Bio-inspired Control Research Articles

\u2018Falling heads\u2019: investigating reflexive responses to head\u2013neck perturbations

BackgroundReflexive responses to head–neck perturbations affect the injury risk in many different situations ranging from sports-related impact to car accident scenarios. Although several experiments have been conducted to investigate these head–neck responses to various perturbations, it is still unclear why and how individuals react differently and what the implications of these different responses across subjects on the potential injuries might be. Therefore, we see a need for both experimental data and biophysically valid computational Human Body Models with bio-inspired muscle control strategies to understand individual reflex responses better.MethodsTo address this issue, we conducted perturbation experiments of the head–neck complex and used this data to examine control strategies in a simulation model. In the experiments, which we call ’falling heads’ experiments, volunteers were placed in a supine and a prone position on a table with an additional trapdoor supporting the head. This trapdoor was suddenly released, leading to a free-fall movement of the head until reflexive responses of muscles stopped the downwards movement.ResultsWe analysed the kinematic, neuronal and dynamic responses for all individuals and show their differences for separate age and sex groups. We show that these results can be used to validate two simple reflex controllers which are able to predict human biophysical movement and modulate the response necessary to represent a large variability of participants.ConclusionsWe present characteristic parameters such as joint stiffness, peak accelerations and latency times. Based on this data, we show that there is a large difference in the individual reflexive responses between participants. Furthermore, we show that the perturbation direction (supine vs. prone) significantly influences the measured kinematic quantities. Finally, ’falling heads’ experiments data are provided open-source to be used as a benchmark test to compare different muscle control strategies and to validate existing active Human Body Models directly.

Distributed leader-following formation control for multiple nonholonomic mobile robots via bioinspired neurodynamic approach

This paper investigates the distributed leader-following formation control problem for multiple nonholonomic wheeled mobile robots via a bioinspired neurodynamic approach. To do this, first, we develop a distributed estimator for each follower robot which uses its own information and the information of its neighboring robots to estimate the leader’s states. Second, a formation tracking control law is proposed for each follower robot based on the estimated states of the leader using the backstepping technique. Then, a bioinspired neurodynamics-based backstepping controller is designed to solve the impractical velocity jumps problem. Furthermore, Lyapunov function-based approach is utilized to derive sufficient conditions which guarantee asymptotic stability of the multiple mobile robot system. Finally, simulation results are presented to illustrate the effectiveness of the proposed controllers.

Gait switching with phase reversal of locomotory waves in the centipede Scolopocryptops rubiginosus

Crawling using locomotory waves is a common method of locomotion for limbless and many-legged invertebrates and stimulates the biomimetic engineering of flexible locomotion. It is generally believed that the direction of locomotory waves is fixed for a given species. However, we found that a centipede, Scolopocryptops rubiginosus, flexibly generated its gait to allow for locomotory waves that varied in direction, depending on (i) locomotion speed and (ii) the physical conditions of terrain. We also found a new type of centipede’s swimming gait unlike eel-like way known so far which is using posteriorly traveling waves of horizontal body undulation. The gait patterns of the centipede were examined in various conditions and analyzed how the waves switched in detailed. We showed that gait patterns were associated with control of stride length rather than stride frequency. Discussion was made on a possible scenario of the gait transition in the centipede compatible with our observations. This finding may give a hint at bio-inspired control of flexible gait switching in response to irregular terrain.

Cooperative ankle-exoskeleton control can reduce effort to recover balance after unexpected disturbances during walking

BackgroundIn the last two decades, lower-limb exoskeletons have been developed to assist human standing and locomotion. One of the ongoing challenges is the cooperation between the exoskeleton balance support and the wearer control. Here we present a cooperative ankle-exoskeleton control strategy to assist in balance recovery after unexpected disturbances during walking, which is inspired on human balance responses.MethodsWe evaluated the novel controller in ten able-bodied participants wearing the ankle modules of the Symbitron exoskeleton. During walking, participants received unexpected forward pushes with different timing and magnitude at the pelvis level, while being supported (Exo-Assistance) or not (Exo-NoAssistance) by the robotic assistance provided by the controller. The effectiveness of the assistive strategy was assessed in terms of (1) controller performance (Detection Delay, Joint Angles, and Exerted Ankle Torques), (2) analysis of effort (integral of normalized Muscle Activity after perturbation onset); and (3) Analysis of center of mass COM kinematics (relative maximum COM Motion, Recovery Time and Margin of Stability) and spatio-temporal parameters (Step Length and Swing Time).ResultsIn general, the results show that when the controller was active, it was able to reduce participants’ effort while keeping similar ability to counteract and withstand the balance disturbances. Significant reductions were found for soleus and gastrocnemius medialis activity of the stance leg when comparing Exo-Assistance and Exo-NoAssistance walking conditions.ConclusionsThe proposed controller was able to cooperate with the able-bodied participants in counteracting perturbations, contributing to the state-of-the-art of bio-inspired cooperative ankle exoskeleton controllers for supporting dynamic balance. In the future, this control strategy may be used in exoskeletons to support and improve balance control in users with motor disabilities.

