Kinematics analysis of a FLHL robot parallel-executed cylinder mechanical integration system with force/position hybrid control servo actuator

In this research subtopic, an electro-hydraulic servo four-legged heavy load (FLHL) robot has been designed and developed. This paper proposes an integration layout cylinder design scheme for a non-lightweight hydraulic servo four-legged robot with high loads and torques of hip joint, and derives the mathematical element analysis model for a parallel hydraulic servo cylinder system. The multiple inherent characteristics of the parallel-executed cylinder integration system model are further explored. Based on the controllable functional requirements of interconnected joints and weakening the influence of internal force coupling, a design idea of force/position hybrid control scheme for the parallel-executed cylinder is determined, and then the force/position signal module design unit is used to reversely solve the force/position hybrid control. Considering the inherent requirements of the servo-executed cylinder force control unit module, the implementation process of magnetic flux compensation and speed compensation is discussed in detail. The minimum amplitude controller is applied to the servo-executed cylinder force unit module, and the proportional integrated controller has been determined in the servo-executed cylinder position control unit module. A compound control strategy proposed in this paper is verified on a parallel hydraulic servo platform. The experimental verification results reveal that the values of position/force attenuation amplitude and lag phase are not greater than 9 % and 18°, respectively. In addition, the feasibility of the interconnected implementation of the hybrid control scheme proposed in this paper is further deepened. The effective conclusion of this research will be accepted in the application field of FLHL robot control system

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

MODELLING AND EXPERIMENTAL VERIFICATION OF LIZARDS' FLEXIBLE TRUNKS' EFFECTS ON ENERGETICALLY EFFICIENT LOCOMOTION

Master'sMASTER OF ENGINEERIN

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PD

Hopping, Landing, and Balancing with Springs

This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one

System Identification of Bipedal Locomotion in Robots and Humans

The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints

Adaptive Locomotion: The Cylindabot Robot

Adaptive locomotion is an emerging field of robotics due to the complex interaction between the robot and its environment. Hybrid locomotion is where a robot has more than one mode of locomotion and potentially delivers the benefits of both, however, these advantages are often not quantified or applied to new scenarios. The classic approach is to design robots with a high number of degrees of freedom and a complex control system, whereas an intelligent morphology can simplify the problem and maintain capabilities. Cylindabot is designed to be a minimally actuated hybrid robot with strong terrain crossing capabilities. By limiting the number of motors, this reduces the robot's weight and means less reinforcement is needed for the physical frame or drive system. Cylindabot uses different drive directions to transform between using wheels or legs. Cylindabot is able to climb a slope of 32 degrees and a step ratio of 1.43 while only being driven by two motors. A physical prototype and simulation models show that adaptation is optimal for a range of terrain (slopes, steps, ridges and gaps). Cylindabot successfully adapts to a map environment where there are several routes to the target location. These results show that a hybrid robot can increase its terrain capabilities when changing how it moves and that this adaptation can be applied to wider environments. This is an important step to have hybrid robots being deployed to real situations

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller