How Machine Learning Improves Robot Navigation

Unlike their sci-fi counterparts, many real-world robots are stationary. However, mobile robots have been around for a while, and they’re becoming more common. Machine learning improvements are a big factor behind this trend.

Navigating through a workspace is much harder for robots than it may seem at first. That barrier is becoming less of an obstacle as machine learning techniques improve. Before long, robots will be able to move well enough for applications like self-driving cars to finally become a full-fledged reality.

How Do Robots Navigate?

Researchers have been working on mobile robots since the 1940s, though they’ve been fairly basic for much of that time. The simplest don’t strictly navigate their environment but follow a set path. Some follow a visible line on the floor, moving along it like a train on tracks, while others follow a preprogrammed path based on distance.

Newer mobile robots may use proximity sensors placed throughout their environment to navigate. While this offers more flexibility than preprogrammed routes, setting up sensors throughout a workplace can get expensive. Communication errors can also lead to crashes or misdirection.

Other robots connect to digital maps and location technologies like GPS to determine where they are in relation to other objects or areas. However, these systems aren’t great at helping robots around obstacles or accounting for changes in the environment.

How Does Machine Learning Improve Robot Navigation?

AI and machine learning take things further. As more mobile robots feature machine learning capabilities, their navigation improves along several fronts.

1. Reduced Manual Input

The most immediate difference is that machine learning-powered robots require less manual input to move. Machine vision algorithms let these robots identify their surroundings so they can navigate without a map or preprogrammed path. They also don’t need workers to set up a network of sensors or lay down physical paths to follow.

This increased autonomy is particularly important in manufacturing workflows. Experts predict skills gaps could leave 2.1 million unfilled jobs in manufacturing by 2030, so factories can’t afford to spend time overseeing robots. When bots can move without manual input, workers can accomplish other, more pressing things to mitigate this shortage.

Robotics engineers can go further and reduce manual input in model training. Unsupervised learning trains algorithms to discover patterns without human intervention, making it a more cost- and time-efficient way to get started.

2. Better Obstacle Recognition

Machine learning is also better at recognizing and avoiding obstacles than other robot navigation methods. This ability stems from machine vision — a central part of how robots navigate with AI. Object recognition identifies paths to follow and highlights obstacles to avoid running into.

This identification is more complex than it may seem at first. It’s not enough to recognize that an object is nearby, as appropriate responses vary for different obstacles. Some robot-guiding models are accurate enough to distinguish tables from chairs while also recognizing people, walls and individual rooms to create a detailed 3D map of the environment.

Once machine vision recognizes an object, the guiding model determines the appropriate response. That could be stopping until a human moves out of the way or walking around stationary obstacles like a box on the floor. Whatever the specifics of the response, this obstacle recognition prevents collisions for streamlined workflows and increased safety.

3. Faster Routes

Mobile robots can also move faster with machine learning. While AI may not improve their physical movement speed, it finds more efficient routes more effectively than conventional methods.

Machine learning models can identify ideal paths in a few ways. The most basic is to analyze maps of the environment and data from these areas to find which routes are most efficient at what times. This is how logistics AI routing software has shortened some companies’ routes by eight miles per driver per year.

The other option is to respond to real-time conditions. A robot moving throughout a warehouse could analyze what’s in front of it to determine the quickest way around obstacles. It would adjust and edit its path as new ones emerge, just as a human might.

4. Improved Adaptability

These three benefits of machine learning in robot navigation contribute to another, larger advantage — adaptability. Because machine learning requires little to no human input, enables accurate object detection and can find the fastest routes, it’s far more flexible than older methods.

Map-based and preprogrammed path-based navigation only works when robots’ environments stay the same every day. They also can’t account for unpredictable obstacles like people moving around. Sensor-based navigation offers a little more flexibility but can’t always detect obstacles and requires manual adjustment if the space itself changes.

Machine learning, by contrast, is flexible by design. Because robots learn to navigate based on real-time vision and other data, they can avoid obstacles even when they’re not in predictable places and find their way through unfamiliar areas. That adaptability is increasingly crucial today, as 48% of manufacturers are moving away from less flexible just-in-time practices. 

5. Improvement Over Time

Machine learning also has the advantage of improving over time. Robot-guided machine learning models become more accurate and flexible the more data they get.

Self-driving cars — the ultimate application of mobile robotics — get better at responding to unexpected situations as they drive more miles and encounter these issues firsthand. Just as humans learn from experience, machine learning applies lessons from past mistakes and successes to new scenarios.

Even outside of driverless vehicles, this ongoing improvement holds vast potential for the field of robotics. It means mobile robots will become more efficient and experience fewer collisions over time, leading to improved returns on investment to justify their high cost. 

Machine Learning Is the Key to Better Mobile Robots

Mobile robots can play a big role in industries like manufacturing, warehousing and transportation. They must first be reliable, and conventional navigation methods aren’t the most consistent.

Machine learning offers solutions to conventional robot navigation’s biggest problems. As these AI models improve, so will mobile robots and their use cases.

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