A six-legged robot learns different walking styles, which it can then use to adapt to tricky terrain or even flee from the first signs of trouble
A six-legged robot with a 鈥減anic mode鈥� is proving to be a whizz at locomotion. When it finds it cannot move freely, the panicky droid scans randomly through the many walking gaits it has taught itself and selects the best for the terrain. That means it can free itself should it get stuck.
Getting robots to choose the right gait on differently textured surfaces and at varying inclinations is tough. Some robots use preprogrammed gaits, while others use software routines called genetic algorithms (GAs) to evolve the best gait on the fly. But both those methods need a lot of onboard computer power.
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Silke Steingrube of the in G枚ttingen, Germany, and colleagues took a different tack. They opted instead for a simple computer called a neural network, a computer system that uses feedback on previous decisions to learn from its experiences. Their robot has six triple-jointed legs each with several sensors. These feed information to the neural network, which then determines the most appropriate gait for the terrain, and adjusts the robot鈥檚 18 motors accordingly (see video).
No uphill struggle
Using its raft of sensors 鈥� which detect foot-contact pressure, light, sound, heat and the robot鈥檚 inclination 鈥� the robot can select the correct gait for uphill, downhill and various types of rough ground. By programming the robot to adopt the most energy-efficient gait possible, the researchers ensured it would switch gaits whenever its incline sensors were triggered. In tests, the robot taught itself 11 different walking styles. 鈥淭he technique should work equally well in four-legged, six-legged or even wheeled robots,鈥� says Steingrube.
It has a flight reflex, too: if a rear sensor detects, say a very high temperature, it interprets this as a threat. 鈥淭he neural network generates a fast, wave-like gait that is appropriate for running away,鈥� says Steingrube.
If the robot gets into difficulty, with a foot stuck in a hole, say, a number of sensors are stimulated. This creates a large input signal, which induces an unpredictable, chaotic output from the neural network, causing it to randomly choose one of its 11 gaits. In other words, the robot cycles through its repertoire until it frees itself.
Chaos reigns
, head of the Bristol Robotics Lab in the UK, is impressed. 鈥淚f you get stuck, going into what鈥檚 effectively a 鈥榯witching鈥� mode like this could indeed be useful,鈥� he says. 鈥淚t would be good to see if they can adapt this to help robots that have been damaged 鈥� lost a leg perhaps, or with a motor that is underperforming.鈥�
At the Robert Gordon University in Aberdeen, UK, roboticist has been using GAs to evolve robot gaits. 鈥淭his chaotic mechanism is an interesting idea and certainly merits more experimentation because many biological neural networks, like those in the autonomic nervous system, are known to exhibit chaotic or semi-chaotic behaviour,鈥� he says.
