An Efficient Gait-generating Method for Electrical Quadruped Robot Based on Humanoid Power Planning Approach

The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prompting.Minimizing energy consumption and keeping the stability of body are considered as two main characteristics of human walking.This work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking pattern.Based on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped robot.Through studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot prototype.The stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype experi-ment.The results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.     

A Bio-Inspired Biped Water Running Robot Incorporating the Watt-I Planar Linkage Mechanism

In this paper, a biped water running robot is developed by mimicking the water-running pattern of basilisk lizards. The dynamic mechanism of the robot was studied based on Watt-I planar linkages, and the movement trajectory of the double bar Assur Group was deduced to simulate the water-running foot trajectories of the basilisk lizard. A Central Pattern Generator （CPG）-based fuzzy control method was proposed to control the robot for realizing balance control and gait adjustment. The effectiveness of the proposed control method was verified on the prototype of a water running robot （weight： 320 g）. When the biped robot is running on water, the average force generated by the propulsion mechanism is 1.3 N, and the robot body tilt angle is 5~. The experiment results show that the propulsion mechanism is effective in realizing the basilisk lizards-like water running patterns, and the CPG-based fuzzy control method is effective in keeping the balance of the robot.     

Anthropomorphic Design of the Human-Like Walking Robot

In this paper, we present a new concept of the mechanical design of a humanoid robot. The goal is to build a humanoid robot utilizing a new structure which is more suitable for human-like walking with the characteristics of the knee stretch, heel-contact, and toe-off. Inspired by human skeleton, we made an anthropomorphic pelvis for the humanoid robot. In comparison with conventional humanoid robots, with such the anthropomorphic pelvis, our robot is capable of adjusting the center of gravity of the upper body by the motion of pelvic tilt, thus reducing the required torque at the ankle joint and the velocity variations in human-like walking. With more precise analysis of the foot mechanism, the fixed-length inverted pendulum can be used to describe the dynamics of biped walking, thus preventing redundant works and power consumption in length variable inverted pendulum system. As the result of the new structure we propose, a humanoid robot is able to walk with human-like gait.     

Trot Gait Design and CPG Method for a Quadruped Robot

Developing efficient walking gaits for quadruped robots has intrigued investigators for years. Trot gait, as a fast locomotion gait, has been widely used in robot control. This paper follows the idea of the six determinants of gait and designs a trot gait for a parallel-leg quadruped robot, Baby Elephant. The walking period and step length are set as constants to maintain a relatively fast speed while changing different foot trajectories to test walking quality. Experiments show that kicking leg back improves body stability. Then, a steady and smooth trot gait is designed. Furthermore, inspired by Central Pattern Generators （CPG）, a series CPG model is proposed to achieve robust and dynamic trot gait. It is generally believed that CPG is capable of producing rhythmic movements, such as swimming, walking, and flying, even when isolated from brain and sensory inputs. The proposed CPG model, inspired by the series concept, can automatically learn the previous well-designed trot gait and reproduce it, and has the ability to change its walking frequency online as well. Experiments are done in real world to verify this method.     

CPG Control for Biped Hopping Robot in Unpredictable Environment

A CPG control mechanism is proposed for hopping motion control of biped robot in unpredictable environment.Based on analysis of robot motion and biological observation of animal's control mechanism,the motion control task is divided into two simple parts:motion sequence control and output force control.Inspired by a two-level CPG model,a two-level CPG control mechanism is constructed to coordinate the drivers of robot joint,while various feedback information are introduced into the control mechanism.Interneurons within the control mechanism are modeled to generate motion rhythm and pattern promptly for motion sequence control; motoneurons are modeled to control output forces of joint drivers in real time according to feedbacks.The control system can perceive changes caused by unknown perturbations and environment changes according to feedback information,and adapt to unpredictable environment by adjusting outputs of neurons.The control mechanism is applied to a biped hopping robot in unpredictable environment on simulation platform,and stable adaptive motions are obtained.     

