Robot has self-adjusting gait

Scientists at the Georg-August-University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization have created a six-legged autonomous walking robot that can adjust its gait using so-called chaos control.

In humans and animals, recurring movements such as walking are controlled by neural circuits called central pattern generators (CPG) and scientists have used this principle in the development of walking machines.

Currently, a walking robot may receive information about its environment via several sensors before selecting the gait controlling CPG most appropriate for the situation it is in. In this scenario, a separate CPG is needed for every gait.

The robot developed by the Göttingen scientists manages the same task with one CPG that generates entirely different gaits and which can switch between these gaits flexibly.

This CPG is a network consisting of two circuit elements. The secret of its functioning lies in the ‘chaos control’. If uncontrolled, the CPG produces a chaotic activity pattern, but this can be controlled by the sensor inputs into periodic patterns that determine the gait. Depending on the sensory input signal, different patterns – and different gaits – are generated.

The connection between sensory properties and CPG can either be pre-programmed or learned by the robot.

According to the Max Planck Institute, the robot can autonomously learn to walk up a slope with as little energy input as possible. Once the robot reaches a slope, a sensor shows that the energy consumption is too high.

At this stage, the connection between the sensor and the control input of the CPG is varied until a gait is found that allows the robot to consume less energy. Once the right connections have been established, the robot has learnt the relation between slope and gait. When it tries to climb the hill a second time, it adopts the appropriate gait.

In the future, it is anticipated the robot will be equipped with a memory device that will enable it to complete movements after the sensory input ceases to exist. In order to walk over an obstacle, for instance, the robot would have to take a large step with each of its six legs.

‘Currently, the robot would not be able to handle this task – as soon as the obstacle is out of sight, it no longer knows which gait to use,’ said Marc Timme, a scientist at the Max Planck Institute for Dynamics and Self-Organization. ‘Once the robot is equipped with a motor memory, it will be capable to use foresight and plan its movements.’