Intelligent Approaches in Locomotion - A Review <s> Method <s> A method is presented for the generation of a free gait for the straight-line motion of a quadruped walking machine. It uses a heuristic graph search procedure based on the A* algorithm. The method essentially looks into the consequences of a move to a certain depth before actually committing to it. Deadlocks and inefficiencies are thus sensed well in advance and avoided. > <s> BIB001 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> The authors have developed a movement pattern generator, using an artificial neural network (ANN) for generating periodic movement trajectories. This model is based on the concept of 'central pattern generators'. Jordan's (1986) sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties, such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine. > <s> BIB002 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but th... <s> BIB003 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> The learning of complex control behaviour of autonomous mobile robots is one of the actual research topics. In this article an intelligent control architecture is presented which integrates learning methods and available domain knowledge. This control architecture is based on Reinforcement Learning and allows continuous input and output parameters, hierarchical learning, multiple goals, self-organized topology of the used networks and online learning. As a testbed this architecture is applied to the six-legged walking machine LAURON to learn leg control and leg coordination. <s> BIB004 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract In this paper a hierarchical control architecture for a six-legged walking machine is presented. The basic components of this architecture are neural networks which are taught in using examples of the control process. It is shown how the basic components “leg control” and “leg coordination” have been implemented by recurrent and feedforward networks respectively. The teaching process and the tests of the walking behaviour have mainly been done in a simulation system. First tests of the leg control on our real walking machine LAURON are also described. <s> BIB005 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents some results from a study of biped dynamic walking using reinforcement learning. During this study a hardware biped robot was built, a new reinforcement learning algorithm as well as a new learning architecture were developed. The biped learned dynamic walking without any previous knowledge about its dynamic model. The self scaling reinforcement (SSR) learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. The learning architecture was developed in order to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of new modules that represent new knowledge, or new requirements for the desired task. <s> BIB006 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents how neural swimming controllers for a simulated lamprey can be developed using evolutionary algorithms. A genetic algorithm is used for evolving the architecture of a connectionist model which determines the muscular activity of a simulated body in interaction with water. This work is inspired by the biological model developed by Ekeberg which repro duces the central pattern generator observed in the real lamprey (Ekeberg, 1993). In evolving artificial controllers, we demonstrate that a genetic algorithm can be an interesting design tech nique for neural controllers and that there exist alternative solutions to the biological connectiv ity. A variety of neural controllers are evolved which can produce the pattern of oscillations necessary for swimming. These patterns can be modulated through the external excitation ap plied to the network in order to vary the speed and the direction of swimming. The best evolved controllers cover larger ranges of frequencies, phase lags and speeds of s... <s> BIB007 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Bipedal locomotion is an important hallmark of human evolution. Despite of complex control systems, human locomotion is characterized by smooth, regular, and repeating movements. Therefore, there is the potential for applying human locomotion strategies and any knowledge available to the biped control. In order to make the most use of the information available, a linguistic-numerical integration-based biped control method is proposed in this paper. The numerical data from biped measuring instruments, and the linguistic rules obtained from intuitive walking knowledge and biomechanics study have been classified into four categories: direct rules, direct data, indirect rules, and indirect data. Based on inverse learning and data fusion theory, two simple and intuitive integration schemes are proposed to integrate linguistic and numerical information with various forms, such as direct and indirect. One is neurofuzzy-based integration, and another is fuzzy rules extraction-based integration. The simulation results show that the biped gait and joint control performance can be significantly improved by the prescribed synergy method-based neurofuzzy gait synthesis and fuzzy rules extraction-based joint control strategies using linguistic and numerical integrated information. <s> BIB008 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper investigates ballistic motions in walking quadrupeds on a horizontal plane. The study is carried out on a quadruped consisting of a body and four identical two-link legs. Each leg has a knee joint and is connected to the body by a haunch joint. Three types of quadruped gaits, bound, amble, and trot, are studied. None of these gaits complies with a flight phase, but they all involve simultaneous and identical motion of two legs. Muscle