Autonomous robot improves environmental sampling at sea

Autonomous system improves environmental sampling at sea
Researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) have invented an autonomous robotic system that sniffs out the most scientifically interesting, but difficult-to-find, sampling spots in vast, unexplored waters. (MIT)

Researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) have invented an autonomous robotic system that sniffs out the most scientifically interesting, but difficult-to-find, sampling spots in vast, unexplored waters, according to an article on MIT’S website.

Environmental scientists are often interested in gathering samples at the most interesting locations, or “maxima,” in an environment. But efforts to deploy maximum-seeking robots suffer from efficiency and accuracy issues. For example, chemicals can get trapped and accumulate in crevices far from a source. Robots may identify those high-concentration spots as the source yet be nowhere close.

In a paper being presented at the International Conference on Intelligent Robots and Systems (IROS), the researchers describe “PLUMES,” a system that enables autonomous mobile robots to zero in on a maximum far faster and more efficiently. PLUMES leverages probabilistic techniques to predict which paths are likely to lead to the maximum, while navigating obstacles, shifting currents, and other variables. As it collects samples, it weighs what it’s learned to determine whether to continue down on the same path or search elsewhere—which may harbor more valuable samples.

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Importantly, PLUMES reaches its destination without ever getting trapped in those tricky high-concentration spots. “That’s important, because it’s easy to think you’ve found gold, but really you’ve found fool’s gold,” said co-first author Victoria Preston, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and in the MIT-WHOI Joint Program.

The researchers built a PLUMES-powered robotic boat that successfully detected the most exposed coral head in the Bellairs Fringing Reef in Barbados—located in the shallowest spot—which is useful for studying how sun exposure impacts coral organisms. In 100 simulated trials in diverse underwater environments, a virtual PLUMES robot also consistently collected seven to eight times more samples of maxima than traditional coverage methods in allotted time frames.

“PLUMES does the minimal amount of exploration necessary to find the maximum and then concentrates quickly on collecting valuable samples there,” said co-first author Genevieve Flaspohler, a PhD student in CSAIL and the MIT-WHOI Joint Program.

A key insight of PLUMES was using techniques from probability to reason about navigating the notoriously complex tradeoff between exploiting what’s learned about the environment and exploring unknown areas that may be more valuable.

Dropped into a new environment, a PLUMES-powered robot uses a probabilistic statistical model called a Gaussian process to make predictions about environmental variables, such as chemical concentrations, and estimate sensing uncertainties. PLUMES then generates a distribution of possible paths the robot can take, and uses the estimated values and uncertainties to rank each path by how well it allows the robot to explore and exploit.

Initially, PLUMES chooses paths that randomly explore the environment. Each sample, however, provides new information about the targeted values in the surrounding environment. The Gaussian process model exploits that data to narrow down possible paths the robot can follow from its given position to sample from locations with even higher value. PLUMES uses a novel objective function—commonly used in machine-learning to maximize a reward—to make the call of whether the robot should exploit past knowledge or explore the new area.

The robot makes the decision on where to collect the next sample by leveraging a modified version of Monte Carlo Tree Search (MCTS), a path-planning technique popularized for powering artificial-intelligence systems that master complex games, such as Go and Chess. The root of this MCTS decision tree starts with a “belief” node, which is the next immediate step the robot can take. This node contains the entire history of the robot’s actions and observations up until that point. Then, the system expands the tree from the root into new lines and nodes, looking over several steps of future actions that lead to explored and unexplored areas.

Researchers are now collaborating with scientists at WHOI to use PLUMES-powered robots to localize chemical plumes at volcanic sites and study methane releases in melting coastal estuaries in the Arctic. Scientists are interested in the source of chemical gases released into the atmosphere, but these test sites can span hundreds of square miles.

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