Study on Decentralization of Spherical Amphibious Multi-robot Control System Based on Smart Contract and Blockchain

With the development of intelligent bionic robots and the improvement of military application,a single robot cannot meet the requirements of the tasks of the current era.The more complex tasks require not only that the robot be able to pass through the field barriers and the amphibious environment,but also that the robot be able to collaborate in a multi-robot system.Consequently,research on the multi-robot control system of spherical amphibious robots is very important.Pres-ently,the main research on amphibious robots is to improve the functions of a single robot,in the absence of the study of the multi-robot control system.Existing systems primarily use a centralized control methodology.Although the transfer of central node can be achieved,there is still a problem of Byzantine fault tolerance in military applications,that is,when the amphibious multi-robot system is invaded by the enemy.The central node may not only fail to accomplish the task,but also lose control of other robots,with severe consequences.To solve the above problems,this paper proposed a decentralized method of spherical amphibious multi-robot control system based on blockchain technology.First,the point-to-point informa-tion network based on long range radio technology of low power wide area network was set up,we designed the blockchain system for embedded application environment and the decentralized hardware and software architecture of multi-robot control system.On this basis,the consensus plugin,smart contract and decentralized multi-robot control algorithm were designed to achieve decentralization.The experimental results of consensus of spherical amphibious multi-robot showed the effectiveness of the decentralization.     

Manuo-ocular coordination in target tracking. I. A model simulating human performance

During eye tracking of a self-moved target, human subjects' performance differs from eye-alone tracking of an external target. Typical latency between target and eye motion onsets is shorter, ocular smooth pursuit (SP) saturation velocity increases and the maximum target motion frequency at which the SP system functions correctly is higher. Based on a previous qualitative model, a quantitative model of the coordination control between the arm motor system and the SP system is presented and evaluated here. The model structure maintains a high level of parallelism with the physiological system. It contains three main parts: the eye motor control (containing a SP branch and a saccadic branch), the arm motor control and the coordination control. The coordination control is achieved via an exchange of information between the arm and the eye sensorimotor systems, mediated by sensory signals (vision, proprioception) and motor command copy. This cross-talk results in improved SP system performance. The model has been computer simulated and the results have been compared with human subjects' behavior observed during previous experiments. The model performance is seen to quantitatively fit data on human subjects. Received: 6 March 1997 / Accepted in revised form: 15 July 1997     

Model-based development of neuroprosthesis for paraplegic patients.

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to the muscles is interrupted. Functional electrical stimulation applied to the lower motor neurons can replace the lacking signals. A so-called neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of functional electrical stimulation. However, the control of multiple joints is difficult due to the complexity, nonlinearity, and time-variance of the system involved. Furthermore, effects such as muscle fatigue, spasticity, and limited force in the stimulated muscle further complicate the control task. Mathematical models of the human musculoskeletal system can support the development of neuroprosthesis. In this article a detailed overview of the existing work in the literature is given and two examples developed by the author are presented that give an insight into model-based development of neuroprosthesis for paraplegic patients. It is shown that modelling the musculoskeletal system can provide better understanding of muscular force production and movement coordination principles. Models can also be used to design and test stimulation patterns and feedback control strategies. Additionally, model components can be implemented in a controller to improve control performance. Eventually, the use of musculoskeletal models for neuroprosthesis design may help to avoid internal disturbances such as fatigue and optimize muscular force output. Furthermore, better controller quality can be obtained than in previous empirical approaches. In addition, the number of experimental tests to be performed with human subjects can be reduced. It is concluded that mathematical models play an increasing role in the development of reliable closed-loop controlled, lower extremity neuroprostheses.     

Modelling the control of interceptive actions

In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics.     

Competing Models of Stability in Complex, Evolving Systems: Kauffman vs. Simon

I criticize Herbert Simon's argument for the claim that complex natural systems must constitute decomposable, mereological or functional hierarchies. The argument depends on certain assumptions about the requirements for the successful evolution of complex systems, most importantly, the existence of stable, intermediate stages in evolution. Simon offers an abstract model of any process that succeeds in meeting these requirements. This model necessarily involves construction through a decomposable hierarchy, and thus suggests that any complex, natural, i.e., evolved, system is constituted by a decomposable hierarchy. I argue that Stuart Kauffman's recent models of genetic regulatory networks succeed in specifying processes that could meet Simon's requirements for evolvability without requiring construction through a decomposable hierarchy. Since Kauffman's models are at least as plausible as Simon's model, Simon's argument that complex natural systems must constitute decomposable, mereological or functional hierarchies does not succeed.     