activities are commonly believed to alternate with periods of relaxation. Our study, therefore, assumes that the swing phase is ballistic, i.e., no active control torque is exerted. Ballistic motion is achieved through appropriate initial velocities. These velocities result from impulsive active control torques and ground reactions exerted at the boundary instants of the single support phase. Natural ballistic motions are shown to exist for the three gaits and for each valid walking velocity class. Torque cost analysis shows that amble and trot gaits are more effi... <s> BIB009 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Recently, the field of humanoid robotics attracts more and more interest and the research on humanoid locomotion based on central pattern generators (CPG) reveals many challenging aspects. This paper describes the design of CPG for stable humanoid bipedal locomotion using an evolutionary approach. In this research, each joint of the humanoid is driven by a neuron that consists of two coupled neural oscillators, and corresponding joint's neurons are connected by strength weight. To achieve natural and robust walking pattern, an evolutionary-based multi-objective optimization algorithm is used to solve the weight optimization problem. The fitness functions are formulated based on zero moment point (ZMP), global attitude of the robot and the walking speed. In the algorithms, real value coding and tournament selection are applied, the crossover and mutation operators are chosen as heuristic crossover and boundary mutation respectively. Following evolving, the robot is able to walking in the given environment and a simulation shows the result. <s> BIB010 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> We present a search-based method for the generation of a terrain-adaptive optimal gait of a six-legged walking machine. In this, several heuristic rules have been proposed to reduce the search effort. We identify the useful support states of the machine and form a table to indicate for each of these states the list of other states to which a transition can be made. This helps in converging to and maintaining a periodic gait through a limited search while retaining adequate options to deviate from such a gait as and when needed. The criterion for optimization is coded into a function that evaluates the promise of a node in the search graph. We have shown how this function may be designed to generate the common periodic gaits like the wave gait, the equal phase gait, and the follow-the-leader gait. The purpose is to demonstrate that the proposed method is sufficiently general and can cater to a wide range of optimizing requirements. <s> BIB011 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru ... <s> BIB012 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract In this paper, a prismatic joint biped robot trajectory planning method is proposed. The minimum consumed energy is used as a criterion for trajectory generation, by using a real number genetic algorithm as an optimization tool. The minimum torque change cost function and constant vertical position trajectories are used in order to compare the results and verify the effectiveness of this method. The minimum consumed energy walking is stable and the impact of the foot with the ground is very small. Experimental investigations of a prismatic joint biped robot confirmed the predictions concerning the consumed energy and stability. <s> BIB013 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> We describe an evolutionary approach to the control problem of bipedal walking. Using a full rigid-body simulation of a biped, it was possible to evolve recurrent neural networks that controlled stable straight-line walking on a planar surface. No proprioceptive information was necessary in order to achieve this task. Furthermore, simple sensory input to locate a sound source was integrated to achieve directional walking. To our knowledge, this is the first work that demonstrates the application of evolutionary optimization to 3D physically simulated biped locomotion. <s> BIB014 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper describes a new method for generating optimal path and gait simultaneously of a six-legged robot using a combined GA-fuzzy approach. The problem of combined path and gait generations involves three steps, namely determination of vehicle’s trajectory, foothold selection and design of a sequence of leg movements. It is a complicated task and no single traditional approach is found to be successful in handling this problem. Moreover, the traditional approaches do not consider optimization issues, yet they are computationally expensive. Thus, the generated path and gaits may not be optimal in any sense. To solve such problems optimally, there is still a need for the development of an efficient and computationally faster algorithm. In the proposed genetic-fuzzy approach, optimal path and gaits are generated by using fuzzy logic controllers (FLCs) and genetic algorithms (GAs) are used to find optimized FLCs. The optimization is done off-line on a number of training scenarios and optimal FLCs are found. The hexapod can then use these GA-tuned FLCs to navigate in test-case scenarios. <s> BIB015 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this work, we present a new method for real time gait generation during walking based on Neural Networks. The minimum consumed energy gaits similar with human motion, are used to teach the Neural Network. After supervised learning, the Neural Network can quickly generate the humanoid robot gait. Simulation and experimental results utilizing the “Bonten-Maru I” humanoid robot show good performance of the proposed method. <s> BIB016 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered. ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences ... <s> BIB017 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Trunk motions are typically used in biped robots to stabilize the locomotion. However, they can be very large for some leg trajectories unless they are carefully designed. This paper proposes a fuzzy-logic zero-moment-point (ZMP) trajectory generator that would eventually reduce the swing motion of the trunk significantly even though the leg trajectory is casually designed, for example, simply to avoid obstacles. The fuzzy-logic ZMP trajectory generator uses the leg trajectory as an input. The resulting ZMP trajectory is similar to that of a human one and continuously moves forward in the direction of the locomotion. The trajectory of the trunk to stabilize the locomotion is determined by solving a differential equation with the ZMP trajectory and the leg trajectory known. The proposed scheme is simulated on a 7-DOF biped robot in the sagittal plane. The simulation results show that the ZMP trajectory generated by the proposed fuzzy-logic generator increases the stability of the locomotion and thus reduces the motion range of the trunk significantly. <s> BIB018 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents a general fuzzy reinforcement learning (FRL) method for biped dynamic balance control. Based on a neuro fuzzy network architecture, different kinds of expert knowledge and measurement-based information can be incorporated into the FRL agent to initialise its action network, critic network and/or evaluation feedback module so as to aecelerate its learning. The proposed FRL agent is constructed and verified using the simulation model of a physical biped robot. The sinmtation analysis shows that by incorporation of the human intuitive balancing knowledge and walking evaluation knowledge, the FRL agent's learning rate for side-to-side and front-to-back balance of the simulated biped can be improved. We also demonstrate that it is possible for a biped robot to start its walking with a priori knowledge and then learn to improve its behaviour with the FRL agents. <s> BIB019 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the robot for biped locomotion, falling and getting up motions. The platform has been used to develop various applications and is expected to initiate more humanoid robotics research. <s> BIB020 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In this paper we present the experimental validation of a framework for the systematic design, analysis, and performance enhancement of controllers that induce stable walking in N -link underactuated planar biped robots. Controllers designed via this framework act by enforcing virtual constraints—holonomic constraints imposed via feedback—on the robot’s configuration, which create an attracting two-dimensional invariant set in the full walking model’s state space. Stability properties of resulting walking motions are easily analyzed in terms of a two-dimensional subdynamic of the full walking model. A practical introduction to and interpretation of the framework is given. In addition, in this paper we develop the ability to regulate the average walking rate of the biped to a continuum of values by modification of within-stride and stride-boundary characteristics, such as step length. <s> BIB021 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> We present a new stability analysis for hybrid legged locomotion systems based on the “symmetric” factorization of return maps. We apply this analysis to two-degrees-of-freedom (2DoF) and three-degrees-of-freedom (3DoF) models of the spring loaded inverted pendulum (SLIP) with different leg recirculation strategies. Despite the non-integrability of the SLIP dynamics, we obtain a necessary condition for asymptotic stability (and a sufficient condition for instability) at a fixed point, formulated as an exact algebraic expression in the physical parameters. We use this expression to characterize analytically the sensory cost and stabilizing benefit of various feedback schemes previously proposed for the 2DoF SLIP model, posited as a low-dimensional representation of running. We apply the result as well to a 3DoF SLIP model that will be treated at greater length in a companion paper as a descriptive model for the robot RHex. <s> BIB022 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> “Mimesis” theory focused in the cognitive science field and “mirror neurons” found in the biology field show that the behavior generation process is not independent of the behavior cognition process. The generation and cognition processes have a close relationship with each other. During the behavioral imitation period, a human being does not practice simple joint coordinate transformation, but will acknowledge the parents’ behavior. It understands the behavior after abstraction as symbols, and will generate its self-behavior. Focusing on these facts, we propose a new method which carries out the behavior cognition and behavior generation processes at the same time. We also propose a mathematical model based on hidden Markov models in order to integrate four abilities: (1) symbol emergence; (2) behavior recognition; (3) self-behavior generation; (4) acquiring the motion primitives. Finally, the feasibility of this method is shown through several experiments on a humanoid robot. <s> BIB023 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Walking pattern synthesis is carried out using a spline-based parametric optimization technique. Generalized coordinates are approximated by spline functions of class C3fitted at knots uniformly distributed along the motion time. This high-order differentiability eliminates jerky variations of actuating