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Students Outpace DeepRacer With IoT Algorithms

College of Charleston attendees teamed with Internet of Things company Logicalis to fine-tune a set of reinforced-learning tools that enable a vehicle, using wireless connectivity to a laptop or tablet, to learn its own route and respond with appropriate speed and direction settings.
By Claire Swedberg

Once the students and the technology company teamed up, Funderburg says, they began exploring. "We wanted to take an end-to-end approach," he states, "and we looked at existing technologies and started filtering out use cases that inspired them." The students' objective, Starr explains, "was to apply the data science process—brainstorming a project, obtaining data, understanding and cleaning data, solving the problem and interpreting the results—in a real-world setting."

The team discovered that they wanted a system with which they could leverage computing at the edge. "That's a big area of focus, machine learning and inference on the edge, without going through the long pipeline," Funderburg says. The group considered several options before building the IoT-based solution that it then employed first on a virtual vehicle.

The group acquired the DeepRacer vehicle and the track beta simulation, then began developing and testing the RL algorithm. The students utilized the simulation of the vehicle, which they named Virtual Car 42 (the number 42 represents the answer to the "ultimate question of life, the universe and everything" in Douglas Adams' novel The Hitchhiker's Guide to the Galaxy and its sequels and adaptations).

They then began putting the simulated vehicle through testing, and ultimately found that the learning algorithms they had developed allowed their virtual vehicle to outperform the AWS DeepRacer, based on the number of laps properly completed. The algorithm identified curves in the road, for instance, and adjusted the vehicle's steering and acceleration to navigate each curve at the highest possible speed.

By July 2019, the group was using the software with the physical car. The vehicle comes with an onboard camera and accelerometer gyroscope that helps them identify the vehicle's location. The students used the AWS Sage Maker machine-learning platform. The AWS Robotmaker robot simulation platform transmits code to the vehicle via Wi-Fi, but this can also be accomplished using a USB stick. The team was then able to control the data flowing to and from the vehicle with an iPad or computer.

The machine-learning algorithms applied to Virtual Car 42 could be provided to other physical devices, such as equipment or vehicles used in commercial or industrial applications. For instance, with IoT connectivity using Wi-Fi or another wireless network, devices could utilize sensors to capture and provide information such as predictive maintenance, image and sound recognition, and surveillance, while also enabling autonomous driving.

The next step could involve marketing, Funderburg says, noting that there are a wide variety of applications for the solution the team has developed. Computing at the edge can serve many IoT deployments, for example, including those in asset tracking, geofencing and other use cases for which sending large amounts of data to a server may pose a security or bandwidth concern.

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