A myocybernetic control model of skeletal muscle

A mathematical model of skeletal muscle is presented which contains the two physiological control parameters stimulation rate and motor unit recruitment. The model is complete in the sense that it adequately describes all possible contractive states normally occurring in living muscle. The modelling procedure relies entirely on established myo-physiological facts and each assumption made is substantiated by experimental data. Extensive simulation studies reveal that the model is capable of correctly predicting practically all known phenomena of the muscular force-output. A simplified version of the model is also presented, particularly suitable for inclusion as the driving structure in complex musculoskeletal link systems. This version was successfully tested in the prediction of an optimal human motion. The present control model is believed to fill a gap in the literature on models of muscle, and may be expected to provide a sound basis for research into the optimal control aspects of muscular contraction, and to stimulate such research.     

Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint

Biomechanics and Modeling in Mechanobiology - Computational modelling is an invaluable tool for investigating features of human locomotion and motor control which cannot be measured except through...     

Biomechatronics--assisting the impaired motor system

Biomechatronics concerns the interdisciplinary field of interaction with the human neuromuscular-skeletal system with the objective to assist impaired human motor control. In this field technology is developed that integrates neuroscience, robotics, interface and sensor technology, dynamic systems and control theory. The primary issue in this field concerns the concepts of assisting impaired human motor function. The secondary, derived, issue concerns possible methods of interfacing with the human body at all hierarchical levels of the human motor system. The application of motor assist systems may serve several goals: it can take over part of the affected motor control, enable the physiological motor system to perform the desired function or aid in training the impaired physiological system. The progress in these issues are reviewed and their potential implications for assistance of the impaired human motor system are discussed.     

A rheological motor model for vertebrate skeletal muscle in due consideration of non-linearity

A rheological motor model that satisfies the major mechanical properties of the skeletal muscle is proposed. The model consists of two Maxwell elements and a Voigt element connected in parallel with each other and has a force generator in it. The model well explains the mechanical behavior in quick and slow recovery phases in the isometric contraction of the muscle and achieves a sufficient isotonic shortening speed. The energy liberation of the motor in isotonic contraction is calculated and a mechanism of control is proposed, which operates so as to decrease the dissipated energy by altering the weights of the elastic and viscous constants in Maxwell elements. And thereby it becomes possible for the motor to possess non-linearity in energy liberation and load-velocity relation alike in muscle. The model would be a base model to be utilized for analyzing the kinetics of human macrosystems and/or for modeling the human neuromuscular system of motion control.     

Spinal Mechanisms May Provide a Combination of Intermittent and Continuous Control of Human Posture: Predictions from a Biologically Based Neuromusculoskeletal Model

Several models have been employed to study human postural control during upright quiet stance. Most have adopted an inverted pendulum approximation to the standing human and theoretical models to account for the neural feedback necessary to keep balance. The present study adds to the previous efforts in focusing more closely on modelling the physiological mechanisms of important elements associated with the control of human posture. This paper studies neuromuscular mechanisms behind upright stance control by means of a biologically based large-scale neuromusculoskeletal (NMS) model. It encompasses: i) conductance-based spinal neuron models (motor neurons and interneurons); ii) muscle proprioceptor models (spindle and Golgi tendon organ) providing sensory afferent feedback; iii) Hill-type muscle models of the leg plantar and dorsiflexors; and iv) an inverted pendulum model for the body biomechanics during upright stance. The motor neuron pools are driven by stochastic spike trains. Simulation results showed that the neuromechanical outputs generated by the NMS model resemble experimental data from subjects standing on a stable surface. Interesting findings were that: i) an intermittent pattern of muscle activation emerged from this posture control model for two of the leg muscles (Medial and Lateral Gastrocnemius); and ii) the Soleus muscle was mostly activated in a continuous manner. These results suggest that the spinal cord anatomy and neurophysiology (e.g., motor unit types, synaptic connectivities, ordered recruitment), along with the modulation of afferent activity, may account for the mixture of intermittent and continuous control that has been a subject of debate in recent studies on postural control. Another finding was the occurrence of the so-called “paradoxical” behaviour of muscle fibre lengths as a function of postural sway. The simulations confirmed previous conjectures that reciprocal inhibition is possibly contributing to this effect, but on the other hand showed that this effect may arise without any anticipatory neural control mechanism.     

