November 21, 2013 04:16pm ETSelf-driving Cars and Autonomous Robots: Where to Now? (Op-Ed) Science fiction to non-fiction: the next generation of robots promises to be ultra intelligent Science fiction to non-fiction: the next generation of robots promises to be ultra intelligent. Credit: andreybl. View full size image

This article was originally published at The Conversation. The publication contributed the article to LiveScience's Expert Voices: Op-Ed & Insights.

There isn’t a radio-control handset in sight as a nimble robot briskly weaves itself in and out of the confined tunnels of an underground mine.

Powered by ultra-intelligent sensors, the robot intuitively moves and reacts to the changing conditions of the terrain, entering areas unfit for human testing. As it does so, the robot transmits a detailed 3D map of the entire location to the other side of the world.

While this might read like a scenario from a George Orwell novel, it is actually a reasonable step into the not-so-distant future of the next generation of robots.

A recent report released by the McKinsey Institute predicts the potential economic contribution of new technologies such as advanced robotics, mobile internet and 3D printing are expected to return between US$14 trillion and US$33 trillion globally per year by 2025.

Credit: Mark Strozier.View full size image

Technology advisory firm Gartner also recently released a report predicting the “smart machine era” to be the most disruptive in the history of IT. This trend includes the proliferation of contextually aware, intelligent personal assistants, smart advisers, advanced global industrial systems and the public availability of early examples of autonomous vehicles.

If the global technology industry and governments are to reap the productivity and economical benefits from this new wave of robotics they need to act now to identify simple yet innovative ways to disrupt their current workflows.

Self-driving cars

The automotive industry is already embracing this movement by discovering a market for driver assistance systems that includes parking assistance, autonomous driving in “stop and go” traffic and emergency braking.

In August 2013, Mercedes-Benz demonstrated how their “self-driving S Class” model could drive the 100-kilometre route from Mannheim to Pforzheim in Germany. (Exactly 125 years earlier, Bertha Benz drove that route in the first ever automobile, which was invented by her husband Karl Benz.)

The car they used for the experiment looked entirely like a production car and used most of the standard sensors on board, relying on vision and radar to complete the task. Similar to other autonomous cars, it also used a crucial extra piece of information to make the task feasible – it had access to a detailed 3D digital map to accurately localise itself in the environment.

A high-resolution 3D map of Guangzhou, China. Credit: Colin ZHU.View full size image

When implemented on scale, these autonomous vehicles have the potential to significantly benefit governments by reducing the number of accidents caused by human error as well as easing traffic congestion as there will no longer be the need to implement tailgating laws enforcing cars to maintain large gaps in between each other.

In these examples, the task (localisation, navigation, obstacle avoidance) is either constrained enough to be solvable or can be solved with the provision of extra information. However, there is a third category, where humans and autonomous systems augment each other to solve tasks.

This can be highly effective but requires a human remote operator or depending on real time constraints, a human on stand-by.

The trade-off Credit: FlySi.View full size image

The question arises: how can we build a robot that can navigate complex and dynamic environments without 3D maps as prior information, while keeping the cost and complexity of the device to a minimum?

Using as few sensors as possible, a robot needs to be able to get a consistent picture of its environment and its surroundings to enable it to respond to changing and unknown conditions.

This is the same question that stood before us at the dawn of robotics research and was addressed in the 1980s and 1990s to deal with spatial uncertainty. However, the decreasing cost of sensors, the increasing computing power of embedded systems and the ability to provide 3D maps, has reduced the importance of answering this key research question.

In an attempt to refocus on this central question, we – researchers at the Autonomous Systems Laboratory at CSIRO – tried to stretch the limits of what’s possible with a single sensor: in this case, a laser scanner.

In 2007, we took a vehicle equipped with laser scanners facing to the left and to the right and asked if it was possible to create a 2D map of the surroundings and to localise the vehicle to that same map without using GPS, inertial systems or digital maps.

The result was the development of our now commercialised Zebedee technology – a handheld 3D mapping system incorporates a laser scanner that sways on a spring to capture millions of detailed measurements of a site as fast as an operator can walk through it.

