All posts by CSAIL MIT

Being able to both walk and take flight is typical in nature – many birds, insects and other animals can do both. If we could program robots with similar versatility, it would open up many possibilities: picture machines that could fly into construction areas or disaster zones that aren’t near roads, and then be able to squeeze through tight spaces to transport objects or rescue people.

The problem is that usually robots that are good at one mode of transportation are, by necessity, bad at another. Drones are fast and agile, but generally have too limited of a battery life to travel for long distances. Ground vehicles, meanwhile, are more energy efficient, but also slower and less mobile.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are aiming to develop robots that can do both. In a new paper, the team presented a system of eight quadcopter drones that can both fly and drive through a city-like setting with parking spots, no-fly zones and landing pads.

“The ability to both fly and drive is useful in environments with a lot of barriers, since you can fly over ground obstacles and drive under overhead obstacles,” says PhD student Brandon Araki, lead author on a paper about the system out of CSAIL director Daniela Rus’ group. “Normal drones can’t maneuver on the ground at all. A drone with wheels is much more mobile while having only a slight reduction in flying time.”

Araki and Rus developed the system along with MIT undergraduate students John Strang, Sarah Pohorecky and Celine Qiu, as well as Tobias Naegeli of ETH Zurich’s Advanced Interactive Technologies Lab. The team presented their system at IEEE’s International Conference on Robotics and Automation (ICRA) in Singapore earlier this month.

How it works

The project builds on Araki’s previous work developing a “flying monkey” robot that crawls, grasps, and flies. While the monkey robot could hop over obstacles and crawl about, there was still no way for it to travel autonomously.

To address this, the team developed various “path-planning” algorithms aimed at ensuring that the drones don’t collide. To make them capable of driving, the team put two small motors with wheels on the bottom of each drone. In simulations the robots could fly for 90 meters or drive for 252 meters before their batteries ran out.

Adding the driving component to the drone slightly reduced its battery life, meaning that the maximum distance it could fly decreased 14 percent to about 300 feet. But since driving is still much more efficient than flying, the gain in efficiency from driving more than offsets the relatively small loss in efficiency in flying due to the extra weight.

“This work provides an algorithmic solution for large-scale, mixed-mode transportation and shows its applicability to real-world problems,” says Jingjin Yu, a computer science professor at Rutgers University who was not involved in the paper.

The team also tested the system using everyday materials like pieces of fabric for roads and cardboard boxes for buildings. They tested eight robots navigating from a starting point to an ending point on a collision-free path, and all were successful.

Rus says that systems like theirs suggest that another approach to creating safe and effective flying cars is not to simply “put wings on cars,” but to build on years of research in drone development to add driving capabilities to them.

“As we begin to develop planning and control algorithms for flying cars, we are encouraged by the possibility of creating robots with these capabilities at small scale,” says Rus. “While there are obviously still big challenges to scaling up to vehicles that could actually transport humans, we are inspired by the potential of a future in which flying cars could offer us fast, traffic-free transportation.”

Click here to read the paper.

Computer scientists have been working for decades on automatic navigation systems to aid the visually impaired, but it’s been difficult to come up with anything as reliable and easy to use as the white cane, the type of metal-tipped cane that visually impaired people frequently use to identify clear walking paths.

White canes have a few drawbacks, however. One is that the obstacles they come in contact with are sometimes other people. Another is that they can’t identify certain types of objects, such as tables or chairs, or determine whether a chair is already occupied.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system that uses a 3-D camera, a belt with separately controllable vibrational motors distributed around it, and an electronically reconfigurable Braille interface to give visually impaired users more information about their environments.

The system could be used in conjunction with or as an alternative to a cane. In a paper they’re presenting this week at the International Conference on Robotics and Automation, the researchers describe the system and a series of usability studies they conducted with visually impaired volunteers.

“We did a couple of different tests with blind users,” says Robert Katzschmann, a graduate student in mechanical engineering at MIT and one of the paper’s two first authors. “Having something that didn’t infringe on their other senses was important. So we didn’t want to have audio; we didn’t want to have something around the head, vibrations on the neck — all of those things, we tried them out, but none of them were accepted. We found that the one area of the body that is the least used for other senses is around your abdomen.”

Katzschmann is joined on the paper by his advisor Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science; his fellow first author Hsueh-Cheng Wang, who was a postdoc at MIT when the work was done and is now an assistant professor of electrical and computer engineering at National Chiao Tung University in Taiwan; Santani Teng, a postdoc in CSAIL; Brandon Araki, a graduate student in mechanical engineering; and Laura Giarré, a professor of electrical engineering at the University of Modena and Reggio Emilia in Italy.

Parsing the world

The researchers’ system consists of a 3-D camera worn in a pouch hung around the neck; a processing unit that runs the team’s proprietary algorithms; the sensor belt, which has five vibrating motors evenly spaced around its forward half; and the reconfigurable Braille interface, which is worn at the user’s side.

The key to the system is an algorithm for quickly identifying surfaces and their orientations from the 3-D-camera data. The researchers experimented with three different types of 3-D cameras, which used three different techniques to gauge depth but all produced relatively low-resolution images — 640 pixels by 480 pixels — with both color and depth measurements for each pixel.

The algorithm first groups the pixels into clusters of three. Because the pixels have associated location data, each cluster determines a plane. If the orientations of the planes defined by five nearby clusters are within 10 degrees of each other, the system concludes that it has found a surface. It doesn’t need to determine the extent of the surface or what type of object it’s the surface of; it simply registers an obstacle at that location and begins to buzz the associated motor if the wearer gets within 2 meters of it.

Chair identification is similar but a little more stringent. The system needs to complete three distinct surface identifications, in the same general area, rather than just one; this ensures that the chair is unoccupied. The surfaces need to be roughly parallel to the ground, and they have to fall within a prescribed range of heights.

Tactile data

The belt motors can vary the frequency, intensity, and duration of their vibrations, as well as the intervals between them, to send different types of tactile signals to the user. For instance, an increase in frequency and intensity generally indicates that the wearer is approaching an obstacle in the direction indicated by that particular motor. But when the system is in chair-finding mode, for example, a double pulse indicates the direction in which a chair with a vacant seat can be found.

The Braille interface consists of two rows of five reconfigurable Braille pads. Symbols displayed on the pads describe the objects in the user’s environment — for instance, a “t” for table or a “c” for chair. The symbol’s position in the row indicates the direction in which it can be found; the column it appears in indicates its distance. A user adept at Braille should find that the signals from the Braille interface and the belt-mounted motors coincide.

In tests, the chair-finding system reduced subjects’ contacts with objects other than the chairs they sought by 80 percent, and the navigation system reduced the number of cane collisions with people loitering around a hallway by 86 percent.