Multi-objective optimal trajectory planning method for robot manipulator with real-time parameters monitoring

The robot manipulator of the lunar rover is a crucial device in lunar exploration missions, capable of performing sampling, experimental operations, and environmental analysis. To meet different task requirements, optimal trajectory planning is essential, and this planning relies on an accurate kinematic model. In the harsh environment of space, the kinematic parameters of the robot manipulator can change due to noise and structural damage, affecting the accuracy of trajectory planning. To address this, an energy-time-jerk optimal trajectory planning method for the robot manipulator with real-time parameter monitoring is proposed. The Sequential Quadratic Programming (SQP) algorithm is utilized for trajectory planning. Building on this, a new algorithm that combines the Extended Kalman Filter (EKF) and SQP algorithm (EKF-SQP) is introduced. Simulation results demonstrate that the proposed algorithm significantly improves the accuracy of the robot manipulator’s trajectory planning. Compared to existing methods, the integration of real-time parameter identification and compensation enhances precision by effectively reducing position errors of the end joint. By continuously updating the kinematic parameters in real-time, the algorithm ensures that the trajectory is dynamically re-planned, allowing the robot manipulator to reach the target position with higher accuracy.