While the system does add a simple inertial measurement unit which helps to track the position of the sensor in space and supports the alignment of sensor readings, the overall configuration still maximises information flow from a very simple and low cost setup.

It achieves this by moving the smarts away from the sensor and into the software to compute a continuous trajectory of the sensor, specifying its position and orientation at any time and taking its actual acquisition speed into account to precisely compute a 3D point cloud.

The crucial step of bringing the technology back to the robot still has to be completed. Imagine what is possible when you remove the barrier of using an autonomous vehicle to enter unknown environments (or actively collaborating with humans) by equipping robots with such mobile 3D mapping technologies. They can be significantly smaller and cheaper while still being robust in terms of localisation and mapping accuracy.

From laboratory to factory floor

A specific area of interest for this robust mapping and localisation is the manufacturing sector where non-static environments are becoming more and more common, such as the aviation industry. Cost and complexity for each device has to be kept to a minimum to meet these industry needs.

With a trend towards more agile manufacturing setups, the technology enables lightweight robots that are able to navigate safely and quickly through unstructured and dynamic environments like conventional manufacturing workplaces. These fully autonomous robots have the potential to increase productivity in the production line by reducing bottlenecks and performing unstructured tasks safely and quickly.

The pressure of growing increasing global competition means that if manufacturers do not find ways to adopt these technologies soon they run the risk of losing their business as competitors will soon be able to produce and distribute goods more efficiently and at less cost.

It is worth pushing the boundaries of what information can be extracted from very simple systems. New systems which implement this paradigm will be able to gain the benefits of unconstrained autonomous robots but this requires a change in the way we look at the production and manufacturing processes.

This article is an extension of a keynote presented at the robotics industry business development event RoboBusiness in Santa Clara, CA on October 25 2013.

Michael Brünig works for CSIRO. Part of this work has received funding from 3D Laser Mapping.

無人駕駛汽車和自主機器人

2024-04-09日下午4時16 ETSelf駕駛汽車和自主機器人：哪裏？

這篇文章最初發錶在NBSP;談話NBSP;出版貢獻了文章，以生活科學的NBSP;專家的聲音：。專欄文章和放大器;洞察.

有ISNrsquo的; TA在視線無線電控製手機作為一個靈活的機器人輕快地編織自己在地下礦井.

的密閉隧道進出

技術超智能傳感器，機器人直觀地移動，並作出反應，改變地形條件，進入不適合人類試驗區。至於它這樣做，機器人傳送整個位置到世界的另一邊的詳細的3D地圖.

雖然這可能讀起來就像從喬治·奧威爾小說中的一個場景，它實際上是一個合理的步驟進不下一代機器人的 - 所以，不久的將來.

麥肯錫研究院最近公佈的一份報告預測新技術，如先進的機器人技術，移動互聯網和3D打印的潛在經濟貢獻有望之間US $ 14個萬億美元，返回全球每年$ 33個萬億2025

技術谘詢機構Gartner也於近日公佈的一份報告預測＆ldquo;智能機時代＆rdquo;的是最破壞性的IT的曆史。這種趨勢包括上下文感知，智能個人助理，智能顧問，先進的全球産業體係和自主車的早期例子公開提供.

擴散。如果全球科技産業和政府都收獲從這個生産率和經濟效益機器人技術的新浪潮，他們需要現在就採取行動，以確定簡單而創新的方法來破壞他們目前的工作流程

自駕車汽車??

汽車行業正在通過發現市場對於駕駛者輔助係統，包括泊車輔助已經擁抱這個動作，自動駕駛在＆ldquo;走走停停＆rdquo;的交通和緊急製動.

在2013年8月，梅賽德斯 - 奔馳展示了如何自己和ldquo;自駕車S級＆rdquo;的模型可以開車從曼海姆的100公裏路線普福爾茨海姆在德國。 （究竟125年早些時候，貝莎奔馳開了有史以來第一個汽車，它發明了丈夫卡爾·奔馳的這條路線。）

他們用於實驗的車看起來完全像一個生産汽車，並用最標準的傳感器上闆，靠視覺和雷達，以完成任務。類似於其他自主車，它也採用了至關重要的額外的資料片，以使任務可行＆ndash的;它有獲得了詳細的三維數字地圖來準確定位自身的環境。

廣州，中國的高解析度3D地圖。

當上規模的實施，這些自動駕駛汽車有可能降低了因人為錯誤以及緩解交通擁堵交通事故的數量顯著受益政府，因為將不再需要實施貼執行車保持在彼此.

