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Researchers develop self driving scooter for indoors and out

The self driving scooter developed by researchers at MIT, NUS assist mobility impaired users in navigating their surroundings.

self driving scooter, mobility impaired, autonomous mobility scooter, MIT, CSAIL, NUS, SMART, route planning algorithms, localisation algorithms, map building algorithms, scheduling algorithm, fleet resources, schedule rides, autonomous cars, IEEE, Intelligent Transport system, golf cart, science, science news Self Driving scooter developed by researchers at MIT, NUS

Researchers have developed a new system for self-driving scooter that can help mobility-impaired users to get down the hall and through the lobby of an apartment building, or pick up an autonomous car on the public roads. The autonomous mobility scooter and related software were designed by researchers from Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the National University of Singapore, and the Singapore-MIT Alliance for Research and Technology (SMART).

“It’s extraordinary to me, because it’s a project that the team conducted in about two months,” said Daniela Rus, Professor of Electrical Engineering and Computer Science at MIT.

The researchers’ system includes several layers of software — low-level control algorithms that enable a vehicle to respond immediately to changes in its environment, such as a pedestrian darting across its path; route-planning algorithms; localisation algorithms that the vehicle uses to determine its location on a map; map-building algorithms that it uses to construct the map in the first place; a scheduling algorithm that allocates fleet resources; and an online booking system that allows users to schedule rides.

The researchers had previously used the same sensor configuration and software in trials of autonomous cars and golf carts, so the new trial completes the demonstration of a comprehensive autonomous mobility system. Using the same control algorithms for all types of vehicles — scooters, golf carts, and city cars — has several advantages. One is that it becomes much more practical to perform reliable analyses of the system’s overall performance.

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“If you have a uniform system where all the algorithms are the same, the complexity is much lower than if you have a heterogeneous system where each vehicle does something different,” Rus said. “That’s useful for verifying that this multilayer complexity is correct,” Rus added.

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Software uniformity also means that the scheduling algorithm has more flexibility in its allocation of system resources. If an autonomous golf cart is not available to take a user across a public park, a scooter could fill in; if a city car is not available for a short trip on back roads, a golf cart might be.


The researchers described the design of the scooter system and the results of a trial in a paper they presented recently at the IEEE International Conference on Intelligent Transportation Systems in Rio de Janeiro, Brazil.

First published on: 08-11-2016 at 04:09:10 pm
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