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.     

Approximate Perturbation Stance Map of the SLIP Runner and Application to Locomotion Control

This paper presents a novel method of perturbation to obtain the analytic approximate solution to the Spring-Loaded Inverted Pendulum (SLIP) dynamics in stance phase with considering the effect of gravity.This perturbation solution achieves higher accuracy in predicting the apex state variables than the typical existing analytic approximations.Particularly,our solution is validated for non-symmetric trajectory of hopping in a large angle range.Furthermore,the stance controller of the SLIP runner is developed to regulate the apex state based on the approximate apex return map.To compensate the energy variation between the current and desired apex states,a stiffness adjustment of the leg spring in stance phase is presented.The deadbeat controller of the angle of attack is designed to track the regulated apex height and velocity.The simulation demonstrates that the SLIP runner applying the proposed stance controller reveals higher tracking accuracy and more rapidly converges to the regulated apex state.     

Simultaneous Optimization of Robot Structure and Control System Using Evolutionary Algorithm

The simultaneous optimization of a robot structure and control system to realize effective mobility in an outdoor environment is investigated. Recently, various wheeled mechanisms with passive and/or active linkages for outdoor environments have been developed and evaluated. We developed a mobile robot having six active wheels and passive linkage mechanisms, and experimentally verified its maneuverability in an indoor environment. However, there are various obstacles in outdoor environment and the travel ability of a robot thus depends on its mechanical structure and control system.We proposed a method of simultaneously optimizing mobile robot structure and control system using an evolutionary algorithm. Here, a gene expresses the parameters of the structure and control system. A simulated mobile robot and controller are based on these parameters and the behavior of the mobile robot is evaluated for three typical obstacles. From the evaluation results, new genes are created and evaluated repeatedly. The evaluation items are travel distance, travel time, energy consumption, control accuracy, and attitude of the robot.Effective outdoor travel is achieved around the 80th generation, after which, other parameters are optimized until the 300th generation. The optimized gene is able to pass through the three obstacles with low energy consumption, accurate control, and stable attitude.     

Control of a Quadruped Robot with Bionic Springy Legs in Trotting Gait

Legged robots have better performance on discontinuous terrain than that of wheeled robots. However, the dynamic trotting and balance control of a quadruped robot is still a challenging problem, especially when the robot has multi-joint legs. This paper presents a three-dimensional model of a quadruped robot which has 6 Degrees of Freedom （DOF） on torso and 5 DOF on each leg. On the basis of the Spring-Loaded Inverted Pendulum （SLIP） model, body control algorithm is discussed in the first place to figure out how legs work in 3D trotting. Then, motivated by the principle of joint function separation and introducing certain biological characteristics, two joint coordination approaches are developed to produce the trot and provide balance. The robot reaches the highest speed of 2.0 m.s-1, and keeps balance under 250 Kg.m.s-1 lateral disturbance in the simulations. The effectiveness of these approaches is also verified on a prototype robot which runs to 0.83 m.s-1 on the treadmill, The simulations and experiments show that legged robots have good biological properties, such as the ground reaction force, and spring-like leg behavior.     

Velocity of body lean evoked by leg muscle vibration potentiate the effects of vestibular stimulation on posture

To investigate the vestibular and somatosensory interaction in human postural control, a galvanic vestibular stimulation of cosine bell shape resulting in a small forward or backward body lean was paired with three vibrations of both soleus muscles. The induced body lean was registered by the position of the center of foot pressure (CoP). During a quiet stance with eyes closed the vibration of both soleus muscles with frequency (of) 40 Hz, 60 Hz and 80 Hz resulted in the body lean backward with velocities related to the vibration frequencies. The vestibular galvanic stimulation with the head turned to the right caused forward or backward modification of CoP backward response to the soleus muscles vibration and peaked at 1.5-2 s following the onset of the vibration. The effect of the paired stimulation was larger than the summation of the vestibular stimulation during the quiet stance and a leg muscle vibration alone. The enhancement of the galvanic stimulation was related to the velocity of body lean induced by the leg muscle vibration. The galvanic vestibular stimulation during a faster body movement had larger effects than during a slow body lean or the quiet stance. The results suggest that velocity of a body postural movement or incoming proprioceptive signal from postural muscles potentiate the effects of simultaneous vestibular stimulations on posture.     

