66 research outputs found
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
Technical Report: A Contact-aware Feedback CPG System for Learning-based Locomotion Control in a Soft Snake Robot
Integrating contact-awareness into a soft snake robot and efficiently
controlling its locomotion in response to contact information present
significant challenges. This paper aims to solve contact-aware locomotion
problem of a soft snake robot through developing bio-inspired contact-aware
locomotion controllers. To provide effective contact information for the
controllers, we develop a scale covered sensor structure mimicking natural
snakes' \textit{scale sensilla}. In the design of control framework, our core
contribution is the development of a novel sensory feedback mechanism of the
Matsuoka central pattern generator (CPG) network. This mechanism allows the
Matsuoka CPG system to work like a "spine cord" in the whole contact-aware
control scheme, which simultaneously takes the stimuli including tonic input
signals from the "brain" (a goal-tracking locomotion controller) and sensory
feedback signals from the "reflex arc" (the contact reactive controller), and
generate rhythmic signals to effectively actuate the soft snake robot to
slither through densely allocated obstacles. In the design of the "reflex arc",
we develop two types of reactive controllers -- 1) a reinforcement learning
(RL) sensor regulator that learns to manipulate the sensory feedback inputs of
the CPG system, and 2) a local reflexive sensor-CPG network that directly
connects sensor readings and the CPG's feedback inputs in a special topology.
These two reactive controllers respectively facilitate two different
contact-aware locomotion control schemes. The two control schemes are tested
and evaluated in the soft snake robot, showing promising performance in the
contact-aware locomotion tasks. The experimental results also further verify
the benefit of Matsuoka CPG system in bio-inspired robot controller design.Comment: 17 pages, 19 figure
Controlling swimming and crawling in a fish robot using a central pattern generator
Online trajectory generation for robots with multiple degrees of freedom is still a difficult and unsolved problem, in particular for non-steady state locomotion, that is, when the robot has to move in a complex environment with continuous variations of the speed, direction, and type of locomotor behavior. In this article we address the problem of controlling the non-steady state swimming and crawling of a novel fish robot. For this, we have designed a control architecture based on a central pattern generator (CPG) implemented as a system of coupled nonlinear oscillators. The CPG, like its biological counterpart, can produce coordinated patterns of rhythmic activity while being modulated by simple control parameters. To test our controller, we designed BoxyBot, a simple fish robot with three actuated fins capable of swimming in water and crawling on firm ground. Using the CPG model, the robot is capable of performing and switching between a variety of different locomotor behaviors such as swimming forwards, swimming backwards, turning, rolling, moving upwards/downwards, and crawling. These behaviors are triggered and modulated by sensory input provided by light, water, and touch sensors. Results are presented demonstrating the agility of the robot and interesting properties of a CPG-based control approach such as stability of the rhythmic patterns due to limit cycle behavior, and the production of smooth trajectories despite abrupt changes of control parameters. The robot is currently used in a temporary 20-month long exhibition at the EPFL. We present the hardware setup that was designed for the exhibition, and the type of interactions with the control system that allow visitors to influence the behavior of the robot. The exhibition is useful to test the robustness of the robot for long term use, and to demonstrate the suitability of the CPG-based approach for interactive control with a human in the loop. This article is an extended version of an article presented at BioRob2006 the first IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronic
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
Design and control of amphibious robots with multiple degrees of freedom
This thesis presents the design and realization of two generations of robot elements that can be assembled together to construct amphibious mobile robots. These elements, designed to be individually waterproof and having their own battery, motor controller, and motor, have been used to actually construct a snake, a boxfish and a salamander robot. Central pattern generator (CPG) models inspired from those found in vertebrates have been used for online trajectory generation on these robots and implemented on their onboard locomotion controllers. CPGs proved to be an interesting way of controlling complex robots, providing a simple interface which hides the complexity of the robot to the end user. Online learning algorithms that can be used to dynamically adapt the locomotion parameters to the environment have been implemented. Finally, this work also shows how robotics can be a useful tool to verify biological hypotheses. For instance, the salamander robot has been used to test a model of CPG for salamander locomotion
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