A new method to achieve smooth walking transitions in hexapod robots

Robots that can move quickly and efficiently across different terrains could be very beneficial as they could successfully complete complex missions in challenging environments. For example, these robots could help monitor complex natural environments, such as forests, or search for survivors after natural disasters.

One of the most common types of robots designed to navigate various terrains are legged robots, whose bodies are often inspired by the body structure of animals. In order to move quickly in different terrains, legged robots should be able to adapt their movements and walking styles based on detected changes in environmental conditions.

Researchers at the Higher Institute of Applied Science and Technology in Damascus, Syria, recently developed a new method to facilitate a smooth transition between different walking modes for a hexapod robot.

Their proposed gait control technique, presented in a paper published in Heliyon, is based on so-called central pattern generators (CPGs), computational approaches that mimic biological CPGs. They are neural networks that underlie many rhythmic movements performed by humans and animals (ie walking, swimming, jogging, etc.).

“Our recent publication is a core component of a larger project to revolutionize the motion control of hexapod robots,” Kifah Helal, corresponding author of the paper, told Tech Xplore.

“While machine learning techniques have not yet been integrated, the architecture we proposed lays the foundation for such advanced applications. Our methodology is built with future machine learning integration in mind, ensuring that it will significantly improve error compensation once implemented.”

Helal and his colleagues first set about designing and simulating a six-legged (hexapod) robot. This simulated robotic platform was then used to test the proposed CPG-based control architecture.

“Our control method uses CPG principles, where each leg of the hexapod robot is controlled by a different rhythmic signal,” explained Helal. “The essence of the different gaits lies in the phase differences between these signals. The main contribution of our paper is a new design of the interaction between the oscillators that ensures smooth gait transitions.”

Helal and his colleagues also developed a workspace trajectory generator, a computational tool that translates the outputs of oscillators integrated in the hexapod robot into trajectories for its feet, ensuring that those trajectories remain effective during transitions. In initial tests, it was found that their proposed control architecture enables stable, efficient, and fast gait changes in both a simulated and a real hexapod robot.

“The most striking results of our research are the harmonious combination of fluidity and transition speed,” Helal said. “Essentially, it’s the combination of fluidity and speed that sets our work apart from other previous efforts. We’ve also verified a mapping feature that ensures the robot’s foot trajectory remains efficient during these transitions.”

The new architecture presented by this team of researchers could soon be tested in further experiments and applied to other legged robots to quickly adapt to environmental changes while maintaining their agility.

In their next studies, Helal and his colleagues plan to further refine their method, address potential glitches, and further increase its performance when the robots encounter particularly challenging terrain.

“Going forward, we plan to delve deeper into machine learning to further improve our robot’s adaptability to the environment,” added Helal. “We are particularly excited to explore perturbation compensation and the integration of pain sensing as feedback mechanisms.

“These improvements will not only improve the robot’s interaction with its environment, but also pave the way for more autonomous and resilient robotic systems.”

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