“My research focuses on persistent operation of robots in challenging environments,” said Nina Mahmoudian, associate professor of mechanical engineering.  “And there’s no more challenging environment than underwater.”

Once a marine robot submerges in water, it loses the ability to transmit and receive radio signals, including data from the Global Positioning System (GPS).  Because of this, marine robots can only deploy for certain distances before they must be retrieved by humans.

“The way these marine robots work is that they are pre-programmed with an itinerary,” said Mahmoudian. “They do their thing, and then they come to the surface and send out a signal to be retrieved.  Humans have to go out, retrieve the robot, upload the data, recharge the battery, and then send it back out by hand.  That’s very expensive, and it limits the amount of time these robots can be performing their work.”

Mahmoudian cites the usage of marine robots in the search for Malaysian Airlines Flight 370, which disappeared over the Indian Ocean in 2014.  “Those robots only had enough battery to operate for 12 hours at a time,” she said.  “The crew spent more hours tending to the robots than the robots spent in operation.”

Her solution is to create a mobile docking station, which underwater robots could autonomously dock with. “And what if we had multiple docks, which were also mobile and autonomous?” continued Mahmoudian.  “The robots and the docks could coordinate with each other, so that they could recharge and upload their data, and then go back out to continue exploring, without the need for human intervention.  We’ve developed the algorithms to maximize these trajectories, so we get the optimum use of these robots.”

Their paper on this mission planning system has been published in the journal IEEE Robotics and Automation Letters, and they have also obtained a patent on their mobile underwater docking station.  “What’s key is that the docking station is portable,” she said.  “It can be deployed in a stationary location, but it can also be deployed on boats, or even on other autonomous underwater vehicles. And it’s designed to be platform-agnostic, so it can be utilized with any underwater vehicle.  The hardware and software work hand-in-hand.”

Mahmoudian pointed out that systems like this already exist in your living room.  “An autonomous vacuum, like a Roomba, does its vacuum cleaning,” she said, “and when it runs out of battery, it autonomously returns to its dock to get recharged.  That’s exactly what we are doing here, but the environment is much more challenging!”

If her system can successfully function in the challenging underwater environment, then Mahmoudian sees even greater horizons for this technology.  “This system can be used anywhere,” she said, “robots on land, air, or sea will be able operate indefinitely.  They will go into the arctic and explore the effects of climate change. They will even go into space!  I imagine robots like these exploring the dense atmospheres of Venus or Europa.  This is the challenge, and we are responding to that challenge!”

Writer: Jared Pike, jaredpike@purdue.edu, 765-496-0374

Source: Nina Mahmoudian, ninam@purdue.edu

Collaborative Mission Planning for Long-Term Operation Considering Energy Limitations

Bingxi Li, Brian R. Page, Barzin Moridian, Nina Mahmoudian

http://doi.org/10.1109/LRA.2020.3003881

ABSTRACT: Mobile robotics research and deployment is highly challenged by energy limitations, particularly in marine robotics applications. This challenge can be addressed by autonomous transfer and sharing of energy in addition to effective mission planning. Specifically, it is possible to overcome energy limitations in robotic missions using an optimization approach that can generate trajectories for both working robots and mobile chargers while adapting to environmental changes. Such a method must simultaneously optimize all trajectories in the robotic network to be able to maximize overall system efficiency. This letter presents a Genetic Algorithm based approach that is capable of solving this problem at a variety of scales, both in terms of the size of the mission area and the number of robots. The algorithm is capable of re-planning during operation, allowing for the mission to adapt to changing conditions and disturbances. The proposed approach has been validated in multiple simulation scenarios. Field experiments using an autonomous underwater vehicle and a surface vehicle verify feasibility of the generated trajectories. The simulation and experimental validation show that the approach efficiently generates feasible trajectories to minimize energy use when operating multi-robot networks.