A passive dynamic walking robot that has a deterministic nonlinear gait

There is a growing body of evidence that the step-to-step variations present in human walking are related to the biomechanics of the locomotive system. However, we still have limited understanding of what biomechanical variables influence the observed nonlinear gait variations. It is necessary to develop reliable models that closely resemble the nonlinear gait dynamics in order to advance our knowledge in this scientific field. Previously, Goswami et al. [1998. A study of the passive gait of a compass-like biped robot: symmetry and chaos. International Journal of Robotic Research 17(12)] and Garcia et al. [1998. The simplest walking model: stability, complexity, and scaling. Journal of Biomechanical Engineering 120(2), 281-288] have demonstrated that passive dynamic walking computer models can exhibit a cascade of bifurcations in their gait pattern that lead to a deterministic nonlinear gait pattern. These computer models suggest that the intrinsic mechanical dynamics may be at least partially responsible for the deterministic nonlinear gait pattern; however, this has not been shown for a physical walking robot. Here we use the largest Laypunov exponent and a surrogation analysis method to confirm and extend Garcia et al.'s and Goswami et al.'s original results to a physical passive dynamic walking robot. Experimental outcomes from our walking robot further support the notion that the deterministic nonlinear step-to-step variations present in gait may be partly governed by the intrinsic mechanical dynamics of the locomotive system. Furthermore the nonlinear analysis techniques used in this investigation offer novel methods for quantifying the nature of the step-to-step variations found in human and robotic gait.     

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks.     

Robust control of CPG-based 3D neuromusculoskeletal walking model

This paper proposes a method for enhancing the robustness of the central pattern generator (CPG)-based three-dimensional (3D) neuromusculoskeletal walking controller. The CPG has been successfully applied to walking controllers and controllers for walking robots. However, the robustness of walking motion with the CPG-based controller is not sufficient, especially when subjected to external forces or environmental variations. To achieve a realistic and stable walking motion of the controller, we propose the use of an attracting controller in parallel with the CPG-based controller. The robustness of the proposed controller is confirmed through simulation results.     

Stumbling with optimal phase reset during gait can prevent a humanoid from falling

The human biped walking shows phase- dependent transient changes in gait trajectory in response to external brief force perturbations. Such responses, referred to as the stumbling reactions, are usually accompanied with phase reset of the walking rhythm. Our previous studies provided evidence, based on a human gait experiment and analyses of mathematical models of gait in the sagittal plane, that an appropriate amount of phase reset in response to a perturbation depended on the gait phase at the perturbation and could play an important role for preventing the walker from a fall, thus increasing gait stability. In this paper, we provide a further material that supports this evidence by a gait experiment on a biped humanoid. In the experiment, the impulsive force perturbations were applied using push-impacts by a pendulum-like hammer to the back of the robot during gait. The responses of the external perturbations were managed by resetting the gait phase with different delays or advancements. The results showed that appropriate amounts of phase resetting contributed to the avoidance of falling against the perturbation during the three-dimensional robot gait. A parallelism with human gait stumbling reactions was discussed.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Energy-efficient indoor search by swarms of simulated flying robots without global information

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints.     

Comparison of human and humanoid robot control of upright stance

There is considerable recent interest in developing humanoid robots. An important substrate for many motor actions in both humans and biped robots is the ability to maintain a statically or dynamically stable posture. Given the success of the human design, one would expect there are lessons to be learned in formulating a postural control mechanism for robots. In this study we limit ourselves to considering the problem of maintaining upright stance. Human stance control is compared to a suggested method for robot stance control called zero moment point (ZMP) compensation. Results from experimental and modeling studies suggest there are two important subsystems that account for the low- and mid-frequency (DC to 1 Hz) dynamic characteristics of human stance control. These subsystems are (1) a “sensory integration” mechanism whereby orientation information from multiple sensory systems encoding body kinematics (i.e. position, velocity) is flexibly combined to provide an overall estimate of body orientation while allowing adjustments (sensory re-weighting) that compensate for changing environmental conditions and (2) an “effort control” mechanism that uses kinetic-related (i.e., force-related) sensory information to reduce the mean deviation of body orientation from upright. Functionally, ZMP compensation is directly analogous to how humans appear to use kinetic feedback to modify the main sensory integration feedback loop controlling body orientation. However, a flexible sensory integration mechanism is missing from robot control leaving the robot vulnerable to instability in conditions where humans are able to maintain stance. We suggest the addition of a simple form of sensory integration to improve robot stance control. We also investigate how the biological constraint of feedback time delay influences the human stance control design. The human system may serve as a guide for improved robot control, but should not be directly copied because the constraints on robot and human control are different.     

