当机器人的运动存在过程噪声,或其携带的闭环反馈传感器存在观测误差时,机器人的运动就会呈现出显著的非确定性。以自然界最为普遍的高斯分布描述系统运动状态的非确定性。用概率论的方法结合机器人本身的线性控制及卡尔曼滤波对机器人可行轨迹进行规划和先验概率的评估,从而得到机器人先验估计概率。采用线性控制方法和卡尔曼滤波相结合,进行高斯运动系统误差建模;然后用高斯运动模型对可行轨迹进行评估,能够计算出轨迹避开障碍和到达目标点的概率。为了进行最优轨迹规划,通过样条化方法计算出一组可行轨迹。理论上,这些轨迹本身都能够达到目标点,并避开障碍,但由于机器人行为的非确定性,机器人仍然存在碰撞和难以达到目标点的可能。通. ; When the robot’s movement has process noise, or its closed-loop feedback sensors have specific observation errors, the robot will present significant uncertain movement. The non-deterministic movement state is discribed by the Gaussian distribution which is widespread in nature. The probability theory combing with the robot’s linear control and Kalman filter estimation is used to plan the trajectory and evaluate the Apriori probability distribution. Linear control method is used in combination with Kalman filter to establish error model of Gaussian motion system. Then, all feasible trajectories are assessed by the Gaussian motion model by calculating the probability of avoiding obstacles and arriving at the target. For the optimal trajectory planning, spline method is used to calculate a set of feasible path. Theoretically, all those trajectories can get the aim point and avoid the obstacles. But for the uncertainty of the robot’s behavior, the robot still has the probability of collision and miss the target. Through Gaussian movement prior probability estimates, the trajectory with the maximum probability value is the optimal one under the non-deterministic Gaussian motion state of the robot.