Behaviour-based modelling of hexapod locomotion: linking biology and technical application

Walking in insects and most six-legged robots requires simultaneous control of up to 18 joints. Moreover, the number of joints that are mechanically coupled via body and ground varies from one moment to the next, and external conditions such as friction, compliance and slope of the substrate are often unpredictable. Thus, walking behaviour requires adaptive, context-dependent control of many degrees of freedom. As a consequence, modelling legged locomotion addresses many aspects of any motor behaviour in general. Based on results from behavioural experiments on arthropods, we describe a kinematic model of hexapod walking: the distributed artificial neural network controller walknet. Conceptually, the model addresses three basic problems in legged locomotion. (I) First, coordination of several legs requires coupling between the step cycles of adjacent legs, optimising synergistic propulsion, but ensuring stability through flexible adjustment to external disturbances. A set of behaviourally derived leg coordination rules can account for decentralised generation of different gaits, and allows stable walking of the insect model as well as of a number of legged robots. (II) Second, a wide range of different leg movements must be possible, e.g. to search for foothold, grasp for objects or groom the body surface. We present a simple neural network controller that can simulate targeted swing trajectories, obstacle avoidance reflexes and cyclic searching-movements. (III) Third, control of mechanically coupled joints of the legs in stance is achieved by exploiting the physical interactions between body, legs and substrate. A local positive displacement feedback, acting on individual leg joints, transforms passive displacement of a joint into active movement, generating synergistic assistance reflexes in all mechanically coupled joints.     

Virtual Constraint Based Control of Bounding Gait of Quadruped Robots

This paper presents a control approach for bounding gait of quadruped robots by applying the concept of Virtual Constraints (VCs).A VC is a relative motion relation between two related joints imposed to the robots in terms of a specified gait,which can drive the robot to run with desired gait.To determine VCs for highly dynamic bounding gait,the limit cycle motions of the passive dynamic model of bounding gait are analyzed.The leg length and hip/shoulder angle trajectories corresponding to the limit cycles are parameterized by leg angles using 4 th-order polynomials.In order to track the calculated periodic motions,the polynomials are imposed on the robot as virtual motion constraints by a high-level state machine controller.A bounding speed feedback strategy is introduced to stabilize the robot running speed and enhance the stability.The control approach was applied to a newly designed lightweight bioinspired quadruped robot,AgiDog.The experimental results demonstrate that the robot can bound at a frequency up to 5 Hz and bound at a maximum speed of 1.2 m·s-1 in sagittal plane with a Froude number approximating to 1.     

Bio-Inspired Controller for a Robot Cheetah with a Neural Mechanism Controlling Leg Muscles

The realization of a high-speed running robot is one of the most challenging problems in developing legged robots.The excellent performance of cheetahs provides inspiration for the control and mechanical design of such robots.This paper presents a three-dimensional model of a cheetah that predicts the locomotory behaviors of a running cheetah.Applying biological knowledge of the neural mechanism,we control the muscle flexion and extension during the stance phase,and control the positions of the joints in the flight phase via a PD controller to minimize complexity.The proposed control strategy is shown to achieve similar locomotion of a real cheetah.The simulation realizes good biological properties,such as the leg retraction,ground reaction force,and spring-like leg behavior.The stable bounding results show the promise of the controller in high-speed locomotion.The model can reach 2.7 m·s- 1 as the highest speed,and can accelerate from 0 to 1.5 m·s -1 in one stride cycle.A mechanical structure based on this simulation is designed to demonstrate the control approach,and the most recently developed hindlimb controlled by the proposed controller is presented in swinging-leg experiments and jump-force experiments.     

Running over unknown rough terrain with a one-legged planar robot

The ability to traverse unknown, rough terrain is an advantage that legged locomoters have over their wheeled counterparts. However, due to the complexity of multi-legged systems, research in legged robotics has not yet been able to reproduce the agility found in the animal kingdom. In an effort to reduce the complexity of the problem, researchers have developed single-legged models to gain insight into the fundamental dynamics of legged running. Inspired by studies of animal locomotion, researchers have proposed numerous control strategies to achieve stable, one-legged running over unknown, rough terrain. One such control strategy incorporates energy variations into the system during the stance phase by changing the force-free leg length as a sinusoidal function of time. In this research, a one-legged planar robot capable of implementing this and other state-of-the-art control strategies was designed and built. Both simulated and experimental results were used to determine and compare the stability of the proposed controllers as the robot was subjected to unknown drop and raised step perturbations equal to 25% of the nominal leg length. This study illustrates the relative advantages of utilizing a minimal-sensing, active energy removal control scheme to stabilize running over rough terrain.     

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.     

Integrating neuromorphic action-oriented perceptual inputs to generate a navigation behaviour for a robot

We use neural networks with pointer map architectures to provide simple attentional processing in a robotic task. A pointer map comprises a map of neurons that encode a stimulus. Besides global feedback inhibition, the map receives feedback excitation via a small group of pointer neurons that encode the location of a salient stimulus on the map as a vectorial representation. The pointer neurons are able to apply selective processing to a particular region of the network. The robot uses these properties to manoeuver in relation to an attended object. We implemented a controller composed of two pointer maps, and a motor map. The first pointer map reports the direction of a salient obstacle in a one-dimensional map of distance derived from infrared sensors. The second pointer map reports the direction to potential obstacles in a two-dimensional edge-enhanced image derived from a forward looking CCD-camera. These outputs are applied to a motor map, where they bias the motor control signals issued to the robots wheels, according to navigational intentions.     