torques. Through connecting conditions, spline polynomial coefficients are determined as a linear function of the joint coordinates at knots. These values are then dealt with as optimization parameters. An optimal control problem is formulated on the basis of a performance criterion to be minimized, representing an integral quadratic amount of driving torques. Using the above spline approximations, this primary problem is recast into a constrained non-linear optimization problem of mathematical programming, which is solved using a computing code implementing an SQP algorithm. As numerical simulations, complete gait cycles are generated for a seven-link planar biped. The only kinematic data to be accounted for are the walking speeds. Optimization of both phases of gait is carried out globally; it includes the optimization of transition configurations of the biped between successive phases of the gait cycle. <s> BIB024 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In this paper, we develop a simple bipedal locomotion algorithm based on biological concepts. The algorithm utilized a central pattern generator (CPG) composed of four coupled neural oscillators (NO) to generate control signal for the bipedal robot. Feedbacks from the robot dynamics and the environment are used to update the CPG online. Our algorithm is then tested on a seven link model of bipedal robot. Simulation results suggest the proposed CPG can generate a smooth and continuous walking pattern for the robot. <s> BIB025 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents simulation and experimental results of dynamic walking and running of a bipedal robot. We proposed the hybrid central pattern generator (H-CPG) method to realize adaptive dynamic motions including stepping and jumping. This method basically consisted of CPG models, but was added the force control system that controlled the acting force from a leg to a floor in the vertical and the horizontal directions separately. At this moment, 2D walking and running was realized on level ground and a slope with a speed of 1.6 m/s. <s> BIB026 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract In this paper, we investigated the effects of the friction condition on walking pattern and energy efficiency, and based on the results, we proposed two new “slip-adaptive” strategies for generating a slip-adaptive walk. The first strategy for a slip-adaptive walk uses a slip reflex via a Central Pattern Generator (CPG) to change the walking pattern. The second strategy for a walk uses a force control to immediately compensate a slip. Using these strategies, a walk, which is adaptive to varying friction conditions and slips, becomes possible. The validity of the proposed method is confirmed through simulation and experimentation. <s> BIB027 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our evolutionary algorithm runs on board the robot and uses the robot's sensors to compute the quality of a gait without assistance from the experimenter. First, we show the evolution of a pace and trot gait on the OPEN-R prototype robot. With the fastest gait, the robot moves at over 10 m/min, which is more than forty body-lengths/min. While these first gaits are somewhat sensitive to the robot and environment in which they are evolved, we then show the evolution of robust dynamic gaits, one of which is used on the ERS-110, the first consumer version of AIBO. <s> BIB028 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments. <s> BIB029 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract In this paper, we propose a control strategy allowing us to perform the dynamic walking gait of an under-actuated robot even if this one is subjected to destabilizing external disturbances. This control strategy is based on two stages. The first one consists of using a set of pragmatic rules in order to generate a succession of passive and active phases allowing us to perform a dynamic walking gait of the robot. The joint trajectories of this reference gait are learned by using neural networks. In the second stage, we use these neural networks to generate the learned trajectories during the first stage. The goal of the use of these neural networks is to increase the robustness of the control of the dynamic walking gait of this robot in the case of external disturbances. The first experimental results are also presented. <s> BIB030 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Abstract Gait generation for legged vehicles has since long been considered as an area of keen interest by the researchers. Soft computing is an emerging technique, whose utility is more stressed, when the problems are ill-defined, difficult to model and exhibit large scale solution spaces. Gait generation for legged vehicles is a complex task. Therefore, soft computing can be applied to solve it. In this work, gait generation problem of a two-legged robot is modeled using a fuzzy logic controller (FLC), whose rule base is optimized offline, using a genetic algorithm (GA). Two different GA-based approaches (to improve the performance of FLC) are developed and their performances are compared to that of manually constructed FLC. Once optimized, the FLCs will be able to generate dynamically stable gait of the biped. As the CPU-time of the algorithm is found to be only 0.002 s in a P-III PC, the algorithm is suitable for on-line (real-time) implementations. <s> BIB031 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper proposes a universal stability criterion of the foot contact of legged robots. The proposed method checks if the sum of the gravity and the inertia wrench applied to the COG of the robot, which is proposed to be the stability criterion, is inside the polyhedral convex cone of the contact wrench between