112 research outputs found
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Simulation and robotics studies of salamander locomotion: Applying neurobiological principles to the control of locomotion in robots
This article presents a project that aims at understanding the neural circuitry controlling salamander locomotion, and developing an amphibious salamander-like robot capable of replicating its bimodal locomotion, namely swimming and terrestrial walking. The controllers of the robot are central pattern generator models inspired by the salamander's locomotion control network. The goal of the project is twofold: (1) to use robots as tools for gaining a better understanding of locomotion control in vertebrates and (2) to develop new robot and control technologies for developing agile and adaptive outdoor robots. The article has four parts. We first describe the motivations behind the project. We then present neuromechanical simulation studies of locomotion control in salamanders. This is followed by a description of the current stage of the robotic developments. We conclude the article with a discussion on the usefulness of robots in neuroscience research with a special focus on locomotion contro
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
Reinforcement Learning of CPG-regulated Locomotion Controller for a Soft Snake Robot
Intelligent control of soft robots is challenging due to the nonlinear and
difficult-to-model dynamics. One promising model-free approach for soft robot
control is reinforcement learning (RL). However, model-free RL methods tend to
be computationally expensive and data-inefficient and may not yield natural and
smooth locomotion patterns for soft robots. In this work, we develop a
bio-inspired design of a learning-based goal-tracking controller for a soft
snake robot. The controller is composed of two modules: An RL module for
learning goal-tracking behaviors given the unmodeled and stochastic dynamics of
the robot, and a central pattern generator (CPG) with the Matsuoka oscillators
for generating stable and diverse locomotion patterns. We theoretically
investigate the maneuverability of Matsuoka CPG's oscillation bias, frequency,
and amplitude for steering control, velocity control, and sim-to-real
adaptation of the soft snake robot. Based on this analysis, we proposed a
composition of RL and CPG modules such that the RL module regulates the tonic
inputs to the CPG system given state feedback from the robot, and the output of
the CPG module is then transformed into pressure inputs to pneumatic actuators
of the soft snake robot. This design allows the RL agent to naturally learn to
entrain the desired locomotion patterns determined by the CPG maneuverability.
We validated the optimality and robustness of the control design in both
simulation and real experiments, and performed extensive comparisons with
state-of-art RL methods to demonstrate the benefit of our bio-inspired control
design.Comment: 20 pages, 17 figures, 4 tables, in IEEE Transactions on Robotic
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