Kin Huat Low

Design and Locomotion Control of a Biomimetic Underwater Vehicle With Fin Propulsion

As a novel biologically inspired underwater vehicle, a robotic manta ray (RoMan-II) has been developed for potential marine applications. Manta ray can perform diversified locomotion patterns in water by manipulating two wide tins. These motion patterns have been implemented on the developed fish robot, including swimming by flapping fins, turning by modulating phase relations of fins, and online transition of different motion patterns. The movements are achieved by using a model of artificial central pattern generators (CPGs) constructed with coupled nonlinear oscillators. This paper focuses on the analytical formulation of coupling terms in the CPG model and the implementation issues of the CPG-based control on the fish robot. The control method demonstrated on the manta ray robot is expected to be a frame- work that can tackle locomotion control problems in other types of multifin-actuated fish robots or more general robots with rhythmic movement patterns.

Parametric study of the swimming performance of a fish robot propelled by a flexible caudal fin

In this paper, we aim to study the swimming performance of fish robots by using a statistical approach. A fish robot employing a carangiform swimming mode had been used as an experimental platform for the performance study. The experiments conducted aim to investigate the effect of various design parameters on the thrust capability of the fish robot with a flexible caudal fin. The controllable parameters associated with the fin include frequency, amplitude of oscillation, aspect ratio and the rigidity of the caudal fin. The significance of these parameters was determined in the first set of experiments by using a statistical approach. A more detailed parametric experimental study was then conducted with only those significant parameters. As a result, the parametric study could be completed with a reduced number of experiments and time spent. With the obtained experimental result, we were able to understand the relationship between various parameters and a possible adjustment of parameters to obtain a higher thrust. The proposed statistical method for experimentation provides an objective and thorough analysis of the effects of individual or combinations of parameters on the swimming performance. Such an efficient experimental design helps to optimize the process and determine factors that influence variability.

Effective Phase Tracking for Bioinspired Undulations of Robotic Fish Models: A Learning Control Approach

Robotic models have been used as one of the approaches to study fish locomotion. Therefore, this paper proposes an effective control scheme that enables robotic models to mimic fin-ray undulation kinematics of live fish. We found in the experiments of robotic fin undulation that the difference between the desired and actual trajectories can be significant. It is believed that the difference might be caused by the phase lagging effect. To tackle the phase tracking problem, a modified iterative learning control (ILC) scheme is proposed and implemented on the robotic fish model. Furthermore, a memory clearing operator is proposed to satisfy the Lipschitz condition. This is necessary for the convergence and feasibility of the ILC scheme. Finally, experimental results illustrate the effectiveness of the proposed learning control approach, including the memory clearing operator.

Gait study and pattern generation of a starfish-like soft robot with flexible rays actuated by SMAs

This paper presents the design and development of a starfish-like soft robot with flexible rays and the implementation of multi-gait locomotion using Shape Memory Alloy (SMA) actuators. The design principle was inspired by the starfish, which possesses a remarkable symmetrical structure and soft internal skeleton. A soft robot body was constructed by using 3D printing technology. A kinematic model of the SMA spring was built and developed for motion control according to displacement and force requirements. The locomotion inspired from starfish was applied to the implementation of the multi-ray robot through the flexible actuation induced multi-gait movements in various environments. By virtue of the proposed ray control patterns in gait transition, the soft robot was able to cross over an obstacle approximately twice of its body height. Results also showed that the speed of the soft robot was 6.5 times faster on sand than on a clammy rough terrain. These experiments demonstrated that the bionic soft robot with flexible rays actuated by SMAs and multi-gait locomotion in proposed patterns can perform successfully and smoothly in various terrains.

