A new method to achieve smooth walking transition in hexapod robots

Robots that can navigate various terrains both quickly and efficiently can be very advantageous, as they can successfully perform complex missions in challenging environments. For example, these robots can 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. To move rapidly in different terrains, legged robots must be able to adapt their movements and walking 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 different gaits of a hexapod robot.

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

“Our latest publication is a fundamental component of a larger project that aims 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 designed lays the foundation for such advanced applications. Our methodology is designed with the future integration of machine learning in mind, ensuring that when implemented, it will increase substantially compensating for dysfunction.”

Helal and his colleagues first set out to design and simulate a six-legged robot (hexapod). This simulated robotic platform was then used to test their proposed CPG-based control architecture.

“Our control method uses the principles of CPGs where each leg of the hexapod robot is driven by a separate 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 the new design of the interaction between the oscillators, ensuring smooth transitions in the gait.”

Helal and his colleagues also developed a workspace trajectory generator, a computational tool that translates the outputs of oscillators built into a hexapod robot into trajectories for its legs, ensuring these trajectories remain effective during transitions. In initial tests, their proposed control architecture was found to enable 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 blend of smoothness and speed of the transition,” said Helal. “Fundamentally, it’s the fusion of fluidity and speed that sets our work apart from other previous efforts. We also validated a mapping function that ensures the robot’s leg trajectory remains effective during these transitions.”

The new architecture introduced by this team of researchers may soon be tested in further experiments and applied to other legged robots to allow them to rapidly adapt to environmental changes while maintaining their agility.

In their future studies, Helal and his colleagues plan to further improve their method, to address potential malfunctions and further increase its performance when robots encounter particularly challenging terrain.

“Looking ahead, we plan to delve deeper into machine learning to further improve our robot’s environmental adaptability,” Helal added. “We are particularly excited about exploring dysfunction compensation and the integration of pain sensitivity as feedback mechanisms.

“These advances 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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