Juang Chia-Feng, Chou Ching-Yu, Lin Chin-Teng
IEEE Trans Cybern. 2022 Aug;52(8):7388-7401. doi: 10.1109/TCYB.2020.3041269. Epub 2022 Jul 19.
This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.
本文提出了一种适用于未知环境中轮式机器人的导航方案。该导航方案由沿障碍物边界跟踪（OBF）、目标搜索（TS）、顶点搜索（VPS）行为以及一个行为监督器组成。OBF行为通过模糊控制器（FC）实现。本文将FC设计问题表述为一个新的约束多目标优化问题，并通过结合专家知识和数据驱动的多目标蚁群优化来找到一组非支配FC解。TS行为通过分别控制机器人方向和速度的新型模糊比例积分微分（PID）控制器和比例微分（PD）控制器实现。VPS行为旨在通过控制机器人朝着从障碍物顶点确定的新子目标移动来缩短导航路线。提出了一种在未知环境中管理OBF、TS和VPS行为之间切换的新型行为监督器。在实际机器人导航中，提出了一种通过使用粒子滤波器融合编码器和红外定位传感器的新型机器人定位方法。最后，本文通过仿真和实验验证了该导航方案的可行性和优势。
