Papers by Potiwat Ngamkajornwiwat
Proceedings of the 2017 ACM International Symposium on Wearable Computers
IEEE Access
Developing a controller that enables a walking robot to autonomously adapt its locomotion to navi... more 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 selfadaptive 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.
Animals to Animats 15: 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, 2018
Walking animals show a high level of proficiency in locomotion performance. This inspires the dev... more Walking animals show a high level of proficiency in locomotion performance. This inspires the development of legged robots to approach these living creatures in emulating their abilities to cope with uncertainty and to quickly react to changing environments in artificial systems. Central pattern generators (CPGs) and a hormone mechanism are promising methods that many researchers have applied to aid autonomous robots to perform effective adjustable locomo-tion. Based on these two mechanisms, we present here a bio-inspired walking robot which is controlled by a combination of multiple CPGs and an artificial hormone mechanism with multiple receptor stages to achieve online gait adaptation. The presented control technique aims to provide more dynamics for the artificial hormone mechanism with an inclusion of hormone-receptor binding effect. The testing scenarios on a simulated hexapod robot include walking performance efficiency and adaptability to unexpected damages. It is clearly seen that varying of hormone-receptor binding effect at each time step results in a better locomotion performance in terms of faster adaptation, more balanced locomo-tion, and self-organized gait generation. The result of our new control technique also supports online gait adaptability to deal with unexpected morphological changes.
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Papers by Potiwat Ngamkajornwiwat