What some of the terms are, some of the needed algorithms, and what makes this problem difficult in certain environments.

一些術語是什麼，一些需要的算法，以及在某些環境中這個問題的難點。

So that's what we're going to cover over a few videos.

是以，我們將通過幾段視頻來介紹這一點。

But in this first one, I want to set the stage a bit and just introduce the idea of autonomous navigation.

但在第一部分，我想先介紹一下自主導航的概念。

I think it's pretty interesting, so I hope you stick around for it.

我覺得這很有趣，希望你們能繼續關注。

I'm Brian, and welcome to a MATLAB Tech Talk.

我是 Brian，歡迎來到 MATLAB 技術講座。

Navigation is the ability to determine your location within an environment and to be able to figure out a path that will take you from your current location to some goal.

導航是指確定自己在某一環境中的位置，並找出從當前位置到某一目標的路徑的能力。

Navigating in the wilderness might require, say, GPS to determine where you are, and a map to plan the best path to get around mountains and lakes to reach your campsite, or whatever your goal is.

例如，在野外導航可能需要 GPS 來確定您的位置，還需要地圖來規劃最佳路徑，以便繞過高山和湖泊到達露營地或您的目標。

Now, autonomous navigation is doing exactly this, but without a human in the loop.

現在，自動導航正是在這樣做，但不需要人類參與。

Broadly speaking, it's how we get a vehicle to determine its location using a set of sensors, and then to move on its own through an environment to reach a desired goal.

廣義上講，這就是我們如何讓車輛通過一組傳感器確定自己的位置，然後在環境中自行移動，以達到預期目標。

And when I say vehicle, I mean any kind of mobile machine.

我說的車輛，是指任何類型的移動機器。

It could be a car traveling down a road, or a UAV making its way back to the airport, or a spacecraft journeying across the solar system, or a submersible exploring the depths of the ocean, or some other mobile robot.

它可以是行駛在公路上的汽車，也可以是返回機場的無人駕駛飛行器，還可以是穿越太陽系的宇宙飛船、探索海洋深處的潛水器或其他移動機器人。

In this way, we've given the vehicle autonomy, the ability to make decisions and act on its own.

通過這種方式，我們賦予了車輛自主權，使其能夠自主決策和行動。

But there are different levels of autonomy, and they range from a vehicle that is simply operated by a human from a remote location, but it has some simple algorithms on board that will take over and autonomously keep it from running off a cliff or something, all the way up to a fully autonomous vehicle with no human interaction at all.

但是，自動駕駛有不同的級別，從僅由人類在遠程位置操作的車輛，但車上有一些簡單的算法，這些算法會接管並自動防止車輛衝下懸崖或其他東西，一直到完全自動駕駛的車輛，完全不需要人類的互動。

Now, for this series, we're mostly going to focus on what it takes to make a fully autonomous vehicle.

現在，在這個系列中，我們將主要關注製造一輛完全自動駕駛汽車所需的條件。

This is because there's a lot more involved in it, and we can apply that knowledge to other vehicles that fall elsewhere on this autonomy spectrum.

這是因為其中涉及的內容更多，我們可以將這些知識應用到其他自動駕駛汽車上。

But even with fully autonomous navigation, we can further divide this into two different approaches.

但即使是完全自主導航，我們也可以進一步將其分為兩種不同的方法。

A heuristic approach, where autonomy is accomplished through a set of practical rules or behaviors.

一種啟發式方法，通過一套實用規則或行為來實現自主。

This doesn't guarantee an optimal result, but it's good enough to achieve some immediate goal.

這並不能保證達到最佳效果，但足以實現一些近期目標。

The benefit of heuristics is that you don't need complete information about the environment to accomplish autonomy.

啟發式方法的好處在於，不需要完整的環境資訊就能實現自主。

And then on the other side, there is an optimal approach, which typically requires more knowledge of the environment.

另一方面，還有一種最佳方法，通常需要更多的環境知識。

And then a plan and resulting actions comes from maximization or minimization of an objective function.

然後，通過目標函數的最大化或最小化來制定計劃和行動。

So let's go into a little bit more detail into each of these.

下面，讓我們逐一詳細介紹。

An example of a heuristic approach is a maze solving vehicle where the simple rules might be to drive forward and keep the wall on the left.

