Bio-Inspired Adaptive Locomotion Control System for Online Adaptation of a Walking Robot on Complex Terrains

Abstract

Developing a controller that enables a walking robot to autonomously adapt its locomotion to navigate unknown complex terrains is difficult, and the methods developed to address this problem typically require robot kinematics with arduous parameter tuning or machine learning techniques that require several trials or repetitions. To overcome this limitation, in this paper, we present continuous, online, and self-adaptive locomotion control inspired by biological control systems, including neural control and hormone systems. The control approach integrates our existing modular neural locomotion control (MNLC) and a newly introduced artificial hormone mechanism (AHM). While the MNLC can generate various gaits through its modulatory input, the AHM, which replicates the endocrine system, adapts to rapid changes in online walking frequency and gait in response to different complex terrains. The control approach is evaluated on an insect-like hexapod robot with 18 degrees of freedom. We provide the results in three sections. First, we demonstrate the adaptability of the robot with the proposed artificial hormones. Second, we compare the performance of two robots with and without artificial hormones while walking on different complex terrains using three performance indexes (stability, harmony, and displacement). Third, we evaluate real-time online adaptations in the real world through real robot walking on different unknown terrains. The experimental results demonstrate that the robot with the proposed artificial hormones does not require several learning trials to adapt its locomotion. Instead, it can continuously adapt its locomotion online, thereby providing greater success and higher performance than other techniques when walking on all terrains. INDEX TERMS Adaptive behaviors, artificial hormones, central pattern generators, locomotion control, neural networks, walking machines.

iCrawl: An Inchworm-Inspired Crawling Robot

“…Furthermore, it is critical that the robot be self-learning for better path planning when negotiating obstacles in an unknown cluttered environment. In the future, we intend to use a neural controller [38], [39,41] to coordinate all the sensorimotor processes simultaneously. It will involve self-learning for autonomous crawling and the appropriate selection of gaits, depending on the surface, and the desired locomotion type (crawling, climbing, obstacle avoidance, etc.).…”

Insect-Inspired Robots: Bridging Biological and Artificial Systems

“…While Walknet control shows impressive performance for versatile and adaptive robot behavior generation (locomotion and object manipulation), it may lead to unstable robot behavior or failure in cases of sensory failure. Therefore, a combination of CPG-and reflex-based control has been actively investigated and various types of this combination have been developed [142,[197][198][199][200][201][202][203]. For instance, CPG-based control with a sensory event mistiming detection method and reflexes was proposed [201].…”

“…This robot can still perform basic locomotion even without the sensory feedback. Recently, an artificial hormone mechanism (AHM) was applied to the adaptive neural locomotion control [202,203]. AHM, which replicates the endocrine system, can continuously online adapt neural locomotion control parameters (lifelong continuous adaptation) for walking on different complex terrains (e.g., mesa terrain, ramp-up and -down terrains, rough terrain, terraced terrain, compliant terrain with different softness levels, and loose terrain).…”

Adaptive Interlimb Coordination Mechanism for Hexapod Locomotion Based on Active Load Sensing

“…For another example, Owaki et al proposed a simple CPG model where one phase oscillator controls each limb's stride motion and demonstrated that phase modulations depending on loads of limbs contribute to generating various gaits locomotion speed and leg amputation (Owaki et al, 2017 ). Regarding adaption to uneven environments, however, previous models still require recruiting a large number of neural components for modulating interlimb coordination depending on situations (Durr, 2001 ; Bläsing, 2006 ; Schilling et al, 2013a , b ; Ngamkajornwiwat et al, 2020 ). This is because the limb without a stable foothold should adaptively change its foot trajectory and frequency comparing other limbs to search steady footholds.…”