Adaptive Locomotion Control of a Hexapod Robot via Bio-Inspired Learning

Abstract

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.

Trends in the Control of Hexapod Robots: A Survey

“…Hence, these systems already provide versatile controllers to deal with the possible variation of the coefficient of friction of the ground. Despite that, the study of the behavior of hexapods in soft terrain is less frequent [28,29,32,53,54,62]. The ability to overcome different terrain topologies also considered scenarios in which the hexapods had to detect and pass over steps and ditches.…”

“…Nonetheless, the manual adjustment of the neural oscillators or the SNN is considered a disadvantage, due to being a time-consuming process. Therefore, some research published in the past five years studied the implementation of RL to self-learn how to generate locomotion based on the interactions of the robot with the environment [7,[60][61][62][63][64][65]. The advantage of this approach is not requiring previous knowledge about the robot or its surroundings, since it learns how to generate gaits through a trial-and-error process.…”

“…Despite the few publications concerning this topic, RL has been successfully implemented to generate novel gaits when internal failures occur, which increases the level of autonomy of hexapods. Nonetheless, only the authors of [62] considered the possibility of using this learning method to adjust a bio-inspired controller for outdoor environments. The stability of the outputs generated by neural oscillators combined with RL can be an emergent solution for the generation of autonomous adaptive locomotion for unstructured environments.…”

“…In this case, the algorithm evaluates its results at the end of each episode, and there is no assurance that the agent visits all states, which can provide a greedy policy. The generation of adaptive gaits is also discussed in [62]. The proposed method contains a CPG model with two layers.…”

A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexapod Robots

“…Bai et al presented a novel CPG (center pattern generator)based gait generation for a curved-leg hexapod robot and enabled the robot to achieve smooth and continuous mutual gait transitions [18]. Ouyang presented an adaptive locomotion control approach for a hexapod robot by using a 3D two-layer artificial center pattern generator (CPG) network [19]. These methods all obtained promising results in some aspects, but they could not confront any complex scene because of the limited gait patterns.…”