the feet of a robot and its environment. The criterion can be used to determine the strong stability of the foot contact when a robot walks on an arbitrary terrain and/or when the hands of the robot are in contact with it under the sufficient friction assumption. The determination is equivalent to check if the ZMP is inside the support polygon of the feet when the robot walks on a horizontal plane with sufficient friction. The criterion can also be used to determine if the foot contact is sufficiently weakly stable when the friction follows a physical law. Therefore, the proposed criterion can be used to judge what the ZMP can, and it can be used in more universal cases <s> BIB032 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> We present a system of coupled nonlinear oscillators to be used as programmable central pattern generators, and apply it to control the locomotion of a humanoid robot. Central pattern generators are biological neural networks that can produce coordinated multidimensional rhythmic signals, under the control of simple input signals. They are found both in vertebrate and invertebrate animals for the control of locomotion. In this article, we present a novel system composed of coupled adaptive nonlinear oscillators that can learn arbitrary rhythmic signals in a supervised learning framework. Using adaptive rules implemented as differential equations, parameters such as intrinsic frequencies, amplitudes, and coupling weights are automatically adjusted to replicate a teaching signal. Once the teaching signal is removed, the trajectories remain embedded as the limit cycle of the dynamical system. An interesting aspect of this approach is that the learning is completely embedded into the dynamical system, and does not require external optimization algorithms. We use our system to encapsulate rhythmic trajectories for biped locomotion with a simulated humanoid robot, and demonstrate how it can be used to do online trajectory generation. The system can modulate the speed of locomotion, and even allow the reversal of direction (i.e. walking backwards). The integration of sensory feedback allows the online modulation of trajectories such as to increase the basin of stability of the gaits, and therefore the range of speeds that can be produced <s> BIB033 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Anthropomorphic walking for a simulated bipedal robot has been realized by means of artificial evolution of central pattern generator (CPG) networks. The approach has been investigated through full rigid-body dynamics simulations in 3D of a bipedal robot with 14 degrees of freedom. The half-center CPG model has been used as an oscillator unit, with interconnection paths between oscillators undergoing structural modifications using a genetic algorithm. In addition, the connection weights in a feedback network of predefined structure were evolved. Furthermore, a supporting structure was added to the robot in order to guide the evolutionary process towards natural, human-like gaits. Subsequently, this structure was removed, and the ability of the best evolved controller to generate a bipedal gait without the help of the supporting structure was verified. Stable, natural gait patterns were obtained, with a maximum walking speed of around 0.9 m/s. <s> BIB034 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In this paper, we present our design and experiments on a planar biped robot under the control of a pure sensor-driven controller. This design has some special mechanical features, for example small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robot's natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time, during walking. This way RunBot can reach a relative speed of 3.5 leg lengths per second after only a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking. <s> BIB035 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents the virtual height inverted pendulum mode (VHIPM), which is a simple and effective trajectory generation method for the stable walking of biped robots. VHIPM, which is based on the inverted pendulum mode (IPM), can significantly reduce the zero moment point (ZMP) error by adjusting the height in the inverted pendulum. We show the relationship between VHIPM and other popular trajectory generation methods, and compare the ZMP errors in walking when trajectories are generated by various methods including VHIPM. We also investigate the sensitivity of the ZMP error in VHIPM to the step length, walking period and mass distribution of a robot. The simulation results show that VHIPM significantly reduces the ZMP errors compared to other methods under various circumstances. <s> BIB036 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In limbed animals, spinal neural circuits responsible for controlling muscular activities during walking are called central pattern generators (CPG). CPG networks display oscillatory activities that actuates individual or groups of muscles in a coordinated fashion so that the limbs of the animal are flexed and extended at the appropriate time and with the required velocity for the animal to efficiently traverse various types of terrain, and to recover from environmental perturbation. Typically, the CPG networks are constructed with many neurons, each of which has a number of control parameters. As the number of muscles increases, it is often impossible to manually, albeit intelligently, select the network parameters for a particular movement. Furthermore, it is virtually impossible to reconfigure the parameters on-line. This paper describes how genetic algorithms (GA) can be used for on-line (re)configuring of CPG networks for a bipedal robot. We show that the neuron parameters and connection weights/network topology