Manuo-ocular coordination in target tracking. II. Comparing the model with human behavior

Several studies have shown that humans track a moving visual target with their eyes better if the movement of this target is directly controlled by the observer's hand. The improvement in performance has been attributed to coordination control between the arm motor system and the smooth pursuit (SP) system. In such a task, the SP system shows characteristics that differ from those observed during eye-alone tracking: latency (between the target-arm and the eye motion onsets) is shorter, maximum SP velocity is higher and the maximum target motion frequency at which the SP can function effectively is also higher. The aim of this article is to qualitatively evaluate the behavior of a dynamical model simulating the oculomotor system and the arm motor system when both are involved in tracking visual targets. The evaluation is essentially based on a comparison of the behavior of the model with the behavior of human subjects tracking visual targets under different conditions. The model has been introduced and quantitatively evaluated in a companion paper. The model is based on an exchange of internal information between the two sensorimotor systems, mediated by sensory signals (vision, arm muscle proprioception) and motor signals (arm motor command copy). The exchange is achieved by a specialized structure of the central nervous system, previously identified as a part of the cerebellum. Computer simulation of the model yielded results that fit the behavior of human subjects observed during previously reported experiments, both qualitatively and quantitatively. The parallelism between physiology and human behavior on the one hand, and structure and simulation of the model on the other hand, is discussed. Received: 6 March 1997 / Accepted in revised form: 15 July 1997     

A mathematical model of the adaptive control of human arm motions

This paper discusses similarities between models of adaptive motor control suggested by recent experiments with human and animal subjects, and the structure of a new control law derived mathematically from nonlinear stability theory. In both models, the control actions required to track a specified trajectory are adaptively assembled from a large collection of simple computational elements. By adaptively recombining these elements, the controllers develop complex internal models which are used to compensate for the effects of externally imposed forces or changes in the physical properties of the system. On a motor learning task involving planar, multi-joint arm motions, the simulated performance of the mathematical model is shown to be qualitatively similar to observed human performance, suggesting that the model captures some of the interesting features of the dynamics of low-level motor adaptation. Received: 20 September 1994 / Accepted in revised form: 18 November 1998     

Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal-musculo-skeletal model

Early human motor development has the nature of spontaneous exploration and boot-strap learning, leading to open-ended acquisition of versatile flexible motor skills. Since dexterous motor skills often exploit body-environment dynamics, we formulate the developmental principle as the spontaneous exploration of consistent dynamical patterns of the neural-body-environment system. We propose that partially ordered dynamical patterns emergent from chaotic oscillators coupled through embodiment serve as the core driving mechanism of such exploration. A model of neuro-musculo-skeletal system is constructed capturing essential features of biological systems. It consists of a skeleton, muscles, spindles, tendon organs, spinal circuits, medullar circuits (CPGs), and a basic cortical model. Through a series of experiments with a minimally simple body model, it is shown that the model has the capability of generating partially ordered behavior, a mixture of chaotic exploration and ordered entrained patterns. Models of self-organizing cortical areas for primary somatosensory and motor areas are introduced. They participate in the explorative learning by simultaneously learning and controlling the movement patterns. A scaled up version of the model, a human infant model, is constructed and put through preliminary experiments. Some meaningful motor behavior emerged including rolling over and crawling-like motion. The results show the possibility that a rich variety of meaningful behavior can be discovered and acquired by the neural-body dynamics without pre-defined coordinated control circuits.     

Modelling of direct motor program learning in fast human arm motions

We propose and simulate a new paradigm for organization of motor control in fast and accurate human arm motions. We call the paradigm direct motor program learning since the control programs are learned directly without knowing or learning the dynamics of a controlled system.The idea is to approximate the dependence of the motor control programs on the vector of the task parameters rather than to use a model of the system dynamics. We apply iterative learning control and scattered data multivariate approximation techniques to achieve the goal. The advantage of the paradigm is that the control complexity depends neither on the order nor on the nonlinearity of the system dynamics.We simulate the direct motor program learning paradigm in the task of point-to-point control of fast planar human arm motions. Simulation takes into account nonlinear arm dynamics, muscle force dynamics, delay in low-level reflex feedback, time dependence of the feedback gains and coactivation of antagonist muscles. Despite highly nonlinear time-variant dynamics of the controlled system, reasonably good motion precision is obtained over a wide range of the task parameters (initial and final positions of the arm). The simulation results demonstrate that the paradigm is indeed viable and could be considered as a possible explanation for the organization of motor control of fast motions.     