啟發式方法的一個例子是迷宮解謎車，其簡單規則可能是向前行駛並保持左邊的牆壁。

So it turns left when the wall turns left, makes a U-turn at the end of a wall, and it turns right at a corner.

是以，當牆向左轉時，它會向左轉；在牆的盡頭，它會掉頭；在拐角處，它會向右轉。

This type of autonomous vehicle will proceed to wander up and down the hallways until it happens to reach the goal.

這種自動駕駛汽車會在走廊上來回穿梭，直到碰巧到達目標。

So in this way, the vehicle doesn't have to maintain a map of the maze or even know that it's in a maze in order to find the end.

是以，這樣一來，車輛就不需要維護迷宮地圖，甚至不需要知道自己在迷宮中，就能找到終點。

It follows an optimal path, but it works, at least as long as it doesn't get itself stuck in a loop.

它遵循的是一條最佳路徑，但至少只要不陷入循環，它就能工作。

Other types of heuristic-based autonomy include things like the simplest of robotic vacuums, where when it approaches an obstacle like a wall, it rotates to a new random angle and just keeps going.

其他類型的啟發式自主包括最簡單的機器人吸塵器，當它接近牆壁等障礙物時，會隨機旋轉到一個新的角度，然後繼續前進。

And as time increases, the chance that the entire floor is covered approaches 100%.

隨著時間的推移，整個地板被覆蓋的機率接近 100%。

And so in the end, the goal of having a clean floor is met, even if the vehicle doesn't take the optimal path to achieve it.

是以，即使車輛沒有采取最佳路徑來實現這一目標，但最終還是達到了清潔地面的目的。

So this brings us to the second type of fully autonomous vehicles, ones that are solving an optimization problem.

這就引出了第二類完全自動駕駛汽車，即解決優化問題的汽車。

In these systems, the vehicle builds a model of the environment, or it updates a model that was given to it, and then it figures out an optimal path to reach the produce a much better result than their heuristic-based counterparts.

在這些系統中，車輛會建立一個環境模型，或者更新給定的模型，然後找出一條最佳路徑，以達到比啟發式系統更好的效果。

Possibly the most famous at the moment is autonomous driving, where a vehicle has to navigate to a destination through dynamic and chaotic streets, and relying on simple behaviors like drive forward and keep the curb to your right is probably not the best approach to safely and quickly get to where you want to go.

目前最著名的可能是自動駕駛，在自動駕駛中，車輛必須在動態和混亂的街道上導航到目的地，而依靠簡單的行為，如向前行駛和保持右側路邊等，可能並不是安全快速到達目的地的最佳方法。

It makes more sense to give the vehicle the ability to if that model is imperfect, and then use it to determine an optimal solution.

更有意義的做法是，如果該模型不完善，則讓車輛有能力利用它來確定最佳解決方案。

Now, it's not usually the case where a solution is either 100% heuristic or 100% optimal.

現在，通常情況下，解決方案不是 100% 的啟發式，就是 100% 的最優化。

Often we can use both approaches to achieve a larger goal.

通常情況下，我們可以同時使用這兩種方法來實現更大的目標。

For example, with an autonomous car, when it approaches a slower car, it has to make a decision to either slow down or to change lanes and pass.

例如，對於自動駕駛汽車來說，當它接近一輛速度較慢的汽車時，它必須做出決定，要麼減速，要麼變道超車。

Now, if it was going to make this decision optimally, it would have to have knowledge beyond the front car to determine if changing lanes is the best solution, and that can be difficult to obtain.

現在，如果它要以最佳方式做出這一決定，就必須掌握前車以外的知識，以確定變更車道是否是最佳解決方案，而這可能很難獲得。

So possibly a better solution is to have a heuristic behavior that says something like, if it's safe to do so, always attempt to pass slower cars.

是以，更好的解決方案可能是採用啟發式行為，比如在安全的情況下，總是嘗試超越速度較慢的車輛。

And once that decision is made, an optimal path to the adjacent lane can be created.

一旦做出決定，就可以創建一條通往相鄰車道的最佳路徑。

And in this way, these two approaches can complement each other depending on the situation.

這樣，這兩種方法就可以根據具體情況相互補充。

Autonomous cars aren't the only examples of systems that make use of these two approaches.

自動駕駛汽車並不是利用這兩種方法的唯一系統實例。

There are other ground vehicles like they have in Amazon warehouses that have to quickly maneuver to a given storage area to move packages around while not running into other mobile vehicles and stationary shelves.