of a canonical walking network can be reconfigured within a few of generations of the GA. The networks, constructed with integrate-and-fire-with-adaptation (IFA) neurons, are implemented with a microcontroller and can be reconfigured to vary walking speed from 0.5Hz to 3.5Hz. The phase relationship between the hips and knees can be arbitrarily set (to within 1 degree) and prescribed complex joint angle profiles are realized. This is a powerful approach to generating complex muscle synergies for robots with multiple joints and distributed actuators. <s> BIB037 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> An antagonistic muscle mechanism that regulates joint compliance contributes enormously to human dynamic locomotion. Antagonism is considered to be the key for realizing more than one locomotion mode. In this paper, we demonstrate how antagonistic pneumatic actuators can be utilized to achieve three dynamic locomotion modes (walking, jumping, and running) in a biped robot. Firstly, we discuss the contribution of joint compliance to dynamic locomotion, which highlights the importance of tunable compliance. Secondly, we introduce the design of a biped robot powered by antagonistic pneumatic actuators. Lastly, we apply simple feedforward controllers for realizing walking, jumping, and running and confirm the contribution of joint compliance to such multimodal dynamic locomotion. Based on the results, we can conclude that the antagonistic pneumatic actuators are superior candidates for constructing a human-like dynamic locomotor. <s> BIB038 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents a motion pattern generator of humanoid robots that walks on a flat plane, steps and a rough terrain. It is guaranteed rigorously that the desired contact between a humanoid robot and terrain should be maintained by keeping the contact wrench sum between them inside the contact wrench cone under the sufficient friction assumption. A walking pattern is generated by solving the contact wrench equations and by applying the resolved momentum control. <s> BIB039 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Biped robot with heterogeneous legs (BRHL) is a novel robot model, which consists of an artificial leg and an intelligent bionic leg. The artificial leg is used to simulate the amputeepsilas healthy leg and the bionic leg works as the intelligent artificial limb. To describe the present gait of the healthy leg and make intelligent bionic leg follow the walking of artificial leg in all phases is the target of BRHLpsilas research. So gait planning of artificial leg is the emphasis of BRHLpsilas research. This paper uses the model of central pattern generator (CPG) in the research of artificial legpsilas gait planning from the point of biology. To obtain natural and robust walking pattern, genetic algorithm is used to optimize parameters of the CPG network model and the fitness function is formulated based on zero moment point (ZMP). Simulation results testify the feasibility of this method. <s> BIB040 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> The generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying offline solutions exist for steady-state walking in absence of disturbances. The question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. This is precisely the problem addressed in this paper. Indeed, we consider the case where the ''master'' motion is measured by a position sensor embedded on a human leg. We propose a method to synchronize the motion of a robot or of other device with respect to the output signal of the sensor. The main goal is to estimate as accurately as possible the current phase along the gait cycle. We use for that purpose a model based on a nonlinear oscillator, which we associate an observer. Introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. The paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input. <s> BIB041 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Central pattern generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environment. <s> BIB042 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> We present a new quadruped robot, ldquoCheetahrdquo, featuring three-segment pantographic legs with passive compliant knee joints. Each leg has two degrees of freedom - knee and hip joint can be actuated using proximal mounted RC servo motors, force transmission to the knee is achieved by means of a bowden cable mechanism. Simple electronics to command the actuators from a desktop computer have been designed in order to test the robot. A Central Pattern Generator (CPG) network has been implemented to generate different gaits. A parameter space search was performed and tested on the robot to optimize forward velocity. <s> BIB043 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> A control problem is studied for generating the locomotion pattern of a semi-looper type robot by applying central pattern generators (CPGs), in which such a robot can realize two-rhythm motion and green caterpillar locomotion depending on the condition of environment. After deriving the dynamical model with two links and one actuator, the simulation of the robot is conducted using a CPG consisting of one van der Pol (VDP) oscillator. A CPG network composed of two VDP oscillators is further constructed to realize four-rhythm motion with three links and two actuators. <s> BIB044 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm. <s> BIB045 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents an approach for the closed-loop control of a fully actuated biped robot that leverages its natural dynamics when walking. Rather than prescribing kinematic trajectories, the approach proposes a set of state-dependent torques, each of which can be