Abstract
Walking robots are considered as a promising solution for locomotion across irregular or rough terrain. While wheeled or tracked robots require flat surface like roads or driveways, walking robots can adapt to almost any terrain type. However, overcoming diverse terrain obstacles still remains a challenging task even for multi-legged robots with a high number of degrees of freedom. Here, we present a novel method for obstacle overcoming for walking robots based on the use of tactile sensors and generative recurrent neural network for positional error prediction. By using tactile sensors positioned on the front side of the legs, we demonstrate that a robot is able to successfully overcome obstacles close to robots height in the terrains of different complexity. The proposed method can be used by any type of a legged machine and can be considered as a step toward more advanced walking robot locomotion in unstructured terrain and uncertain environment.
Highlights
Most of earth’s terrain is uneven and full of different kinds of obstacles, which makes it difficult for traditional wheeled vehicles to reach their destination in natural environment if roads are not available [1]
We developed a method that can be used in an uncertain environment where the robot has no prior information about the positions of obstacles
We introduce the obstacle overcoming method based on the use of tactile sensors and describe its implementation in the walking hexapod robot along with the experimental setup for testing the obstacle overcoming method
Summary
Most of earth’s terrain is uneven and full of different kinds of obstacles, which makes it difficult for traditional wheeled vehicles to reach their destination in natural environment if roads are not available [1]. Such vehicles are especially demanded for a variety of critical applications such as rescue [2], disaster management [3] or border security patrol [4]. Obstacles must be detected and climbed over or bypassed with minimum error from the path trajectory This means that in order to have stable movement in complex dynamic environments, sensors and obstacle overcoming methods are necessary [12]
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