Simulation of Spinal Muscle Control in Human Gait Using OpenSim

This paper presents a neuro-musculoskeletal simulation approach to human gait based on an original model of central pattern generator and the muscle synergy approach. The controller, shown here, aims at simplifying movement control by reducing the number of parameters to optimize. The model of the simplified motor coordination chain was built, from the midbrain through spinal neurons to the muscles allowing of simulating some neural and muscular values of gait, hard to obtain otherwise. The work also includes a bio-inspired reflex-based balance controller designed from knowledge of spinal gait. The used anatomic musculoskeletal model has been implemented in OpenSim platform. Time simulations show the ability of the platform to be able to produce different gait patterns including nominal and disturbed. Both gaits are controlled by the same bio-inspired closed-loop CPG- and reflex-based circuitries. After 2 second standing phase, the model was able to walk for about 5 meters with 10 steps. 2 steps were disrupted in the second simulation.

Motor Learning for Manoeuvring Control of a Remotely Operated Vehicle

The paper investigates the application of a bio-inspired control paradigm encompassing nonlinear function approximation and adaptive learning to design a manoeuvring controller for a remotely operated vehicle. The bio-inspired learning controller is adopted to overcome limitations introduced by unknown nonlinear dynamics as well as by environmental disturbances. The proposed controller is compared against a traditional adaptive controller with respect to tracking performance and control effort. Simulation results show that the bio-inspired controller displays better tracking performance when the structure of the system dynamics is unknown. Further, the paper shows that offline training of the functional approximator helps in delivering a smoother control effort, and that a bio-inspired integral action fulfils the disturbance rejection.

An IoT architecture based on the control of Bio Inspired manufacturing system for the detection of anomalies with vibration sensors

This work presents an IoT architecture for the detection of anomalies in motors with vibration sensors using a real-time autoen-coder, based on a new bio-inspired control architecture for recently proposed manufacturing systems. Unlike other approaches, this work analyzes the behavior of the anomaly detection system in real time, seeking to cover the new requirements for real-time processing and scalability in control systems. A neural network is implemented to control anomalies based on a bio-inspired architecture, achieving the detection of anomalies in the time domain based on the evaluation of different models based on recurrent neural networks. Similarly, an evaluation is shown regarding the latency of each component of the system, thus finding possible bottlenecks in real-time operation. The system was implemented on a prototype conveyor belt with low-cost accelerometers, commercial-use microcontrollers, and free software.

MECHANICAL DESIGN, ACTUATION AND CONTROL OF BIOINSPIRED SOFT SURGICAL ROBOTS

The design of surgical soft robots is one of the most challenging problems in modern robotics. In addition to the still pending problems of general soft robotics i.e. selection/design of materials, actuators, and mechanical structures, there are serious limitations in terms of their operating environment and conditions: interaction with biological tissues, the effect of sterilization and cleaning liquids, the shape and dimensions of the working space, as well as often occurring accidents during the surgery which poses new robotic control problems to be studied. As it is known, each object of bioinspiration (elephant trunk, octopus leg, etc.) has its own type of motion, kinematic structure, and control, therefore the comparative analysis of soft robots according to different objects of bioinspiration will facilitate their selection and application in minimally invasive surgery (MIS), taking into account, of course, the limitations associated with the surgery. A review of modern literature on mechanical design, methods and technical means of actuation and control of the main types of bioinspired soft surgical robots is presented.The design and development problems, including material selection, mechanical design and optimization, actuation, and bioinspired control for each type of the existing soft surgical robots are discussed. The results and recommendations of this study will help to develop new samples of soft surgical robotic devices with task-specific functional characteristics necessary for MIS applications.

Adapting Highly-Dynamic Compliant Movements to Changing Environments: A Benchmark Comparison of Reflex- vs. CPG-Based Control Strategies.

