在机器人规划中，不确定性是一个普遍存在的问题。不确定性源主要可以分为两类：环境不确定性和系统不确定性。

Uncertainty is a common problem in robot planning. The sources of uncertainty can be mainly divided into two categories: environmental uncertainty and system uncertainty.

　　环境不确定性：环境不确定性是指由于环境的复杂性和变化性，导致机器人在执行任务时无法完全准确地获取环境信息和响应环境变化的能力。

Environmental uncertainty: Environmental uncertainty refers to the ability of robots to accurately obtain environmental information and respond to environmental changes during task execution due to the complexity and variability of the environment.

　　传感器噪声：例如，识别的障碍物或机器人的位置等。

Sensor noise: For example, recognized obstacles or the position of robots.

　　控制扰动：例如，无人机的风场扰动等。

Control disturbances, such as wind field disturbances caused by drones.

　　未建模环境：例如，崎岖的地形等。

Unmodeled environment: for example, rugged terrain, etc.

　　意图：例如，动态环境下其余智能体的未来行为等。

Intention: For example, the future behavior of other intelligent agents in a dynamic environment.

　　系统不确定性：系统不确定性则是指由于机器人自身存在的限制和不完善的模型，导致机器人在执行任务时无法完全准确地预测自身行为和响应环境变化的能力。

System uncertainty: System uncertainty refers to the inability of robots to accurately predict their behavior and respond to environmental changes during task execution due to their own limitations and imperfect models.

　　在机器人控制中，机器人的运动方程通常是非线性的，但为了方便计算，可能会使用线性模型进行规划和控制。这样就可能导致规划结果和实际情况不完全一致，从而影响机器人的运动表现和性。因此，对于非线性系统，建立更为准确的模型是一个重要的研究方向，以便更好地处理不确定性并提高规划的性能。

In robot control, the motion equations of robots are usually nonlinear, but for the convenience of calculation, linear models may be used for planning and control. This may lead to inconsistencies between the planning results and the actual situation, thereby affecting the motion performance and safety of the robot. Therefore, for nonlinear systems, establishing more accurate models is an important research direction to better handle uncertainty and improve planning performance.

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