DAX 自動(dòng)化提供來(lái)自包括科爾摩根在內(nèi)的眾多制造商的數(shù)千種 CNC、伺服電機(jī)和驅(qū)動(dòng)部件，以提供圓形和線性定位精度和速度控制，以滿足您的特定應(yīng)用。

科爾摩根AKM伺服電機(jī)擁有廣泛的標(biāo)準(zhǔn)產(chǎn)品，為您提供了選擇和靈活性 – 這些產(chǎn)品經(jīng)過(guò)優(yōu)化與科爾摩根伺服驅(qū)動(dòng)器配合使用，可提供卓越的性能以滿足您的應(yīng)用需求。

科爾摩根的無(wú)刷伺服電機(jī)產(chǎn)品在業(yè)界市場(chǎng)有出色的性能及靈活性。基于AKM系列標(biāo)準(zhǔn)伺服電機(jī)，科爾摩根為低溫應(yīng)用場(chǎng)合研發(fā)了耐低溫伺服電機(jī)，適應(yīng)低至-40ºC的極寒環(huán)境使用。

 

機(jī)器人已經(jīng)比許多人意識(shí)到的要廣泛得多。它們現(xiàn)在對(duì)許多行業(yè)至關(guān)重要，為創(chuàng)新帶來(lái)了新的機(jī)遇和途徑。從機(jī)器人中獲益多的行業(yè)之一是交通運(yùn)輸。

 

在制造業(yè)之外，機(jī)器人技術(shù)在交通運(yùn)輸中的應(yīng)用是一個(gè)相對(duì)較新的現(xiàn)象。盡管相對(duì)新穎，但機(jī)器人已經(jīng)在運(yùn)輸行業(yè)取得了長(zhǎng)足的進(jìn)步，推動(dòng)了多個(gè)子行業(yè)和應(yīng)用的創(chuàng)新。沒(méi)有機(jī)器人，現(xiàn)代交通就不一樣了，未來(lái)只會(huì)鞏固這一點(diǎn)。

 

以下是機(jī)器人如何推動(dòng)運(yùn)輸業(yè)向前發(fā)展，以及它們可以從這里走向何方。

自動(dòng)駕駛汽車

機(jī)器人推動(dòng)交通創(chuàng)新的標(biāo)志性的例子可能是自動(dòng)駕駛汽車。雖然全自動(dòng)駕駛汽車尚未成為現(xiàn)實(shí)，但自動(dòng)化功能已經(jīng)為當(dāng)今的汽車帶來(lái)了重大改進(jìn)。自動(dòng)制動(dòng)、車道修正和自適應(yīng)巡航控制都是當(dāng)今車輛中機(jī)器人控制的例子。

 

自動(dòng)緊急制動(dòng)已將配備它的汽車的后部碰撞減少了 50%。這些系統(tǒng)依靠機(jī)器視覺(jué)等機(jī)器人技術(shù)來(lái)識(shí)別和響應(yīng)障礙物。然后，他們將整個(gè)車輛變成一種機(jī)器人，無(wú)需人工輸入即可行動(dòng)。

 

今天阻礙全自動(dòng)汽車的是機(jī)器人技術(shù)還不夠先進(jìn)。他們的人工智能 (AI) 系統(tǒng)必須非?？焖俚刈龀鲰憫?yīng)，即使對(duì)于機(jī)器人也是如此，并且在變化和不可預(yù)測(cè)的情況下始終如一地執(zhí)行。隨著機(jī)器人的進(jìn)步和這些目標(biāo)的實(shí)現(xiàn)，真正的自動(dòng)駕駛汽車將成為日常現(xiàn)實(shí)。

 
 

雖然完全機(jī)器人乘用車已被證明是一個(gè)挑戰(zhàn)，但自動(dòng)化公共交通可能更容易。公共汽車、火車和班車遵循固定路線，提供了當(dāng)今機(jī)器人良好運(yùn)行所需的可預(yù)測(cè)性。他們通常還會(huì)在專用空間中行駛，從而降低與其他車輛發(fā)生碰撞的風(fēng)險(xiǎn)。

 

德國(guó)巴特比恩巴赫市于 2017 年開始測(cè)試自動(dòng)駕駛巴士。，它已完成 10,000 多公里的無(wú)人駕駛旅行，搭載約 20,000 名乘客。制造自動(dòng)班車的公司 EasyMile 已幫助全球城市建立無(wú)人駕駛巴士路線。

 

