In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between a manipulator and an obstacle. The distance calculations are based on Gilbert_Johnson_Keerthi algorithm, which calculates the distance between a manipulator and an obstacle by directly using their three-dimensional geometries, which also implies that our algorithm works for a manipulator and an arbitrarily shaped obstacle. Theoretical treatment proves the stability and convergence, and simulations results using an LBR IIWA seven-DOF manipulator are presented to analyze the performance of the proposed framework.

中文翻译：

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冗余机器人机械臂的避障和跟踪控制：一种基于 RNN 的元启发式方法


在本文中，我们提出了一种基于元启发式的控制框架，称为甲虫触角嗅觉循环神经网络，用于冗余机械臂的同步跟踪控制和避障。对于任何工业机械手来说，在跟踪预定义参考路径的同时避开障碍物的能力至关重要。制定的控制框架通过在目标函数中引入惩罚项，将跟踪控制和避障统一为单个约束优化问题，从而主动奖励优化器避开障碍。所提出的框架的显着特征之一是惩罚项的制定方式遵循一个简单的原则：最大化操纵器和障碍物之间的最小距离。距离计算基于 Gilbert_Johnson_Keerthi 算法，该算法直接使用机械手和障碍物的三维几何形状来计算机械手和障碍物之间的距离，这也意味着我们的算法适用于机械手和任意形状的障碍物。理论处理证明了稳定性和收敛性，并给出了使用 LBR IIWA 七自由度机械臂的仿真结果来分析所提出框架的性能。