To control highly-dynamic compliant motions such as running or hopping, vertebrates rely on reflexes and Central Pattern Generators (CPGs) as core strategies. However, decoding how much each strategy contributes to the control and how they are adjusted under different conditions is still a major challenge. To help solve this question, the present paper provides a comprehensive comparison of reflexes, CPGs and a commonly used combination of the two applied to a biomimetic robot. It leverages recent findings indicating that in mammals both control principles act within a low-dimensional control submanifold. This substantially reduces the search space of parameters and enables the quantifiable comparison of the different control strategies. The chosen metrics are motion stability and energy efficiency, both key aspects for the evolution of the central nervous system. We find that neither for stability nor energy efficiency it is favorable to apply the state-of-the-art approach of a continuously feedback-adapted CPG. In both aspects, a pure reflex is more effective, but the pure CPG allows easy signal alteration when needed. Additionally, the hardware experiments clearly show that the shape of a control signal has a strong influence on energy efficiency, while previous research usually only focused on frequency alignment. Both findings suggest that currently used methods to combine the advantages of reflexes and CPGs can be improved. In future research, possible combinations of the control strategies should be reconsidered, specifically including the modulation of the control signal's shape. For this endeavor, the presented setup provides a valuable benchmark framework to enable the quantitative comparison of different bioinspired control principles.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms.

User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

Design, Control, Modeling, and Simulation of Mechanical Ventilator for Respiratory Support

In the early stage of the 21st century, humankind is facing high medical risks. To the best of our knowledge, there is currently no efficient way to stop chains of infections, and hence citizens suffer significantly increasing numbers of diseases. The most important factor in this scenario is the lack of necessary equipment to cure disease and maintain our living. Once breath cannot be guaranteed, humans find themselves in a dangerous state. This study aimed to design, control, model, and simulate mechanical ventilator that is open-source structure, lightweight, and portable, which is proper for patients to cure themselves at home. In the scope of this research, the hardware platform for the mechanical design, implementation of control rules, and some trials of both simulations and experiments are presented as our methodology. The proposed design of ventilator newly features the bioinspired mechanism, finger-like actuator, and flow rate-based control. Firstly, the approximate evaluation of the lung model is presented with some physiological characteristics. Owing to this investigation, the control scheme was established to adapt to the biological body. Moreover, it is essential for the model to be integrated to determine the appropriate performance of the closed-loop system. Derived from these theoretical computations, the innovative concept of mechanical design was demonstrated using the open-source approach, and the real-world model was constructed. In order to estimate the driving torque, the hardware modeling was conducted using mathematical expressions. To validate the proposed approach, the overall system was evaluated using Matlab/Simulink, and experiments with the proposed platform were conducted in two situations: 20 lpm as a reference flow rate for 4 seconds and 45 lpm for 2.5 seconds, corresponding to normal breath and urgent breath. From the results of this study, it can be clearly observed that the system’s performance ensures that accurate airflow is provided, although the desired airflow fluctuates. Based on the test scenario in hardware, the RMS (root-mean-square) values of tracking errors in airflow for both cases were 1.542 and 1.767. The proposed design could deal with changes in airflow, and this machine could play a role as a proper, feasible, and robust solution to support human living.

Bio-Inspired Conceptual Mechanical Design and Control of a New Human Upper Limb Exoskeleton

Safe operation, energy efficiency, versatility and kinematic compatibility are the most important aspects in the design of rehabilitation exoskeletons. This paper focuses on the conceptual bio-inspired mechanical design and equilibrium point control (EP) of a new human upper limb exoskeleton. Considering the upper limb as a multi-muscle redundant system, a similar over-actuated but cable-driven mechatronic system is developed to imitate upper limb motor functions. Additional torque adjusting systems at the joints allow users to lift light weights necessary for activities of daily living (ADL) without increasing electric motor powers of the device. A theoretical model of the “ideal” artificial muscle exoskeleton is also developed using Hill’s natural muscle model. Optimal design parameters of the exoskeleton are defined using the differential evolution (DE) method as a technique of a multi-objective optimization. The proposed cable-driven exoskeleton was then fabricated and tested on a healthy subject. Results showed that the proposed system fulfils the desired aim properly, so that it can be utilized in the design of rehabilitation robots. Further studies may include a spatial mechanism design, which is especially important for the shoulder rehabilitation, and development of reinforcement learning control algorithms to provide more efficient rehabilitation treatment.