2019 年，一列自動(dòng)駕駛列車在 48 英里的軌道上牽引 30 輛貨車，展示了無(wú)人駕駛列車的潛力。隨著美國(guó)希望擴(kuò)大其鐵路系統(tǒng)并使其現(xiàn)代化，自動(dòng)駕駛列車可能會(huì)變得司空見慣。早期制動(dòng)等人工智能功能也可以使鐵路旅行更安全。

 

雖然自動(dòng)駕駛汽車可能是機(jī)器人在交通運(yùn)輸中令人興奮的應(yīng)用，但它們遠(yuǎn)非。運(yùn)輸行業(yè)中機(jī)器人技術(shù)更常見的用例是在制造車輛的制造中心。自動(dòng)化已成為汽車制造的關(guān)鍵部分，可實(shí)現(xiàn)更高的產(chǎn)量。

 

Tesla Gigafactory 在機(jī)器人技術(shù)使用方面處于行業(yè)地位，某些部分實(shí)現(xiàn)了 90% 的自動(dòng)化，幾乎不需要人工輸入。這種高水平的自動(dòng)化使工廠能夠在創(chuàng)紀(jì)錄的時(shí)間內(nèi)生產(chǎn)出技術(shù)復(fù)雜的車輛，以滿足高需求。考慮到特斯拉 Cyber ??truck在一個(gè)月的預(yù)購(gòu)量如何超過(guò) 250,000輛，這種速度至關(guān)重要。

 

更快的生產(chǎn)時(shí)間也讓汽車制造商在更短的時(shí)間內(nèi)推出新車型。因此，他們可以生產(chǎn)出創(chuàng)新、并將其交到駕駛員手中，而這個(gè)想法仍然是新的和令人興奮的。

 

Robots are already far more widespread than many people realize. They’re now essential to many industries, unlocking new opportunities and avenues for innovation. One of the sectors that stands to gain the most from robots is transportation.

 

Outside of manufacturing, the implementation of robotics in transportation is a relatively recent phenomenon. Despite this relative novelty, robots have already made significant strides in the transportation industry, driving innovation across multiple sub-sectors and applications. Modern transportation wouldn’t be the same without robots, and the future will only serve to solidify this.

 

Here’s how robots are pushing the transportation industry forward and where they could go from here.

 

Self-Driving Cars

Perhaps the most iconic example of robots driving innovation in transport is self-driving cars. While fully autonomous vehicles are not yet a reality, automated features have already brought significant improvements to cars today. Automatic braking, lane correction, and adaptive cruise control are all examples of robotic control in today’s vehicles.

 

Automatic emergency braking has cut rear collisions by 50% in cars that have it. These systems rely on robotic technologies like machine vision to recognize and respond to obstacles. They then turn the entire vehicle into a type of robot, acting without human input.

 

The only things holding back fully autonomous cars today are robotic technologies that are not yet advanced enough. Their artificial intelligence (AI) systems have to respond remarkably quickly, even for a robot, and perform consistently in varying and unpredictable situations. As robots advance and these goals become possible, true self-driving cars will become a daily reality.

 

Autonomous Public Transport

While fully robotic passenger vehicles have proved a challenge, automating public transit may be easier. Buses, trains, and shuttles follow fixed routes, providing the predictability that robots today need to perform well. They also typically travel in dedicated spaces, reducing the risk of collision with other vehicles.

 

The city of Bad Birnbach, Germany, started testing an autonomous bus in 2017. Within the first year, it had completed more than 10,000 kilometers of driverless travel, carrying around 20,000 passengers. EasyMile, the company that made the autonomous shuttle, has since helped cities across the globe establish driverless bus routes.

 

In 2019, an autonomous train pulled 30 freight cars across 48 miles of track, showing the potential of driverless trains. As the U.S. looks to expand and modernize its rail system, autonomous trains could become commonplace. AI features like early braking could make rail travel safer, too.

 

Faster Production

While autonomous vehicles may be the most exciting application of robots in transportation, they’re far from the only one. A more common use case for robotics in the transport industry is in the manufacturing centers that build vehicles. Automation has become a critical part of car manufacturing, enabling higher output.

 

The Tesla Gigafactory leads the industry in robotics use, with some sections being 90% automated, requiring almost no human input. This high level of automation lets the factory produce its technologically complex vehicles in record time to meet high demand. Considering how the Tesla Cybertruck sold more than 250,000 preorders in its first month, that speed is essential.

 

Faster production times also let automakers roll out new models in less time. As a result, they can produce innovative, cutting-edge designs and get them in drivers’ hands while the idea is still new and exciting.