Human-like Manipulation Planning for Articulated Manipulator

A new method based on human-likeness assessment and optimization concept to solve the problem of human-like manipulation planning for articulated robot is proposed in this paper.This method intrinsically formulates the problem as a constrained optimization problem in robot configuration space.The robot configuration space is divided into different subregions by human likeness assessment.A widely used strategy,Rapid Upper Limb Assessment (RULA) in applied ergonomics,is adopted here to evaluate the human likeness of robot configuration.A task compatibility measurement of the robot velocity transmission ratio along a specified direction is used as the target function for the optimization problem.Simple illustrative examples of this method applied to a two Degree of Freedom (DOF) planar robot that resembles the upper limb of a human are presented.Further applications to a humanoid industrial robot SDA10D are also presented.The reasonable planning results for these applications assert the effectiveness of our method.     

Self-Localization of an Omnidirectional Mobile Robot Based on an Optical Flow Sensor

An omnidirectional mobile robot has the advantage that three degrees of freedom of motion in a 2D plane can be set independently, and it can thus move in arbitrary directions while maintaining the same heading. Dead reckoning is often used for self-localization using onboard sensors in omnidirectional robots, by means of measuring wheel velocities from motor encoder data, as well as in car-like robots. However, omnidirectional mobile robots can easily slip because of the nature of omni-wheels with multiple free rollers, and dead reckoning will not work if even one wheel is not attached to the ground. An odometry method where the data is not affected by wheel slip must be introduced to acquire high quality self-location data for omnidirectional mobile robots. We describe a method to obtain robot ego-motion using camera images and optical flow calculation, i.e., where the camera is used as a velocity sensor. In this paper, a silicon retina vision camera is introduced as a mobile robot sensor, which has a good dynamic range under various lighting conditions. A Field-Programmable Gate Array (FPGA) optical flow circuit for the silicon retina is also developed to measure ego-motion of the mobile robot. The developed optical flow calculation system is introduced into a small omnidirectional mobile robot and evaluation experiments for the mobile robot ego-motion are carried out. In the experiments, the accuracy of self-location by the dead reckoning and optical flow methods are evaluated by comparison using motion capture. The results show that the correct position is obtained by the optical flow sensor rather than by dead reckoning.     

Modeling of a bipedal robot using mutually coupled Rayleigh oscillators

The objective of the work presented here was the modeling of a bipedal robot using a central pattern generator (CPG) formed by a set of mutually coupled Rayleigh oscillators. We analyzed a 2D model, with the three most important determinants of gait, that performs only motions parallel to the sagittal plane. Using oscillators with integer relation of frequency, we determined the transient motion and the stable limit cycles of the network formed by the three oscillators, showing the behavior of the knee angles and the hip angle. A comparison of the plotted graphs revealed that the system provided excellent results when compared to experimental analysis. Based on the results of the study, we come to the conclusion that the use of mutually coupled Rayleigh oscillators can represent an excellent method of signal generation, allowing their application for feedback control of a walking machine.Acknowledgements The authors would like to express their gratitude to CNPq and CAPES for the financial support provided during the course of this research.     

Sensory feedback in CNN-based central pattern generators

Central Pattern Generators (CPGs) are a suitable paradigm to solve the problem of locomotion control in walking robots. CPGs are able to generate feed-forward signals to achieve a proper coordination among the robot legs. In literature they are often modelled as networks of coupled nonlinear systems. However the topic of feedback in these systems is rarely addressed. On the other hand feedback is essential for locomotion. In this paper the CPG for a hexapod robot is implemented through Cellular Neural Networks (CNNs). Feedback is included in the CPG controller by exploiting the dynamic properties of the CPG motor-neurons, such as synchronization issue and local bifurcations. These universal paradigms provide the essential issues to include sensory feedback in CPG architectures based on coupled nonlinear systems. Experiments on a dynamic model of a hexapod robot are presented to validate the approach introduced.     

