Hexapod robots, designed to navigate various terrains, hold great potential for complex missions in challenging environments like monitoring forests or searching for survivors after natural disasters. To efficiently move across diverse terrains, these robots must adapt their gait based on changing environmental conditions.
Researchers at the Higher Institute for Applied Science and Technology in Damascus, Syria, have developed a new method to facilitate smooth transitions between different gaits in hexapod robots. This method, described in a paper published in Heliyon, utilizes central pattern generators (CPGs)—computational models that mimic the neural networks responsible for rhythmic movements in humans and animals.
“Our recent publication is a foundational component of a larger project that aims to revolutionize the locomotion control of hexapod robots,” said Kifah Helal, the paper’s corresponding author. “While machine learning techniques have not yet been integrated, our architecture is designed with future machine learning applications in mind, ensuring significant enhancements in malfunction compensation.”
Helal and his team first designed and simulated a hexapod robot to test their control architecture based on CPGs. This architecture allows each leg of the robot to be governed by distinct rhythmic signals, with the essence of different gaits lying in the phase differences between these signals. The novel interaction design among the oscillators ensures seamless gait transitions.
The researchers also developed a workspace trajectory generator, a tool that translates the oscillators’ outputs into effective foot trajectories for the hexapod robot. Initial tests showed that this control architecture enables stable, efficient, and swift gait changes in both simulated and real hexapod robots.
We also validated a mapping function that ensures the robot’s foot trajectory remains effective throughout these transitions.
