Memory-saving memetic computing for path-following mobile robots
Memory-saving memetic computing for path-following mobile robots
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DOI：
10.1016/j.asoc.2012.11.039
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Giovanni Iacca;Fabio Caraffini;Ferrante Neri
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文献类型：
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作者：
Giovanni Iacca;Fabio Caraffini;Ferrante Neri
本文采用最近提出的单解模因计算优化方法，即三阶段优化模因探索（3SOME），实现移动机器人板载自整定PID控制器。更具体地说，在控制回路的执行过程中，实时地找到最优PID参数，使路径跟踪操作中的跟踪误差最小化。该方法分离了控制任务和优化任务，并使用简单的操作系统原语来共享数据。该系统能够对轨迹的修改做出反应，从而赋予机器人智能学习和自配置能力。一个流行的商业机器人工具，即乐高头脑风暴机器人，已被用于测试和实施该系统。在模拟和真实的乐高机器人中都进行了测试。实验结果表明，与其他在线优化技术和经验PID整定程序相比，3SOME保证了鲁棒和有效的控制行为，从而代表了自整定控制系统的有效替代方案。
In this paper, a recently proposed single-solution memetic computing optimization method, namely three stage optimization memetic exploration (3SOME), is used to implement a self-tuning PID controller on board of a mobile robot. More specifically, the optimal PID parameters minimizing a measure of the following error on a path-following operation are found, in real-time, during the execution of the control loop. The proposed approach separates the control and the optimization tasks, and uses simple operating system primitives to share data. The system is able to react to modifications of the trajectory, thus endowing the robot with intelligent learning and self-configuration capabilities. A popular commercial robotic tool, i.e. the Lego Mindstorms robot, has been used for testing and implementing this system. Tests have been performed both in simulations and in a real Lego robot. Experimental results show that, compared to other online optimization techniques and to empiric PID tuning procedures, 3SOME guarantees a robust and efficient control behaviour, thus representing a valid alternative for self-tuning control systems.