Purpose - Uncertainty can arise for a manipulator because its motion can deviate unpredictably from the assumed dynamical model and because sensors might provide information regarding the system state that is imperfect because of noise and imprecise measurement. This paper aims to propose a method to estimate the probable error ranges of the entire trajectory for a manipulator with motion and sensor uncertainties. The aims are to evaluate whether a manipulator can safely avoid all obstacles under uncertain conditions and to determine the probability that the end effector arrives at its goal area. Design/methodology/approach - An effective, analytical method is presented to evaluate the trajectory error correctly, and a motion plan was executed using Gaussian models by considering sensor and motion uncertainties. The method used an integrated algorithm that combined a Gaussian error model with an extended Kalman filter and a linear- quadratic regulator. Iterative linearization of the nonlinear dynamics was used around every section of the trajectory to derive all of the prior probability distributions before execution. Findings - Simulation and experimental results indicate that the proposed trajectory planning method based on the motion and sensor uncertainties is indeed highly convenient and efficient. Originality/value - The proposed approach is applicable to manipulators with motion and sensor uncertainties. It helps determine the error distribution of the predefined trajectory. Based on the evaluation results, the most appropriate trajectory can be selected among many predefined trajectories according to the error ranges and the probability of arriving at the goal area. The method has been successfully applied to a manipulator operating on the Chinese Space Station.