136 research outputs found
Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors
Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds
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
Multisensor Input for CPG-Based Sensory—Motor Coordination
International audienceThis paper describes a method for providing in real time a reliable synchronization signal for cyclical motions such as steady-state walking. The approach consists in estimating online a phase variable on the basis of several implicit central pattern generator associated with a set of sensors. These sensors can be of any kind, provided their output strongly reflects the timedmotion of a link. They can be, for example, spatial position or orientation sensors, or foot sole pressure sensors. The principle of the method is to use their outputs as inputs to nonlinear observers of modified Van der Pol oscillators that provide us with several independent estimations of the overall phase of the system. These estimations are then combined within a dynamical filter constituted of a Hopf oscillator. The resulting phase is a reliable indexing of the cyclic behavior of the system, which can finally be used as input to low-level controllers of a robot. Some results illustrate the efficiency of the approach, which can be used to control robots
Fast biped walking with a neuronal controller and physical computation
Biped walking remains a difficult problem and robot models can
greatly {facilitate} our understanding of the underlying
biomechanical principles as well as their neuronal control. The
goal of this study is to specifically demonstrate that stable
biped walking can be achieved by combining the physical properties
of the walking robot with a small, reflex-based neuronal network,
which is governed mainly by local sensor signals. This study shows
that human-like gaits emerge without {specific} position or
trajectory control and that the walker is able to compensate small
disturbances through its own dynamical properties. The reflexive
controller used here has the following characteristics, which are
different from earlier approaches: (1) Control is mainly local.
Hence, it uses only two signals (AEA=Anterior Extreme Angle and
GC=Ground Contact) which operate at the inter-joint level. All
other signals operate only at single joints. (2) Neither position
control nor trajectory tracking control is used. Instead, the
approximate nature of the local reflexes on each joint allows the
robot mechanics itself (e.g., its passive dynamics) to contribute
substantially to the overall gait trajectory computation. (3) The
motor control scheme used in the local reflexes of our robot is
more straightforward and has more biological plausibility than
that of other robots, because the outputs of the motorneurons in
our reflexive controller are directly driving the motors of the
joints, rather than working as references for position or velocity
control. As a consequence, the neural controller and the robot
mechanics are closely coupled as a neuro-mechanical system and
this study emphasises that dynamically stable biped walking gaits
emerge from the coupling between neural computation and physical
computation. This is demonstrated by different walking
experiments using two real robot as well as by a Poincar\'{e} map
analysis applied on a model of the robot in order to assess its
stability. In addition, this neuronal control structure allows the
use of a policy gradient reinforcement learning algorithm to tune
the parameters of the neurons in real-time, during walking. This
way the robot can reach a record-breaking walking speed of 3.5
leg-lengths per second after only a few minutes of online
learning, which is even comparable to the fastest relative speed
of human walking
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