile robot and the objects in front of the robot. Constraints on the states and inputs of the system are also easily considered in the optimal control problem of MPC. This makes the control inputs not exceed the allowable limits of the robot. Simulations are carried out to reveal the controller's efficiency and show how to choose the right parameters to synchronize path tracking and obstacle avoidance tasks.
非完整移动机器人预测无碰撞路径跟踪控制
研究开发了一种模型预测无碰撞路径跟踪控制器,并将其应用于全向移动机器人。控制移动机器人跟踪参考路径,同时避免与障碍物碰撞。通过引入虚拟自由度,即几何参考曲线的路径参数,将路径跟踪问题转化为扩展对象的调节问题。然后应用模型预测控制器(MPC)对移动机器人进行控制。根据机器人状态与参数路径的差值建立优化代价函数。MPC的解可以通过反复求解最优控制问题(OCP)来得到,使优化代价函数减小到最小值,使机器人状态尽可能接近路径状态。通过添加术语作为移动机器人与前方物体之间距离的函数来考虑避障。在MPC的最优控制问题中,对系统状态和输入的约束也很容易被考虑。这使得控制输入不超过机器人的允许极限。仿真结果显示了该控制器的有效性,并展示了如何选择合适的参数来同步路径跟踪和避障任务。
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来源期刊
FME Transactions
FME Transactions ENGINEERING, MECHANICAL-
CiteScore
3.60
自引率
31.20%
发文量
24
审稿时长
12 weeks
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