By Kerry Benson<\/a> A rather unusual situation recently unfolded inside a laboratory\u2014moths playing a \u201cvideo game,\u201d flitting their wings as they navigated through a virtual forest displayed on a projector screen.<\/p>\n Each of the moths\u2019 movements was being tracked by Boston University engineers and University of Washington biologists. The team is leveraging the moth data to develop new navigational programs that provide autonomous aerial drones with a better sense of direction. The findings of their latest study, funded partly by a $7.5 million Department of Defense Multidisciplinary University Research Initiative (MURI) grant to develop self-navigating vehicles that can traverse land, sea, and air<\/a>, were recently published online in PLOS Computational Biology<\/a><\/em>.<\/p>\n The navigational challenge the moths helped the team overcome? Engineers working on control programs for autonomous vehicles have long struggled with what they call \u201cthe curse of dimensionality,\u201d which is that a drone navigating a multidimensional world has such an overwhelming number of options to consider at any given time (which direction it travels, how far, how fast, and so on) that it struggles to determine the best paths to take. Robots just aren\u2019t naturals at making those kinds of decisions\u2014but living beings are.<\/p>\n \u201cHumans and animals are ideal navigators; they\u2019re our experts,\u201d says Yannis Paschalidis<\/a>, a BU College of Engineering professor of electrical and computer, biomedical, and systems engineering, and a senior author on the new study.<\/p>\n \u201cThey can learn very fast and navigate quickly in very complex environments,\u201d he says. \u201cAnd if we can observe them and understand the [navigational strategies] that they are using, we can take these as our starting point. Then, with less computational effort, we can adapt those strategies to fit any new situation and any new drone that needs to navigate in a certain environment.\u201d<\/p>\n There\u2019s more than one way to navigate through an environment, but Paschalidis, who is also director of BU\u2019s Center for Information and Systems Engineering, and collaborators found that moths, like many other species, primarily rely on a visual perception pattern called optical flow. That means that as moths are flying, they sense their location by keeping track of the way their surroundings appear to move around them. Humans use this strategy, too; think of when you\u2019re in a car, approaching a sign along the road. At first, the sign is ahead of you, but as you drive past it, it seems to slide behind you. The sign hasn\u2019t physically moved from its location, but it has moved within your visual field, and this apparent movement tells your brain how fast you\u2019re going and where you\u2019re positioned relative to the sign.<\/p>\n Yet on its own, the principles of optical flow aren\u2019t enough to safely guide an autonomous drone around obstacles (or moths, it seems, who are notorious for incinerating themselves against burning hot lights). So Paschalidis and the team, including UW neurobiologist Thomas Daniel<\/a>, merged their learnings from analyzing moths with another navigational strategy that maps the specific location of obstacles. An animal like a bobcat, for instance, uses an obstacle detection strategy to analyze a broad area of forest, note where the trees are located, and plan out a path that avoids bumping into trees.<\/p>\n The researchers created two navigational programs to try out in drones in computer simulation: one program that encapsulated the optical flow navigation strategy of a moth; and a second, enhanced program that combined the moth\u2019s strategy with obstacle detection strategy. Using the programs, they challenged a simulated drone to navigate a variety of virtual forests.<\/p>\n The enhanced program navigated more effectively than the moth\u2019s strategy alone, but with a big catch: the enhanced program had to be readjusted to perform optimally in each new forest simulation. In contrast, the moth\u2019s strategy was more adaptable to new environments. While it didn\u2019t select the absolute best path through the different forest environments, its strategy could perform better across a wide range of scenarios without needing to be adjusted by human input.<\/p>\n \u201cThis robustness is driven by the fact that the [moth\u2019s navigational strategy] isn\u2019t optimized to do exceptionally well in a very specific setting. The strategy does well in many different settings simply because these animals need to be adaptable in order to survive,\u201d Paschalidis says.<\/p>\n Today\u2019s aerial drones are often optimized to perform specific missions with known parameters, but they aren\u2019t able to self-navigate unknown landscapes. Autonomous drones still need refining, but instilling them with more adaptable navigational strategies from living organisms could someday allow them to serve us in a whole new variety of contexts, from agricultural pursuits to remote rescue missions, and in uncharted rural areas as well as dynamic, obstacle-ridden cities.<\/p>\n
\nOriginally featured on The Brink<\/a><\/p>\n