Relay Robotics Calls For Creation Of SAE-Style Autonomy Levels For Indoor Robots Robotics & Automation News

(MENAFN- Robotics & automation News) Relay Robotics calls for creation of SAE-style autonomy levels for indoor robots

Relay Robotics – a provider of indoor robots mainly used in hotels and hospitals to deliver small items – is calling for the creation of a framework for indoor robots similar to that for autonomous road cars.

The SAE – short for Society of Automotive Engineers – released a guidance in 2014 which defined levels of autonomy in automotive vehicles from 0 to 5, and this has become the standard reference for the automotive industry.

Now, in a detailed article , two robotics experts at Relay Robotics – Sonali Deshpande, senior navigation engineer, and Jim Slater, robot systems architect – have outlined a set of definitions which the company hopes will help the wider industry develop such a framework for indoor robots.

In the automotive sector, much of the SAE framework is based on the distribution of driving responsibilities between the human driver and the self-driving agent. Level 0 indicates no automation – or entirely mechanical – where the human driver is completely in control.

Levels 1, 2, and 3 have varying degrees of partial automation. At Level 4, the vehicle is fully self-driving, but only under certain defined conditions.

And Level 5 is full automation everywhere and in all conditions.

Below is the way Relay Robotics sees how indoor robots could be defined in parallel to the automotive sector.

These are robots that have no autonomous navigation capabilities and rely entirely on humans to operate them. Robots that fall into this category are telepresence robots and remote controlled robots like remote-controlled cars.

Robots that have a minimal sensor suite and can only navigate on paths that are predefined using physical mechanisms like wires buried in the floor, magnetic tape or paint. These Level 1 robots have no ability to leave these predefined paths.

Such AGVs have no concept of location, using only the distance traveled along the path to make decisions. They can typically detect obstacles and slow down or stop for them, but they do not have the ability to avoid obstacles.

Level 1 robots need extensive changes to a building's infrastructure during installation leading to significant cost. They have almost no social navigation capability, and so their operational domain is mainly highly structured and controlled manufacturing and logistics environments.

Robots operating at Level 2 are AGVs that do not need physical path definition but still rely on paths that are digitally defined during installation. These mobile robots can localize themselves within a site using external aids such as reflectors, fiducials or beacons that are placed in strategic locations at the site. They can use this location to follow the virtually defined paths.

Like Level 1 robots, these robots also cannot leave their virtual predefined paths and can only detect and stop for obstacles but cannot avoid them.

Although the infrastructure changes required are not as intrusive as Level 1, because of the need for installation of external localization sources, these robots have moderate complexity of installation.

The fixed paths mean that they have low social navigation skill and are still best used in relatively structured environments with little to no interaction with humans.

Robots operating at Level 3 rely entirely on onboard sensors for navigation. They use lidars and/or cameras to form a map of their environment and localize themselves within it. Using this map, they can plan their own paths through the site.

They can also dynamically change their path if they detect obstacles on it. So they can not only detect obstacles, but can also avoid them.

This independence and flexibility of Level 3 robots results in moderate social navigation skills and significantly reduced installation complexity since no infrastructure changes are required.

Level 3 robots can be used in unstructured environments where they can navigate alongside humans. They represent a significant increase in intelligence, and systems of this level and higher are called autonomous mobile robots (AMRs). Most modern service robots belong to this category.

Even though robots of Level 3 cross the threshold of navigating in unstructured environments alongside humans, they still navigate with moderate social navigation skill.

They do not have the advanced social navigation skills needed to adapt to all human interaction scenarios with sophistication. This sometimes requires the humans it interacts with to compensate for its behavioral limitations.

In contrast, Level 4 robots are AMRs with social navigation skills evolved enough to be on par with humans. They can capably navigate in any indoor environment in any situation provided there aren't any physical limitations.

This means that their operational domain can include all indoor environments. Another ramification of this is that Level 4 robots should never need human intervention to navigate.

This level has not yet been fully achieved, and defining and evaluating everything that is required for such sophisticated social navigation is challenging and remains an active area of research. Here is an infographic from a recent attempt to capture all the facets of social navigation:

To navigate capably in all indoor environments, robots need to be able to optimize within a complex, ill-defined, and constantly changing set of rules.

This is something that humans handle effortlessly and often without conscious thought, but that ease belies a lot of complexity.

Relay Robotics details some challenges that lie on the path to achieving human-level social navigation.

All in all, achieving this level of social navigation is extremely challenging. While some Level 3 robots may have partially solved some of these problems, there is still quite a ways to go to reach true Level 4 autonomy.

As humans, we are able to find our way even in new, unfamiliar buildings by relying on signage, using semantic knowledge, and by asking for directions when necessary. Robots today cannot do this. At the very least, the site needs to be fully mapped during installation.

Level 5 robots are robots that could navigate in all indoor environments on par with human skill, as well as do so in a completely new environment without detailed prebuilt maps and a manually intensive installation process.

This would remove installation complexity entirely, allowing robots to be operational in new environments instantly, reducing friction for adoption, and paving the way for robots to become more widespread.

This is a missing level in the framework for self-driving cars as they also go through a similar process where high precision 3D maps of an area are created and annotated before a self-driving car can operate in it.

Developments in artificial intelligence could help realize Level 5 capability, says Relay Robotics.

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