constructed from a combination of low-gain spring-damper couples. Accordingly, the limb motion is determined by interaction of the passive control elements and the natural dynamics of the biped, rather than being dictated by a reference trajectory. In order to implement the proposed approach, the authors develop a model-based transformation from the control torques that are defined in a mixed reference frame to the actuator joint torques. The proposed approach is implemented in simulation on an anthropomorphic biped. The simulated biped is shown to converge to a stable, natural-looking walk from a variety of initial configurations. Based on these simulations, the mechanical cost of transport is computed and shown to be significantly lower than that of trajectory-tracking approaches to biped control, thus validating the ability of the proposed idea to provide efficient dynamic walking. Simulations further demonstrate walking at varying speeds and on varying ground slopes. Finally, controller robustness is demonstrated with respect to forward and backward push-type disturbances and with respect to uncertainty in model parameters. <s> BIB046 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems. <s> BIB047 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> The development of optimized motions of humanoid robots that guarantee fast and also stable walking is an important task, especially in the context of autonomous soccer-playing robots in RoboCup. We present a walking motion optimization approach for the humanoid robot prototype HR18 which is equipped with a low-dimensional parameterized walking trajectory generator, joint motor controller and an internal stabilization. The robot is included as hardware-in-the-loop to define a low-dimensional black-box optimization problem. In contrast to previously performed walking optimization approaches, we apply a sequential surrogate optimization approach using stochastic approximation of the underlying objective function and sequential quadratic programming to search for a fast and stable walking motion. This is done under the conditions that only a small number of physical walking experiments should have to be carried out during the online optimization process. For the identified walking motion for the considered 55 cm tall humanoid robot, we measured a forward walking speed of more than 30 cm s-1 . With a modified version of the robot, even more than 40 cm s-1 could be achieved in permanent operation. <s> BIB048 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> The objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a Central Pattern Generator (CPG) based control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. In this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleigh's oscillator. An indigenous probe, Humanoid Gait Oscillator Detector (HGOD) has been designed for capturing gait patterns from various individuals of different height, weight and age. These data are used to design a Fuzzy inference system which generates most suitable gait pattern for an amputee. The output of the Fuzzy inference system is used for designing a CPG best suitable for the amputee. We then developed a CPG based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called AMAL (Adaptive Modular Active Leg). Also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network. <s> BIB049 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In this paper, we propose a new CPG model and hebbian learning rule for the CPG. The output of the proposed CPG is determined by only phase differences of synchronization of component oscillators. A phase synchronization can be regarded as an adaptive behavior to an environment, so the CPG has an adaptavility despite of only simple connections to the environment. We also propose a motion learning rule for the proposed CPG. Since the rule is described by only simple signal processing, so that it can be realized by electric circuits easily, it will be able to be used efficiently for high degree of freedom robots. <s> BIB050 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> To generate coordinated periodic movements, robot locomotion demands mechanisms which are able to learn and produce stable rhythmic motion in a controllable way. Because systems based on biological central pattern generators (CPGs) can cope with these demands, these kind of systems are gaining in success. In this work we introduce a novel methodology that uses the dynamics of a randomly connected recurrent neural network for the design of CPGs. When a randomly connected recurrent neural network is excited with one or more useful signals, an output can be trained by learning an instantaneous linear mapping of the neuron states. This technique is known as reservoir computing (RC). We will show that RC has the necessary capabilities to be fruitful in designing a CPG that is able to learn human motion which is applicable for imitation learning in humanoid robots. <s> BIB051 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In the early 1950s, von Holst and Mittelstaedt proposed that motor commands copied within the central nervous system (efference copy) help to distinguish 'reafference' activity (afference activity due to self-generated motion) from 'exafference' activity (afference activity due to external stimulus). In addition, an efference copy can be also used to compare it with the actual sensory feedback in order to suppress self-generated sensations. Based on these biological findings, we conduct here two experimental studies on our biped ''RunBot'' where such principles together with neural forward models are applied to RunBot's dynamic locomotion control. The main purpose of this article is to present the modular design of RunBot's control architecture