Adjustable Compliance and Force Feedback as Key Elements for Stable and Efficient Hopping

Achieving efficient and human-like hopping motions with consistent consecutive patterns requires proper mechanical design and control. In this regard, we introduce a new bioinspired design and control approach comprised of a hybrid actuation system and force-based compliance control. Combining an electric motor with a pneumatic artificial muscle makes up the so-called Electric-Pneumatic Actuation (EPA) system which enables presetting the leg compliance. Using the ground reaction force for online adjustment of EPA allows controlling the leg stiffness. This approach combines a simplified version of reflex-based control and physical impedance adjustment, that resembles the human neuromuscular system in a parsimonious way. By optimizing PAM pressures and control gains for different targeted hopping heights using Bayesian optimization, we show experimentally that the proposed approach generates various stable hopping motions and yields efficient performance. Moreover, our method is shown to be capable of creating more human-like hopping patterns compared to the previous studies on the same robot.

Bio-Inspired Take-Off Maneuver and Control in Vertical Jumping for Quadruped Robot with Manipulator.

The jumping motion of legged robots is an effective way to overcome obstacles in the rugged microgravity planetary exploration environment. At the same time, a quadruped robot with a manipulator can achieve operational tasks during movement, which is more practical. However, the additional manipulator will restrict the jumping ability of the quadruped robot due to the increase in the weight of the system, and more active degrees of freedom will increase the control complexity. To improve the jumping height of a quadruped robot with a manipulator, a bio-inspired take-off maneuver based on the coordination of upper and lower limbs is proposed in this paper. The kinetic energy and potential energy of the system are increased by driving the manipulator-end (ME) to swing upward, and the torso driven by the legs will delay reaching the required peak speed due to the additional load caused by the accelerated ME. When the acceleration of ME is less than zero, it will pull the body upward, which reduces the peak power of the leg joints. Therefore, the jumping ability of the system is improved. To realize continuous and stable jumping, a control framework based on whole-body control was established, in which the quadruped robot with a manipulator was a simplified floating seven-link model, and the hierarchical optimization was used to solve the target joint torques. This method greatly simplifies the dynamic model and is convenient for calculation. Finally, the jumping simulations in different gravity environments and a 15° slope were performed. The jump heights have all been improved after adding the arm swing, which verified the superiority of the bio-inspired take-off maneuver proposed in this paper. Furthermore, the stability of the jumping control method was testified by the continuous and stable jumping.

Bio-Inspired Design, Modeling, and Control of Robotic Fish Propelled by a Double-Slider-Crank Mechanism Driven Tail

Abstract This paper presents the design, modeling, and control of a three-joint robotic fish propelled by a double-slider-crank (DSC) driven caudal fin. DSC is a mechanism that can use one direct current motor to achieve oscillating foil propulsion. Its design is guided by a traveling wave equation that mimics a fish's undulatory locomotion. After multiple tests, the robotic fish displayed good performance in mimicking a real fish's swimming motion. DSC mechanism is proven to be an effective propulsion technique for a robotic fish. With the help of another servomotor at the first joint of the fish's tail, the robotic fish can have a two-dimensional free-swimming capability. In experiments, the robotic fish can achieve a swimming speed of 0.35 m/s at 3 Hz, equivalent to 0.98 body length (BL) per second. Its steering rate is proportional to a bias angle. The DSC benefits the control of the robotic fish by independently adjusting its steering and swimming speed. This characteristic is studied in a hydrodynamic model that derives the thrust within a DSC frame. Besides the dynamic model, a semiphysics-based and data-driven linear model is established to connect bias angle to robotic fish's heading angle. The linear model is used for designing an observed-state feedback control control, and the controller has been examined in simulation and experiments.

Bio-inspired algorithms for industrial robot control using deep learning methods

The biologically inspired algorithm is a significant embranchment of sequence on computational intelligence and plays a critical role in industrial robot control. Industrial robots are well suited for conducting manipulation or material handling functions in extremely constrained and predictable environments. By comparison, living organisms are highly resilient and able to perform tasks in evolving environments, such as stable locomotion over uneven terrain. The robotics challenge is to use inspiration from biology to build devices capable of functioning in unrestrained or moderately constrained environments. Hence, in this study, a Bio-inspired Intelligent Industrial Robot Control System (BIIRCS) has been suggested using Deep Learning methods. A bio-inspired neural network is considered to model the complex environment and to guide a team of robots for the coverage task. The collected data is fed into a Deep Learning Neural Network to comprehend the localization and recognition of present objects from various classes. With the derived data, appropriate robot actions can be planned and executed. Usable objects are identified and seized in the robot's workspace, or that feed is sufficient for unattainable objects. This study confirms the ability to create intelligent systems using existing Deep Learning algorithms and industrial robotics. The simulations' findings reveal that the new approach achieves a high-performance ratio of 83.5%, accuracy ratio of 95.4%, less operational time of 7.8%, low RMSE rate of 11.2%, and increased coverage task rate of 96.7% when compared to other existing approaches.