還有其他地面車輛，就像亞馬遜倉庫裡的車輛一樣，它們必須快速移動到指定的存儲區域，搬運包裹，同時又不能碰到其他移動車輛和固定貨架。

Or vehicles that search within disaster areas that have to navigate unknown and hazardous terrain.

或者在災區搜索的車輛，必須在未知和危險的地形中穿行。

There are space missions like OSIRIS-REx which has to navigate around the previously unvisited asteroid Bennu and prepare for a located touch and go to collect a sample to return to Earth.

有一些太空任務，比如 OSIRIS-REx，它必須繞著之前未曾訪問過的小行星貝努（Bennu）航行，並準備進行定位接觸，收集樣本返回地球。

There are robotic arms and manipulators that navigate within their local space to pick things up and move them to new locations.

機械臂和機械手可以在在地空間內導航，拾取物品並將其移動到新的位置。

There's UAVs and drones that survey areas.

有無人駕駛飛行器和無人機勘測區域。

And many, many more applications.

還有更多更多的應用。

But autonomous navigation isn't necessarily easy, despite how common it's becoming in the world.

但是，自動導航並不一定容易，儘管它在世界上已經變得非常普遍。

And most of what makes it difficult is that the vehicle has to navigate through an environment that isn't perfectly known.

而造成困難的主要原因是，車輛必須在並不完全瞭解的環境中航行。

And so in order to create a plan, it has to build up a model of the environment over time.

是以，為了制定計劃，它必須建立一個長期的環境模型。

And the environment is constantly changing, and so the model has to be constantly updated.

環境在不斷變化，是以模型也必須不斷更新。

And then there's obstacles that move around and aren't necessarily obvious, so sensing and recognizing them is difficult as well.

還有一些障礙物會四處移動，不一定很明顯，是以感知和識別它們也很困難。

And the more uncertainty there is in the environment and the environment model, the harder the navigation problem becomes.

環境和環境模型的不確定性越大，導航問題就越難解決。

For example, building an autonomous spacecraft that's orbiting the Earth is typically a simpler navigation problem than an autonomous aircraft, at least in terms of environment complexity.

例如，與自主飛行器相比，至少就環境複雜性而言，建造繞地球軌道飛行的自主航天器通常是一個更簡單的導航問題。

Space is a more predictable environment than air because we have less uncertainty with the forces that act on the vehicle, and we have more certainty in the tracks that other nearby objects are on.

太空環境比空中環境更容易預測，因為我們對作用在飛行器上的力的不確定性更小，而且我們對附近其他物體所處的軌道也更有把握。

Therefore, we can have more confidence in a plan, and then have a better expectation that the spacecraft will autonomously be able to follow that plan.

是以，我們可以對計劃更有信心，進而對航天器能夠自主執行該計劃抱有更大的期望。

With aircraft, we have to deal with unknown turbulence, and flocks of birds flying around, and other human-controlled planes, and landing, and taxiing around an airport.

對於飛機來說，我們必須應對未知的亂流、成群飛來飛去的鳥兒、其他由人類控制的飛機、降落以及在機場周圍滑行。

But an autonomous aircraft is itself typically a simpler problem than an autonomous car, for the same reasons.

但與自動駕駛汽車相比，自動駕駛飛機本身通常是一個更簡單的問題，原因也是如此。

There's much more uncertainty driving around in a city than there is flying around in relatively open air.

在城市裡開車的不確定性要比在相對開闊的空中飛行的不確定性大得多。

So the thing I want to stress here is that what makes these vehicles impressive is not the fact that they can move on their own.

是以，我想在這裡強調的是，這些車輛之所以令人印象深刻，並不在於它們能夠自行移動。

I mean, it's pretty trivial to get a car to drive forward by itself.

我的意思是，讓一輛汽車自己向前行駛是一件非常瑣碎的事情。

You just need an actuator that compresses the gas pedal.

您只需要一個壓縮油門踏板的執行器。

The car will take off and drive forward.

汽車將起飛並向前行駛。

The difficult part is getting it to navigate autonomously, within an uncertain and changing environment.

困難的是讓它在不確定和不斷變化的環境中自主導航。

For the car, it's to get to the destination efficiently, and to follow local traffic laws, and to avoid potholes and balls rolling into the street, and to reroute around construction, and to avoid other cars driven by unpredictable humans, and to do all of this in the snow, and in the rain, and so on.