and discuss how the inherent dynamic properties of the different modules lead to the required signal processing. We believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots. <s> BIB052 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a trade-off between classification accuracy and the number of training examples, resulting in potential misclassifications both at the motion and hierarchy formation level. This article presents an approach enabling fast on-line incremental learning, combined with an incremental memory consolidation process correcting initial misclassifications and errors in organization, to improve the stability and accuracy of the learned motions, analogous to the memory consolidation process following motor learning observed in humans. Following initial organization, motions are randomly selected for reclassification, at both low and high levels of the hierarchy. If a better reclassification is found, the knowledge structure is reorganized to comply. The approach is validated during incremental acquisition of a motion database containing a variety of full body motions.1 <s> BIB053 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> In this paper, a probability-based balance monitoring concept for humanoid robots is proposed. Two algorithms are presented that allow us to distinguish between exceptional situations and normal operations. The first classification approach uses Gaussian-Mixture-Models (GMM) to describe the distribution of the robot's sensor data for typical situations such as stable walking or falling down. With the GMM it is possible to state the probability of the robot being in one of the known situations. The concept of the second algorithm is based on Hidden-Markov-Models (HMM). The objective is to detect and classify unstable situations by means of their typical sequences in the robot's sensor data. When appropriate reflex motions are linked to the critical situations, the robot can prevent most falls or is at least able to execute a controlled falling motion. The proposed algorithms are verified by simulations and experiments with our bipedal robot BARt-UH. <s> BIB054 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot. <s> BIB055 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper presents an algorithm for planning the foothold positions of quadruped robots on irregular terrain. The input to the algorithm is the robot kinematics, the terrain geometry, a required motion path, as well as initial posture. Our goal is to develop general algorithm that navigate quadruped robots quasi-statically over rough terrain, using an APF (Artificial Potential Field) and graph searching. The algorithm is planning a sequence set of footholds that navigates the robot along the required path with controllable motion characteristics. Simulations results demonstrate the algorithm in a planner environment. <s> BIB056 </s> Intelligent Approaches in Locomotion - A Review <s> Method <s> This paper considers the effect of natural material compliance on the stiffness and stability of frictional multi-contact grasps and fixtures. The contact preload profile is a key parameter in the nonlinear compliance laws governing such contacts. The paper introduces the Hertz-Walton contact compliance model which is valid for linear contact loading profiles. The model is specified in a lumped parameter form suitable for on-line grasping applications, and is entirely determined by the contact friction and by the material and geometric properties of the contacting bodies. The model predicts an asymmetric stiffening of the tangential reaction force as the normal load at the contact increases. As a result, the composite stiffness matrix of multi-contact grasps governed by natural compliance effects is asymmetric, indicating that these contact arrangements are not governed by any potential energy function. Based on the compliant grasp dynamics, the paper derives rules indicating which contact point locations and what preload profiles guarantee grasp and fixture stability. The paper also describes preliminary experiments supporting the contact model predictions. <s> BIB057
Biped Quadruped Hexapod Other Sim Hard Sim Hard Sim Hard Sim Hard Analytical BIB036 , BIB046 - BIB032 , BIB024 -  , BIB012 , BIB055 [2], BIB020 ,  , BIB038 , BIB039 - BIB047 , BIB021 , BIB048 , BIB013 , BIB016 [47]- BIB009 , BIB056 [50]-  [54], BIB022 , BIB001 [64] BIB057 CPG BIB040 , BIB049 , BIB025 , BIB033 , BIB010 - BIB034 [3], BIB047 , BIB026 , BIB035 - BIB041 ,  , BIB037 [70]- BIB042 , BIB050 [75]- BIB027 , BIB043 , BIB028 [80] BIB007 , BIB044 [81], BIB029 [10],  -  ,  NN BIB014 , BIB012 , BIB017 , BIB002 , BIB055 , BIB051 [67], BIB052 , BIB006 - BIB030 [104], BIB004 ,  , BIB005 [101], BIB003 HMM BIB053 , BIB045 [114]- BIB054 Rule based BIB018 , BIB055 , BIB008 , BIB019 , BIB031 [121] BIB015 , BIB011 [124] Beyond just one method of locomotion, some papers have investigated different gaits, ranging from walking to sprinting, along with smooth transitions between the gaits. As well as locomotion, some research has been conducted into other movement skills, e.g., jumping  , BIB038 . We also review techniques that allow systems to imitate an observed movement pattern BIB023 - BIB045 . Although some of the reviewed papers are easy to classify in a single category, there are also many that span and overlap several categories. For example, specialized NN topologies blur the distinction between NNs and CPGs. Also, given their biological source, CPGs are often described as neural models, even if they do not resemble traditional NNs. Nevertheless, the aim of this review is to identify the core methods used in locomotion, and to discuss and provide comparative analysis of their usability, considering the application areas.