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A walking robot called human: lessons to be learned from neural control of locomotion
Journal of …, 2002
From what we know at present with respect to the neural control of walking, it can be concluded that an optimal biologically inspired robot could have the following features. The limbs should include several joints in which position changes can be obtained by actuators across the joints. The control of mono-and biarticular actuators should occur at least at three levels: one at direct control of the actuators (equivalent to motoneuron level), the second at indirect control acting at a level which controls whole limb movement (flexion or extension) and the third at a still higher level controlling the interlimb coordination. The limb level circuits should be able to produce alternating flexion and extension movements in the limb by means of coupled oscillator flexor and extensor parts which are mutually inhibitory. The interlimb control level should be able to command the various limb control centers. All three control levels should have some basic feedback circuits but the most essential one is needed at the limb control level and concerns the decision to either flex or extend a given limb. The decision to activate the extensor part of the limb oscillator has to be based on feedback signalling the onset of loading of the limb involved. This should be signalled by means of load sensors in the limb. The decision to activate the flexor part of the limb oscillator has to depend on various types of feedback. The most important requirement is that flexion should only occur when the limb concerned is no longer loaded above a given threshold. The rule for the initiation of limb flexion can be made more robust by adding the requirement that position at the base of the limb (''hip'') should be within a normal end of stance phase range. Hence, human locomotion is thought to use a number of principles which simplify control, just as in other species such as the cat. It is suggested that cat and human locomotion are good models to learn from when designing efficient walking robots.
Some results in passive-dynamic walking
1998
Human walking can be approximated as a mechanical process governed by Newton's laws of motion, and not controlled. Tad McGeer first demonstrated, and we have confirmed, that a two-dimensional legged mechanism with four moving parts can exhibit stable, human-like walking on a range of shallow slopes with no actuation and no control (energy lost in friction and collisions is recovered from gravity). More recently, we have found a simple walking mechanism that also balances from side to side. There is much that might be understood about walking by considering it as a natural motion of a simple uncontrolled and unpowered dynamical system, or a passive-dynamic system. As seen from a control perspective, our work largely involves investigation of control parameters which are physical properties rather than the traditional active-control parameters (such as feedback gains, neural net parameters, genetic algorithm reward schemes, etc.). We are testing the hypothesis that human walking is based on an uncontrolled mechanical process by designing, building, and studying uncontrolled or minimallycontrolled walking devices and seeing how well they mimic human motion. * This Euromech conference paper is based largely on a funding proposal submitted to the National Science Foundation in December 1997 .
A Bio-Inspired Approach to the Realization of Sustained Humanoid Motion
2012
Abstract This paper overviews some author's biomechanical inspiration for the development of an approach which enables the realization of bipedal artificial motion. First, we introduce the notion of dynamic balance, which is a basic prerequisite for the realization of any task by humanoids. Then, as ground reference points, important indicators of a humanoid's state were introduced and discussed. Particular attention was paid to ZMP, which is the most important indicator of robot dynamic balance.
There are several problems which arise when using a standard ZMP-based pattern generator to control an intrinsically compliant humanoid robot like COMAN. We present two techniques: pelvis forward trajectory smoothing and polynomial admittance gain modulation, which make it possible to use the conventional pattern generator to control such passively compliant robots. The former method modifies the reference of the pelvis trajectory to counteract its overshooting caused by the compliance of the legs. The latter method is meant to decrease the impact during initial contact and decrease the error between the foot position and original reference during the mid-stance caused by the use of admittance control. We explain details of both of the methods and show results from walking experiments with and without the controls, proving their effectiveness.
Semi-Passive Dynamic Walking Approach for Bipedal Humanoid Robot Based on Dynamic Simulation
Biped Robots, 2011
The research on the principles of legged locomotion is an interdisciplinary endeavor. Such principles are coming together from research in biomechanics, neuroscience, control theory, mechanical design, and artificial intelligence. Such research can help us understand human and animal locomotion in implementing useful legged vehicles. There are three main reasons for exploring the legged locomotion. The first reason is to develop vehicles that can move on uneven and rough terrain. Vehicles with wheels can only move on prepared surfaces such as roads and rails; however, most surfaces are not paved. The second reason is to understand human and animal locomotion mechanics. The study of the mechanisms and principles of control found in nature can help us develop better legged vehicles. The third reason which motivated the study of legged locomotion is the need to build artificial legs for amputees. Although some effective artificial legs have been built to date, more indepth research is required to fully understand the mechanisms and movements necessary to substitute the actual limbs. The research in this paper concerns a group of legged robots known as bipedal walking robots. Research on this subject has a long history; however, it is only in the last two decades that successful experimental prototypes have been developed. The vast majority of humanoid and bipedal robots control the joint angle profiles to carry out the locomotion. Active walking robots (robots with actuators) can do the above task with reasonable speed and position accuracy at the cost of high control efforts, low efficiencies, and most of the time unnatural gaits. WABIAN-2R is among the most successful bipedal walking humanoid robots. In spite of the extensive research on humanoid robots, the actions of walking, running, jumping and manipulation are still difficult for robots. Passive-dynamic walking robots have been developed by researchers to mimic human walking. The main goal of building passive-dynamic walking robots is to study the role of natural dynamics in bipedal walking. Passive-dynamic walkers use gravitational energy to walk down a ramp without any actuators. They are energy efficient but have weak stability in the gait. In addition, the major cause of the energy loss in the current passive-dynamic www.intechopen.com
Learning from demonstration and adaptation of biped locomotion
2004
In this paper, we introduce a framework for learning biped locomotion using dynamical movement primitives based on non-linear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements.
Biologically inspired joint control for a humanoid robot
2004
The GuRoo is a 1.2m tall, 23 degree of freedom humanoid constructed at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRoo project is the development of appropriate learning strategies for control and coordination of the robot's many joints. The development of learning strategies is seen as a way to side-step the inherent intricacy of modeling a highly non-linear multi-DOF biped robot. This paper outlines the approach taken to generate an appropriate control scheme for the joints of the GuRoo. The paper demonstrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-forward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on a Cerebellar Modeled Articulated Controller (CMAC) architecture. Results from tests on the GuRoo platform show that both systems provide improvements in stability and joint control tracking. This paper presents a biologically inspired control scheme as a two-stage approach. A Genetic Algorithm determines a set of feedback control parameters based on a fitness function minimizing both tracking error and vibration experienced by each joint. This process is performed offline on a simulator. The tuned feedback system is augmented with a feedforward system that uses an on-line learning algorithm based on a Cerebellar Modeled Articulated Controller (CMAC) neural network. The GuRoo humanoid robot with its high degree of freedom and non-linear dynamics forms a suitable platform to apply the system.
A framework for learning biped locomotion with dynamical movement primitives
2004
This article summarizes our framework for learning biped locomotion using dynamical movement primitives based on nonlinear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements.