This work examines the challenges of significant sampling randomness and path redundancy in conventional rapid expansion random tree (RRT) algorithms for robotic arm path planning. We propose a path planning technique for industrial robotic arms that is based on an enhanced RRT-Connect algorithm. The program employs global sampling optimization and incorporates an artificial potential field to actively guide sampling points into unoccupied areas, hence minimizing unproductive exploration. The attraction and repulsion coefficients can be adaptively modified simultaneously. The algorithm thereafter integrates the progressive optimality property to incrementally optimize the step size, employing larger step sizes initially for swift connectivity and smaller step sizes in later phases for enhanced optimization, thus accelerating convergence speed. At junctions, nodes sharing a common parent are prioritized to decrease directional changes and minimize path curvature. Ultimately, to assess the efficacy and practicality of the algorithm, it is juxtaposed with various classical algorithms within a simulation framework. The findings indicate that the proposed method is at least as effective as the traditional methods.