Trajectory planning of seven-degree-of-freedom redundant manipulator in narrow space

Traditional six-degree-of-freedom manipulators struggle with obstacle avoidance and smooth target point arrival. This paper focuses on trajectory planning for a seven-degree-of-freedom redundant manipulator, introducing a trajectory optimization algorithm based on improved RRT* (I-RRT*) and cubic spline smoothing. The I-RRT* algorithm addresses the randomness in search tree expansion and enhances target orientation through an adaptive growth strategy and mixed sampling. In the experimental section, the I-RRT* method is compared to APF-RRT*, APF-RRT, RRT*, and RRT algorithms. Results demonstrate that I-RRT* outperforms these methods in path length, time efficiency, and overall optimization. In addition, the manipulator’s ability to safely and successfully reach target points in narrow spaces is confirmed. Overall, this study enhances the trajectory planning capabilities of seven-degree-of-freedom redundant manipulators in hazardous environments, enabling flexible navigation, effective obstacle avoidance, and precise target arrival.