之間在這些例子中較大的間隙法，任務（定位，導航，避障）的任一約束足以可解或可與提供額外的信息來解決。然而，有一個第三類，在人類和自治係統互相補充以解決任務.

這可以是非常有效的，但需要一個人的遠程操作員或取決於實時限製，在待機人類。

的問題是：我們如何能夠建立一個機器人，可以瀏覽複雜和動態的環境中冇有三維地圖作為先驗信息，同時保持了設備的成本和複雜性降至最低

用盡可能少的傳感器成為可能，機器人需要能夠得到它的環境和周圍的一緻的畫麵，使其能對不斷變化和未知的情況作出反應.

這是擺在我們麵前的站在黎明同樣的問題機器人研究並解決20世紀80年代和90年代，以應對空間的不確定性。然而，傳感器的成本逐漸降低，嵌入式係統的不斷增加計算能力，並提供3D地圖的能力，降低了回答這個重點研究的問題.

在試圖把目光集中到這個中心問題的重要性，我們＆ndash的;研究人員在自治係統實驗室CSIRO＆ndash的;試圖伸展的什麼＆rsquo的限度氏可能用一個傳感器：在此情況下，激光掃描器.

在2007年，我們採取了配備有激光掃描儀的車輛朝嚮的左側和嚮右側，並詢問是否有可能以創建環境的二維地圖和本地化車輛到同一地圖，而無需使用全球定位係統，慣性係統或數字地圖.

結果是我們現在市售Zebedee的技術＆ndash的的發展;手持式3D地圖係統採用了激光掃描儀，搖擺在春天捕獲數以百萬計的網站的詳細測量一樣快，操作人員可以通過它走.

雖然係統確實增加了一個簡單的慣性測量單元，這有助於追蹤傳感器的位置在空間和支持傳感器讀數的對準，整體配置仍最大化從一個非常簡單和低成本的設置信息流.

它由從傳感器和到軟件到移動智慧遠離實現此計算傳感器的連續軌跡，指定其位置和方嚮，在任何時候和以它的實際採集速度考慮在內，以精確地計算三維點雲.

使技術回機器人的關鍵步驟仍必須完成。想象一下，什麼是可能的，當你刪除使用自主汽車進入未知環境（或與人合作，積極），通過配備機器人這樣的移動3D繪圖技術的障礙。它們可以是顯著更小，更便宜，同時仍然強勁的定位與地圖精度等方麵。

從實驗室到工廠車間

為這個強大的測繪和定位感興趣的特定領域是製造業，其中非靜態環境正變得越來越比較常見的，如航空業。成本和複雜性的每個裝置必須保持在最低限度，以滿足這些工業需要.

隨著嚮更敏捷製造設置一種趨勢，該技術可使輕質機器人能夠通過非結構化和動態環境類似於傳統的安全快速導航製造業的工作場所。這些全自主機器人必須通過減少瓶頸和進行非結構化任務，安全，快速.

日益增加的全球競爭的壓力，以提高生産效率，在生産線上的電勢意味著，如果廠家不想方設法盡快它們運行的??採用這些技術失去了他們的業務，競爭對手的風險將很快能夠更有效地生産和銷售商品和更低的成本.

值得推什麼樣的信息可以從非常簡單的係統中提取的界限。它實現這種模式的新係統將能夠獲得對不受約束的自主機器人的好處，但是這需要我們來看看在生産和製造過程的方式發生變化.

這篇文章是基調在機器人産業企業提出延期發展的大事RoboBusiness在美國加州聖克拉拉，在2024-04-09日.

邁克爾登記及uuml; NIG工程CSIRO。這項工作的一部分已獲得的資金從三維激光測繪.