Hexapod Robot Moves By Controlling Chaos
Posted In: Bernstein Center for Computational Neuroscience, Chaos, Controlling, gait, hexapod, Marc Timme, Max Planck Institute for Dynamics and Self-Organization, Moves, Poramate Manoonpong, robot, terrain, walking
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Robots don’t generally have muscles, therefore they can’t rely on muscle memory to help them more easily complete repetitive tasks like we do. This can be a problem for autonomous robots, since they may have to accommodate changing terrain in real time or else get stuck or lose their balance.
So how do you get around that problem? One way is to create a robot that can process information from a variety of sensors positioned near the “legs” and identify different patterns as it moves. Some scientists use small neural circuits called “central pattern generators” (CPG) to create walking robots that are aware of their surroundings. One of the challenges is that the robot generally needs a separate CPG for each leg to sense obstacles and take the necessary action.
Bernstein Center for Computational Neuroscience researcher Poramate Manoonpong and Max Planck Institute for Dynamics and Self-Organization researcher Marc Timme are leading a project where they have created a six-legged robot with one CPG that can switch gaits depending on the obstacles it encounters. The robot manipulates the sensor inputs into periodic patterns (instead of chaotic ones) that determine its gait. In the future, the robot will also be equipped with a memory device that will allow it to complete movements even after the sensory input ceases.