Literature DB >> 25811228

Goal-directed multimodal locomotion through coupling between mechanical and attractor selection dynamics.

S G Nurzaman1, X Yu, Y Kim, F Iida.   

Abstract

One of the most significant challenges in bio-inspired robotics is how to realize and take advantage of multimodal locomotion, which may help robots perform a variety of tasks adaptively in different environments. In order to address the challenge properly, it is important to notice that locomotion dynamics are the result of interactions between a particular internal control structure, the mechanical dynamics and the environment. From this perspective, this paper presents an approach to enable a robot to take advantage of its multiple locomotion modes by coupling the mechanical dynamics of the robot with an internal control structure known as an attractor selection model. The robot used is a curved-beam hopping robot; this robot, despite its simple actuation method, possesses rich and complex mechanical dynamics that are dependent on its interactions with the environment. Through dynamical coupling, we will show how this robot performs goal-directed locomotion by gracefully shifting between different locomotion modes regulated by sensory input, the robot's mechanical dynamics and an internally generated perturbation. The efficacy of the approach is validated and discussed based on the simulation and on real-world experiments.

Mesh:

Year:  2015        PMID: 25811228     DOI: 10.1088/1748-3190/10/2/025004

Source DB:  PubMed          Journal:  Bioinspir Biomim        ISSN: 1748-3182            Impact factor:   2.956


  4 in total

Review 1.  Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective.

Authors:  Fumiya Iida; Surya G Nurzaman
Journal:  Interface Focus       Date:  2016-08-06       Impact factor: 3.906

2.  An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems.

Authors:  Fernando Rosas; Pedro A M Mediano; Martín Ugarte; Henrik J Jensen
Journal:  Entropy (Basel)       Date:  2018-10-16       Impact factor: 2.524

3.  Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy.

Authors:  Daniel F B Haeufle; Katrin Stollenmaier; Isabelle Heinrich; Syn Schmitt; Keyan Ghazi-Zahedi
Journal:  Front Robot AI       Date:  2020-10-21

4.  Cognitive swarming in complex environments with attractor dynamics and oscillatory computing.

Authors:  Joseph D Monaco; Grace M Hwang; Kevin M Schultz; Kechen Zhang
Journal:  Biol Cybern       Date:  2020-03-31       Impact factor: 2.086

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.