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.

无人驾驶汽车和自主机器人

2013年11月21日下午4时16 ETSelf驾驶汽车和自主机器人：哪里？

这篇文章最初发表在NBSP;谈话NBSP;出版贡献了文章，以生活科学的NBSP;专家的声音：。专栏文章和放大器;洞察.

有ISNrsquo的; TA在视线无线电控制手机作为一个灵活的机器人轻快地编织自己在地下矿井.

的密闭隧道进出

技术超智能传感器，机器人直观地移动，并作出反应，改变地形条件，进入不适合人类试验区。至于它这样做，机器人传送整个位置到世界的另一边的详细的3D地图.

虽然这可能读起来就像从乔治·奥威尔小说中的一个场景，它实际上是一个合理的步骤进不下一代机器人的 - 所以，不久的将来.

麦肯锡研究院最近公布的一份报告预测新技术，如先进的机器人技术，移动互联网和3D打印的潜在经济贡献有望之间US $ 14个万亿美元，返回全球每年$ 33个万亿2025

技术咨询机构Gartner也于近日公布的一份报告预测＆ldquo;智能机时代＆rdquo;的是最破坏性的IT的历史。这种趋势包括上下文感知，智能个人助理，智能顾问，先进的全球产业体系和自主车的早期例子公开提供.

扩散。如果全球科技产业和政府都收获从这个生产率和经济效益机器人技术的新浪潮，他们需要现在就采取行动，以确定简单而创新的方法来破坏他们目前的工作流程

自驾车汽车??

汽车行业正在通过发现市场对于驾驶者辅助系统，包括泊车辅助已经拥抱这个动作，自动驾驶在＆ldquo;走走停停＆rdquo;的交通和紧急制动.

在2013年8月，梅赛德斯 - 奔驰展示了如何自己和ldquo;自驾车S级＆rdquo;的模型可以开车从曼海姆的100公里路线普福尔茨海姆在德国。 （究竟125年早些时候，贝莎奔驰开了有史以来第一个汽车，它发明了丈夫卡尔·奔驰的这条路线。）

他们用于实验的车看起来完全像一个生产汽车，并用最标准的传感器上板，靠视觉和雷达，以完成任务。类似于其他自主车，它也采用了至关重要的额外的资料片，以使任务可行＆ndash的;它有获得了详细的三维数字地图来准确定位自身的环境。

广州，中国的高解析度3D地图。

当上规模的实施，这些自动驾驶汽车有可能降低了因人为错误以及缓解交通拥堵交通事故的数量显著受益政府，因为将不再需要实施贴执行车保持在彼此.

之间在这些例子中较大的间隙法，任务（定位，导航，避障）的任一约束足以可解或可与提供额外的信息来解决。然而，有一个第三类，在人类和自治系统互相补充以解决任务.

这可以是非常有效的，但需要一个人的远程操作员或取决于实时限制，在待机人类。

的问题是：我们如何能够建立一个机器人，可以浏览复杂和动态的环境中没有三维地图作为先验信息，同时保持了设备的成本和复杂性降至最低

用尽可能少的传感器成为可能，机器人需要能够得到它的环境和周围的一致的画面，使其能对不断变化和未知的情况作出反应.

这是摆在我们面前的站在黎明同样的问题机器人研究并解决20世纪80年代和90年代，以应对空间的不确定性。然而，传感器的成本逐渐降低，嵌入式系统的不断增加计算能力，并提供3D地图的能力，降低了回答这个重点研究的问题.

在试图把目光集中到这个中心问题的重要性，我们＆ndash的;研究人员在自治系统实验室CSIRO＆ndash的;试图伸展的什么＆rsquo的限度氏可能用一个传感器：在此情况下，激光扫描器.

在2007年，我们采取了配备有激光扫描仪的车辆朝向的左侧和向右侧，并询问是否有可能以创建环境的二维地图和本地化车辆到同一地图，而无需使用全球定位系统，惯性系统或数字地图.

结果是我们现在市售Zebedee的技术＆ndash的的发展;手持式3D地图系统采用了激光扫描仪，摇摆在春天捕获数以百万计的网站的详细测量一样快，操作人员可以通过它走.

虽然系统确实增加了一个简单的惯性测量单元，这有助于追踪传感器的位置在空间和支持传感器读数的对准，整体配置仍最大化从一个非常简单和低成本的设置信息流.

它由从传感器和到软件到移动智慧远离实现此计算传感器的连续轨迹，指定其位置和方向，在任何时候和以它的实际采集速度考虑在内，以精确地计算三维点云.

使技术回机器人的关键步骤仍必须完成。想象一下，什么是可能的，当你删除使用自主汽车进入未知环境（或与人合作，积极），通过配备机器人这样的移动3D绘图技术的障碍。它们可以是显著更小，更便宜，同时仍然强劲的定位与地图精度等方面。

从实验室到工厂车间

为这个强大的测绘和定位感兴趣的特定领域是制造业，其中非静态环境正变得越来越比较常见的，如航空业。成本和复杂性的每个装置必须保持在最低限度，以满足这些工业需要.

随着向更敏捷制造设置一种趋势，该技术可使轻质机器人能够通过非结构化和动态环境类似于传统的安全快速导航制造业的工作场所。这些全自主机器人必须通过减少瓶颈和进行非结构化任务，安全，快速.

日益增加的全球竞争的压力，以提高生产效率，在生产线上的电势意味着，如果厂家不想方设法尽快它们运行的??采用这些技术失去了他们的业务，竞争对手的风险将很快能够更有效地生产和销售商品和更低的成本.

值得推什么样的信息可以从非常简单的系统中提取的界限。它实现这种模式的新系统将能够获得对不受约束的自主机器人的好处，但是这需要我们来看看在生产和制造过程的方式发生变化.

这篇文章是基调在机器人产业企业提出延期发展的大事RoboBusiness在美国加州圣克拉拉，在2013年10月25日.

迈克尔登记及uuml; NIG工程CSIRO。这项工作的一部分已获得的资金从三维激光测绘.