Modeling dynamical interactions between fast and slow movements: fast saccadic eye movement behavior in the presence of the slower VOR

An ongoing controversy has to do with the interactions between “fast” (saccadic, quick phase) and “slow” (all other) eye movements. By attacking such issues with both experimental and especially simulation studies using our nonlinear sixth order reciprocally innervated model of the eye mechanical system, insights can be gained into the nature of these nontrivial phenomena. In our present study we relied both (1) on simulation of saccades under a wide range of experimental conditions [vestibular ocular reflex (VOR) velocities from -100 to 100 deg/sec, VOR induced position ranges from -30 to 30 degrees, time-optimal saccades ranging from 2 to 40 degrees], and (2) on using a wide variety of computer simulation of eye movement models, ranging from nonlinear ones with first and especially second order multipulse step controller signal structures, to different controller signal interaction schemes, to simulation using linearized models. We have isolated two important nonlinear phenomena: a level I nonlinear mechanical interaction, dependent not only on the initial velocity but also on the “position effect,” a new finding; and a level II nonlinear neurological interaction, close to “squelching” of the VOR controller signals by the dominating saccadic signal. Furthermore, we have used our simulation findings to reinterpret others' experimental data on eye movement interactions, including saccadic-smooth pursuit, saccadic-vergence, and vestibular nystagmus.     

Significance of the relative position in space of associated stimuli for formation and performance of motor conditioned reflexes

In experiments on cats the rate of formation of conditioned reflexes to sound (running to the feeding trough) depended on the spatial interrelations of the paired stimuli: the closer the source of the conditioned signal to the feeding trough, the sooner the formation of the conditioned reflex. It has been assumed that during formation of a conditioned reflex the closing of connection between conditioned and unconditioned stimuli is also attended with the closing of connection between the spatial parameters of the paired stimuli. Experiments with inactivation through cold of the temporal area (cortical representation of the vestibular system) of one hemisphere have shown that such a connection is formed in central parts of the vestibular analyser. When the conditioned reflex is elaborated to one feeding trough, the connection is duplicated by both hemispheres; in reflexes to two feeding troughs (i.e. spatial choice) such connections are lateralized in each hemisphere.     

Afferent signals from pigeon extraocular muscles modify the vestibular responses of units in the abducens nucleus.

Although the extraocular muscles contain stretch receptors it is generally believed that their afferents exert no influence on the control of eye movement. However, we have shown previously that these afferent signals reach various brainstem centres concerned with eye movement, notably the vestibular nuclei, and that the decerebrate pigeon is a favourable preparation in which to study their effects. If the extraocular muscle afferents do influence oculomotor control from moment-to-moment they should exert a demonstrable effect on the oculomotor nuclei. We now present evidence that extraocular muscle afferent signals do, indeed, alter the responses of units in an oculomotor nucleus (the abducens, VI nerve nucleus, which supplies the lateral rectus muscle) to horizontal, vestibular stimulation induced by sinusoidal oscillation of the bird. Such stimuli evoke a vestibulo-ocular reflex in the intact bird. The extraocular stretch receptors were activated by passive eye movement within the pigeon's saccadic range; such movements modified the vestibular responses of all 19 units studied which were all, histologically, in the abducens nucleus. The magnitude of the effects, purely inhibitory in 15 units, depended both on the amplitude and the velocity of the eye movement and most units showed selectivity for particular combinations of plane (e.g. horizontal versus vertical) and direction (e.g. rostral versus caudal) of eye movement. The results show that an afferent signal from the extraocular muscles influences vestibularly driven activity in the abducens nucleus to which it carries information related to amplitude, velocity, plane and direction of eye movement in the saccadic range. They thus strongly support the view that extraocular afferent signals are involved in the control of eye movement.     

Propulsive Velocity Optimization of 3-Joint Fish Robot Using Genetic-Hill Climbing Algorithm

Underwater robot is a new research field which is emerging quickly in recent years.Previous researches in this field focuson Remotely Operated Vehicles(ROVs),Autonomous Underwater Vehicles(AUVs),underwater manipulators,etc.Fish robot,which is a new type of underwater biomimetic robot,has attracted great attention because of its silence in moving and energyefficiency compared to conventional propeller-oriented propulsive mechanism.However,most of researches on fish robots have been carried out via empirical or experimental approaches,not based ondynamic optimality.In this paper,we proposed an analytical optimization approach which can guarantee the maximum propulsivevelocity of fish robot in the given parametric conditions.First,a dynamic model of 3-joint(4 links)carangiform fishrobot is derived,using which the influences of parameters of input torque functions,such as amplitude,frequency and phasedifference,on its velocity are investigated by simulation.Second,the maximum velocity of the fish robot is optimized bycombining Genetic Algorithm(GA)and Hill Climbing Algorithm(HCA).GA is used to generate the initial optimal parametersof the input functions of the system.Then,the parameters are optimized again by HCA to ensure that the final set of parametersis the"near"global optimization.Finally,both simulations and primitive experiments are carried out to prove the feasibility ofthe proposed method.     

Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots

Various mechanisms have recently been developed that combine linkage mechanisms and wheels. In particular, the combination of passive linkage mechanisms and small wheels is a main research trend because standard wheeled mobile mechanisms find it difficult to move on rough terrain. In our previous research, a six-wheel mobile robot employing a passive linkage mechanism has been developed to enhance maneuverability and was able to climb over a 0.20 m bump and stairs. We designed a hybrid velocity and torque controller using a neural network since simple velocity controllers fail to climb up. In this paper, we propose an environment recognition system for a wheeled mobile robot that consists of multiple classification analyses to make the robot more adaptive to various environments by selecting a suitable system such as decision making, navigation and controller using the result of the environment recognition system. We evaluate the recognition performance in operation environments; slopes, bumps and stairs by comparing principle component, k-means and self-organizing map analyses.     

Biomimetic Motion Control System Based on a CPG for an Amphibious Multi-Link Mobile Robot

Robots and robotics technologies are expected to provide new tools for inspection and manipulation, especially in extreme environments that are dangerous for human beings to access directly, such as underwater environments, volcanic areas, or nuclear power plants. Robots designed for such extreme environments should be sufficiently robust and strong to cope with disturbance and breakdowns. We focus on the movement of animals to realize robust robot systems. One approach is to mimic the nervous systems of animals. The central pattern generator of a nervous system has been shown to control motion patterns, such as walking, respiration and flapping. In this paper, a robot motion control system using a central pattern generator is proposed and applied to an amphibious multi-link mobile robot.     

A biologically inspired neural net for trajectory formation and obstacle avoidance

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.     

Visually guided gait modifications for stepping over an obstacle: a bio-inspired approach

There is an increasing interest in conceiving robotic systems that are able to move and act in an unstructured and not predefined environment, for which autonomy and adaptability are crucial features. In nature, animals are autonomous biological systems, which often serve as bio-inspiration models, not only for their physical and mechanical properties, but also their control structures that enable adaptability and autonomy—for which learning is (at least) partially responsible. This work proposes a system which seeks to enable a quadruped robot to online learn to detect and to avoid stumbling on an obstacle in its path. The detection relies in a forward internal model that estimates the robot’s perceptive information by exploring the locomotion repetitive nature. The system adapts the locomotion in order to place the robot optimally before attempting to step over the obstacle, avoiding any stumbling. Locomotion adaptation is achieved by changing control parameters of a central pattern generator (CPG)-based locomotion controller. The mechanism learns the necessary alterations to the stride length in order to adapt the locomotion by changing the required CPG parameter. Both learning tasks occur online and together define a sensorimotor map, which enables the robot to learn to step over the obstacle in its path. Simulation results show the feasibility of the proposed approach.     

Cross-Modal Calibration of Vestibular Afference for Human Balance

To determine how the vestibular sense controls balance, we used instantaneous head angular velocity to drive a galvanic vestibular stimulus so that afference would signal that head movement was faster or slower than actual. In effect, this changed vestibular afferent gain. This increased sway 4-fold when subjects (N = 8) stood without vision. However, after a 240 s conditioning period with stable balance achieved through reliable visual or somatosensory cues, sway returned to normal. An equivalent galvanic stimulus unrelated to sway (not driven by head motion) was equally destabilising but in this situation the conditioning period of stable balance did not reduce sway. Reflex muscle responses evoked by an independent, higher bandwidth vestibular stimulus were initially reduced in amplitude by the galvanic stimulus but returned to normal levels after the conditioning period, contrary to predictions that they would decrease after adaptation to increased sensory gain and increase after adaptation to decreased sensory gain. We conclude that an erroneous vestibular signal of head motion during standing has profound effects on balance control. If it is unrelated to current head motion, the CNS has no immediate mechanism of ignoring the vestibular signal to reduce its influence on destabilising balance. This result is inconsistent with sensory reweighting based on disturbances. The increase in sway with increased sensory gain is also inconsistent with a simple feedback model of vestibular reflex action. Thus, we propose that recalibration of a forward sensory model best explains the reinterpretation of an altered reafferent signal of head motion during stable balance.