Robots that can navigate various terrains both rapidly and efficiently could be highly advantageous, as they could successfully complete complex missions in challenging environments. For instance, these robots could help .
Robots that can navigate various terrains both rapidly and efficiently could be highly advantageous, as they could successfully complete complex missions in challenging environments. For instance, these robots could help to monitor complex natural environments, such as forests, or could search for survivors after natural disasters.
One of the most common types of robots designed to navigate varying terrains are legged robots, whose bodies are often inspired by the body structure of animals. To move swiftly in varying terrains, legged robots should be able to adapt their movements and gait-styles based on detected changes in their environmental conditions.
Researchers at the Higher Institute for Applied Science and Technology in Damascus, Syria, recently developed a new method to facilitate a smooth transition between the different gaits of a hexapod robot.
Their proposed gait control technique, introduced in a paper published in Heliyon, is based on so-called central pattern generators (CPGs), computational approaches that mimic biological CPGs. These are the neural networks underpinning many rhythmic movements performed by humans and animals (i.e., walking, swimming, jogging, etc.