OMNIITOX concept model supports characterisation modelling for life cycle impact assessment

Goal Scope Background  The main focus in OMNIITOX is on characterisation models for toxicological impacts in a life cycle assessment (LCA) context. The OMNIITOX information system (OMNIITOX IS) is being developed primarily to facilitate characterisation modelling and calculation of characterisation factors to provide users with information necessary for environmental management and control of industrial systems. The modelling and implementation of operational characterisation models on eco and human toxic impacts requires the use of data and modelling approaches often originating from regulatory chemical risk assessment (RA) related disciplines. Hence, there is a need for a concept model for the data and modelling approaches that can be interchanged between these different contexts of natural system model approaches. Methods. The concept modelling methodology applied in the OMNIITOX project is built on database design principles and ontological principles in a consensus based and iterative process by participants from the LCA, RA and environmental informatics disciplines. Results. The developed OMNIITOX concept model focuses on the core concepts of substance, nature framework, load, indicator, and mechanism, with supplementary concepts to support these core concepts. They refer to the modelled cause, effect, and the relation between them, which are aspects inherent in all models used in the disciplines within the scope of OMNIITOX. This structure provides a possibility to compare the models on a fundamental level and a language to communicate information between the disciplines and to assess the possibility of transparently reusing data and modelling approaches of various levels of detail and complexity. Conclusions  The current experiences from applying the concept model show that the OMNIITOX concept model increases the structuring of all information needed to describe characterisation models transparently. From a user perspective the OMNIITOX concept model aids in understanding the applicability, use of a characterisation model and how to interpret model outputs. Recommendations and Outlook  The concept model provides a tool for structured characterisation modelling, model comparison, model implementation, model quality management, and model usage. Moreover, it could be used for the structuring of any natural environment cause-effect model concerning other impact categories than toxicity.     

Analysis of reflex modulation with a biologically realistic neural network

In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor. Action Editor: Karen Sigvardt     

Impedance characteristics of a neuromusculoskeletal model of the human arm II. Movement control

The modulation of neuromusculoskeletal impedance during movements is analysed using a motor control model of the human arm. The motor control system combines feedback and feedforward control and both control modes are determined in one optimization process. In the model, the stiffness varies at the double movement frequency for 2-Hz oscillatory elbow movements and has high values at the movement reversals. During goal-directed two-degrees-of-freedom arm movements, the stiffness is decreased during the movement and may be increased in the initial and final phases, depending on the movement velocity. The stiffness has a considerable curl during the movement, as was also observed in experimental data. The dynamic stiffness patterns of the model can be explained basically by the α−γ coactivation scheme where feedback gains covary with motor control signals. In addition to the modulation of the gain factors, it is argued that the variation of the intrinsic stiffness has a considerable effect on movement control, especially during fast movements. Received: 14 October 1997 / Accepted in revised form: 18 May 1999     

A complete set of control equations for the human musculo-skeletal system

A mathematical model of the total human musculo-skeletal system is presented. The model comprises a link-mechanical and a musculo-mechanical set of ordinary first-order differential equations which describe the dynamics of the segment model and muscle model respectively. The interdependence of the two sets of equations is demonstrated. The set of musculo-mechanical equations contains the two neuromuscular control parameters motor unit recruitment and stimulation rate, and the significance of such a representation for a control-theoretical treatment of musculo-skeletal systems is discussed. Finally, after a short discussion of the successful application of the present model in the prediction of an optimal human motion, further possibilities are indicated of the use of the model for investigations into the control behaviour of musculo-skeletal systems.     

Computational nature of human adaptive control during learning of reaching movements in force fields

Learning to make reaching movements in force fields was used as a paradigm to explore the system architecture of the biological adaptive controller. We compared the performance of a number of candidate control systems that acted on a model of the neuromuscular system of the human arm and asked how well the dynamics of the candidate system compared with the movement characteristics of 16 subjects. We found that control via a supra-spinal system that utilized an adaptive inverse model resulted in dynamics that were similar to that observed in our subjects, but lacked essential characteristics. These characteristics pointed to a different architecture where descending commands were influenced by an adaptive forward model. However, we found that control via a forward model alone also resulted in dynamics that did not match the behavior of the human arm. We considered a third control architecture where a forward model was used in conjunction with an inverse model and found that the resulting dynamics were remarkably similar to that observed in the experimental data. The essential property of this control architecture was that it predicted a complex pattern of near-discontinuities in hand trajectory in the novel force field. A nearly identical pattern was observed in our subjects, suggesting that generation of descending motor commands was likely through a control system architecture that included both adaptive forward and inverse models. We found that as subjects learned to make reaching movements, adaptation rates for the forward and inverse models could be independently estimated and the resulting changes in performance of subjects from movement to movement could be accurately accounted for. Results suggested that the adaptation of the forward model played a dominant role in the motor learning of subjects. After a period of consolidation, the rates of adaptation in the internal models were significantly larger than those observed before the memory had consolidated. This suggested that consolidation of motor memory coincided with freeing of certain computational resources for subsequent learning. Received: 01 January 1998 / Accepted in revised form: 26 January 1999