對於汽車來說，它需要高效地到達目的地，遵守當地的交通法規，避開坑窪不平的路面和滾到馬路上的球，繞過施工路段，避開其他由不可預知的人類駕駛的汽車，在雨雪天氣中完成所有這些任務，等等。

It's not an easy feat.

這並非易事。

So, to understand how we get vehicles to do that, and other incredible autonomous tasks, we need to revisit the capabilities of autonomous systems that we covered in the first video of the Sensor Fusion and Tracking series.

是以，要了解如何讓車輛完成這些任務以及其他令人難以置信的自主任務，我們需要重溫一下 "傳感器融合與跟蹤 "系列第一個視頻中介紹的自主系統的能力。

And if you haven't seen it and want a longer description, I've left a link below.

如果你還沒看過，想了解更詳細的介紹，我在下面留下了鏈接。

But here's a quick recap.

不過，這裡要簡單回顧一下。

Autonomous systems need to interact with the sensors.

自主系統需要與傳感器互動。

This sensor data has to be interpreted into something that is more useful than just measured quantities.

這些傳感器數據必須解釋成比測量值更有用的東西。

These are things like understanding where other objects and obstacles are, and building a model or a map of the environment, and understanding the state of the autonomous vehicle itself, what its location is and orientation.

這包括瞭解其他物體和障礙物的位置，建立環境模型或地圖，以及瞭解自動駕駛汽車本身的狀態、位置和方向。

And with this information, the vehicle has everything it needs to plan a path from the current location to the goal, avoiding obstacles and other objects along the way.

有了這些資訊，車輛就有了規劃從當前位置到目標的路徑所需的一切，並能避開沿途的障礙物和其他物體。

And then the last step is to act on that plan, to drive the motors and the actuators in such a way that the vehicle follows the path.

最後一步是根據計劃採取行動，驅動電機和執行器，使車輛按照路徑行駛。

The actuators impact the physical world, and the whole loop continues.

執行器對物理世界產生影響，整個循環繼續進行。

We sense the environment, we understand where we are in relation to landmarks in the environment, we perceive and to follow that plan, and so on until we get to the goal.

我們感知環境，瞭解自己所處的位置與環境中的地標之間的關係，感知並按照計劃行事，如此循環往復，直至達到目標。

Now, in the Sensor Fusion video, we talked about how Sensor Fusion and Tracking straddled the Sense and Perceive steps.

在 "傳感器融合 "視頻中，我們談到了 "傳感器融合 "和 "跟蹤 "是如何跨越 "感知 "和 "認知 "兩個步驟的。

And while Sensor Fusion and Tracking are absolutely necessary parts of autonomous navigation, in this series, we're going to focus our attention on other algorithms within the Perceive step, and on the Planning step.

雖然 "傳感器融合 "和 "跟蹤 "絕對是自主導航的必要組成部分，但在本系列中，我們將重點關注 "感知 "步驟和 "規劃 "步驟中的其他算法。

And we're going to answer questions like, what does it mean to How does a vehicle know where it is within that model?

我們要回答的問題包括："車輛如何知道自己在該模型中的位置？

How does a vehicle track other large objects and obstacles?

車輛如何跟蹤其他大型物體和障礙物？

What are some of the ways path planning is accomplished?

實現路徑規劃的方法有哪些？

And then how do you know that the system is going to work in the end?

那你怎麼知道這個系統最終會起作用呢？

So that's what you have to look forward to.

這就是你所期待的。

In the next video, we're going to explore how a vehicle can determine its location within an environment model using a particle filter and Monte Carlo localization.

在下一個視頻中，我們將探索車輛如何利用粒子濾波器和蒙特卡洛定位來確定其在環境模型中的位置。

So if you don't want to miss that, or any other future tech talk videos, don't forget to subscribe to this channel.

所以，如果你不想錯過這段視頻，或者以後的任何科技講座視頻，別忘了訂閱本頻道。

And if you want to check out my channel, Control System Lectures, I cover more control theory topics there as well.

如果你想查看我的頻道 "控制系統講座"，我還會在那裡介紹更多控制理論話題。

Thanks for watching, and I'll see you next time.

感謝您的收看，我們下次再見。