Design and Control of a Two-Motor-Actuated Tuna-Inspired Robot System

This article presents the mechanical design and locomotion control of a novel tuna-inspired robot system for both fast swimming and high maneuverability. Mechanically, the developed robotic fish named CasiTuna comprises three important parts, i.e., an innovative two-motor-actuated propulsive mechanism, a buoyancy adjustment structure, and a pair of pectoral fins. Unlike most robotic fishes' multiple concatenated links-based propulsive mechanism, CasiTuna's two-motor-actuated one places both motors in the anterior body and utilizes a transmission system to achieve tuna-like lateral undulations. Meanwhile, the buoyancy adjustment mechanism in conjunction with pectoral fins endows the robot with the capability of three-dimensional maneuverability. Kinematic and dynamic analyses are further conducted to reveal the interactive hydrodynamic forces. Regarding the locomotion control method, a bio-inspired central pattern generator-based controller is adopted to achieve multimodal swimming. In particular, two kinds of turning maneuvers are implemented and discussed. Aquatic experiments, including straight swimming, circular turning, and nearly static pitching validate the effectiveness of proposed mechatronic design and locomotion control methods. Remarkably, CasiTuna achieved a peak forward speed of 0.8 m/s (corresponding to 1.52 body lengths per second) and a minimum turning radius of less than 0.3 body lengths.

Reprint of: Automated stem cell production by bio-inspired control

The potential in treating chronic and life-threatening diseases by stem cell therapies can greatly be exploited via the efficient automation of stem cell production. Working with living material though poses severe challenges to automation. Recently, production platforms has been developed and tested worldwide with the aim to increase the reproducibility, quality and throughput of the process, to minimize human errors, and to reduce costs of production. A distinctive feature of this domain is the symbiotic co-existence and co-evolution of the technical, information and communication, as well as biological ingredients in production structures. A challenging way to overcome the issues of automated production is the use of biologically inspired control algorithms. In the paper an approach is described which combines digital, agent-based simulation and reinforcement learning for this purpose. The modelling of the cell growth behaviour, which is an important prerequisite of the simulation, is also introduced, together with an appropriate model fitting procedure. The applicability of the proposed approach is demonstrated by the results of a comprehensive investigation.

A COMPUTATIONAL FRAMEWORK OF GOAL DIRECTED VOLUNTARY MOTION GENERATION AND CONTROL LOOP IN HUMANOID ROBOTS

In this paper, it is aimed to construct a computational framework related to bio-inspired motion generation and control systems for humanoid robots. To acquire natural motion patterns in humanoid robots, behaviors observed from biological motor systems in humans and other mammals should be analyzed in detail. Computational mechanisms are mainly placed on the bio-physical plausible neural structures embodied in different dynamics. The main components of the system are composed of the limbic system, neocortex, cerebellum, brainstem, and spinal cord modules. Internal dynamics of these modules include a nonlinear estimator (e.g. chaotic attractor), memory formation, learning (neural plasticity) procedure. While the proposed novel neuro-cognitive framework is performing goal-directed voluntary motion generation and control tasks, also it estimates the amount of motion errors and computes motion correction signals. By this study, some motion-based central nervous system lesions (e.g. epilepsy, Parkinson, etc.) can be computationally modeled so that impairments of motor control commands are detected. Thus motion disorders can be reconstructed not only in humanoid robots but also in humans via some locomotion equipment.

Humanoid adaptive locomotion control through a bioinspired CPG-based controller

AbstractTo achieve adaptive gait planning of humanoid robots, a hierarchical central pattern generator (H-CPG) model with a basic rhythmic signal generation layer and a pattern formation layer is proposed to modulate the center of mass (CoM) and the online foot trajectory. The entrainment property of the CPG is exploited for adaptive walking in the absence of a priori knowledge of walking conditions, and the sensory feedback is applied to modulate the generated trajectories online to improve walking adaptability and stability. The developed control strategy is verified using a humanoid robot on sloped terrain and shows good performance.