在機(jī)器人規(guī)劃中，不確定性是一個(gè)普遍存在的問(wèn)題。不確定性源主要可以分為兩類：環(huán)境不確定性和系統(tǒng)不確定性。

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

　　環(huán)境不確定性：環(huán)境不確定性是指由于環(huán)境的復(fù)雜性和變化性，導(dǎo)致機(jī)器人在執(zhí)行任務(wù)時(shí)無(wú)法完全準(zhǔn)確地獲取環(huán)境信息和響應(yīng)環(huán)境變化的能力。

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.

　　傳感器噪聲：例如，識(shí)別的障礙物或機(jī)器人的位置等。

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

　　控制擾動(dòng)：例如，無(wú)人機(jī)的風(fēng)場(chǎng)擾動(dòng)等。

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

　　未建模環(huán)境：例如，崎嶇的地形等。

Unmodeled environment: for example, rugged terrain, etc.

　　意圖：例如，動(dòng)態(tài)環(huán)境下其余智能體的未來(lái)行為等。

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

　　系統(tǒng)不確定性：系統(tǒng)不確定性則是指由于機(jī)器人自身存在的限制和不完善的模型，導(dǎo)致機(jī)器人在執(zhí)行任務(wù)時(shí)無(wú)法完全準(zhǔn)確地預(yù)測(cè)自身行為和響應(yīng)環(huán)境變化的能力。

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.

　　在機(jī)器人控制中，機(jī)器人的運(yùn)動(dòng)方程通常是非線性的，但為了方便計(jì)算，可能會(huì)使用線性模型進(jìn)行規(guī)劃和控制。這樣就可能導(dǎo)致規(guī)劃結(jié)果和實(shí)際情況不完全一致，從而影響機(jī)器人的運(yùn)動(dòng)表現(xiàn)和安全性。因此，對(duì)于非線性系統(tǒng)，建立更為準(zhǔn)確的模型是一個(gè)重要的研究方向，以便更好地處理不確定性并提高規(guī)劃的性能。

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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