Empirical Study on Shapes of the Foot Pad and Walking Gaits for Water-Running Robots

Recently, various kinds ofbiomimetic robots have been studied. Among these biomimetic robots, water-running robots that mimic the characteristics of basilisk lizards have received much attention. However, studies on the performance with respect to different geometric parameters and gaits have been lacking. To run on the surface of water, a water-running robot needs suffi- cient force with high stability to stay above the water. We experimentally measured the performance of the foot pads with different geometric parameters and with various gaits. We measured and analyzed the forces in the vertical direction and rolling angles of five different foot pad shapes： a circular shape, square shape, half-spherical shape, open half-cylinder shape, and closed half-cylinder shape. Additionally, the rolling stabilities of three kinds of gaits： biped, trotting, and tripod, were also empirically analyzed. The results of this research can be used as a guideline to design a stable water-running robot.     

A model of cerebrocerebello-spinomuscular interaction in the sagittal control of human walking

A computationally developed model of human upright balance control (Jo and Massaquoi on Biol cybern 91:188–202, 2004) has been enhanced to describe biped walking in the sagittal plane. The model incorporates (a) non-linear muscle mechanics having activation level -dependent impedance, (b) scheduled cerebrocerebellar interaction for control of center of mass position and trunk pitch angle, (c) rectangular pulse-like feedforward commands from a brainstem/ spinal pattern generator, and (d) segmental reflex modulation of muscular synergies to refine inter-joint coordination. The model can stand when muscles around the ankle are coactivated. When trigger signals activate, the model transitions from standing still to walking at 1.5 m/s. Simulated natural walking displays none of seven pathological gait features. The model can simulate different walking speeds by tuning the amplitude and frequency in spinal pattern generator. The walking is stable against forward and backward pushes of up to 70 and 75 N, respectively, and with sudden changes in trunk mass of up to 18%. The sensitivity of the model to changes in neural parameters and the predicted behavioral results of simulated neural system lesions are examined. The deficit gait simulations may be useful to support the functional and anatomical correspondences of the model. The model demonstrates that basic human-like walking can be achieved by a hierarchical structure of stabilized-long loop feedback and synergy-mediated feedforward controls. In particular, internal models of body dynamics are not required.     

Robust and efficient walking with spring-like legs

The development of bipedal walking robots is inspired by human walking. A way of implementing walking could be performed by mimicking human leg dynamics. A fundamental model, representing human leg dynamics during walking and running, is the bipedal spring-mass model which is the basis for this paper. The aim of this study is the identification of leg parameters leading to a compromise between robustness and energy efficiency in walking. It is found that, compared to asymmetric walking, symmetric walking with flatter angles of attack reveals such a compromise. With increasing leg stiffness, energy efficiency increases continuously. However, robustness is the maximum at moderate leg stiffness and decreases slightly with increasing stiffness. Hence, an adjustable leg compliance would be preferred, which is adaptable to the environment. If the ground is even, a high leg stiffness leads to energy efficient walking. However, if external perturbations are expected, e.g. when the robot walks on uneven terrain, the leg should be softer and the angle of attack flatter. In the case of underactuated robots with constant physical springs, the leg stiffness should be larger than k = 14 in order to use the most robust gait. Soft legs, however, lack in both robustness and efficiency.     

Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots

In this paper, we present an extended mathematical model of the central pattern generator (CPG) in the spinal cord. The proposed CPG model is used as the underlying low-level controller of a humanoid robot to generate various walking patterns. Such biological mechanisms have been demonstrated to be robust in locomotion of animal. Our model is supported by two neurophysiological studies. The first study identified a neural circuitry consisting of a two-layered CPG, in which pattern formation and rhythm generation are produced at different levels. The second study focused on a specific neural model that can generate different patterns, including oscillation. This neural model was employed in the pattern generation layer of our CPG, which enables it to produce different motion patterns—rhythmic as well as non-rhythmic motions. Due to the pattern-formation layer, the CPG is able to produce behaviors related to the dominating rhythm (extension/flexion) and rhythm deletion without rhythm resetting. The proposed multi-layered multi-pattern CPG model (MLMP-CPG) has been deployed in a 3D humanoid robot (NAO) while it performs locomotion tasks. The effectiveness of our model is demonstrated in simulations and through experimental results.