Rehabilitation control strategies for a gait robot via EMG evaluation

Therapists often emphasize selection and design of proper training programs for individual patients in different situations and rehabilitation stages. Thus, in order to cater different patient groups, this paper proposes four assistant control strategies for robotic rehabilitation of gait locomotion: (1) orthosis-free, (2) totally-passive, (3) optimal assistance and (4) resistance exercise. A robotic gait system with pelvic control (PC) is also designed by integrating body weight support (BWS), robotic orthosis (RO), parallelogram arm (PA), and mobile platform (MP). This paper also investigates the electromyography (EMG) signals from the eight major muscles of the leg and compares them to those created by our robotic device. The initial results of clinical trials indicate the potential for robotic rehabilitation in patients with gait impairments.

Survey and Introduction to the Focused Section on Bio-Inspired Mechatronics

Understanding and adapting the underlying principles of biological systems to engineering systems have the promise of enabling many new mechatronic systems that can operate in unstructured and uncertain environments robustly and efficiently. This paper first reports a brief survey of recent studies on bio-inspired mechatronic systems and their biological counterparts in respects of locomotion, actuation, sensing, and control. Next, brief highlights of the 20 papers in this “Focused Section on Bio-Inspired Mechatronics” are given. Finally, current challenges and future trends of bio-inspired mechatronic systems are described.

Design and Implementation of a Lightweight Bioinspired Pectoral Fin Driven by SMA

Pectoral fins play an important role in the fish swimming performance, especially in maneuverability underwater. This paper presents the swimming propulsion by means of a flexible and lightweight pectoral fin inspired by a Koi Carp. The fin is driven by embedded shape memory alloy (SMA) wires. In this paper, the kinematics of a pectoral fin from a live Koi Carp fish is first studied. The motion of fin rays is analyzed, in which four basic patterns are extracted from the motion of the pectoral fin captured experimentally, especially the motion in retreating and hovering. Inspired by the fin motion of the live fish, an SMA-driven fin ray providing a two-degree-of-freedom bending motion is proposed. The detailed design of the bioinspired pectoral fin driven by SMA-driven rays is then presented. The basic unit is an SMA-driven plate with two SMA wires embedded on the two opposite sides of a plastic plate. The SMA-driven plate can bend by a pulse width modulation current delivered through SMA wires. An assembled SMA fin ray is next formed by two SMA plates, which are placed in series with their cross sections perpendicular to each other. As a result, a lightweight bioinspired pectoral fin is constructed by placing radially multiple SMA fin rays. The integrated pectoral fin is able to exhibit four patterns extracted in the biological kinematic study. The simulation and experimental optimization on the SMA-driven plate are presented in the final part of this paper. The diameter of SMA wires is optimized and the oscillation angle of SMA plate is obtained. The experiment is also conducted to evaluate the motions of the bioinspired pectoral fin. The result demonstrates that the SMA-based fin is effective in driving the bioinspired fin. Moreover, the bioinspired pectoral fin is able to perform complex motions that can contribute to the maneuverability of fish robots.

Hardware Development and Locomotion Control Strategy for an Over-Ground Gait Trainer: NaTUre-Gaits

Therapist-assisted body weight supported (TABWS) gait rehabilitation was introduced two decades ago. The benefit of TABWS in functional recovery of walking in spinal cord injury and stroke patients has been demonstrated and reported. However, shortage of therapists, labor-intensiveness, and short duration of training are some limitations of this approach. To overcome these deficiencies, robotic-assisted gait rehabilitation systems have been suggested. These systems have gained attentions from researchers and clinical practitioner in recent years. To achieve the same objective, an over-ground gait rehabilitation system, NaTUre-gaits, was developed at the Nanyang Technological University. The design was based on a clinical approach to provide four main features, which are pelvic motion, body weight support, over-ground walking experience, and lower limb assistance. These features can be achieved by three main modules of NaTUre-gaits: 1) pelvic assistance mechanism, mobile platform, and robotic orthosis. Predefined gait patterns are required for a robotic assisted system to follow. In this paper, the gait pattern planning for NaTUre-gaits was accomplished by an individual-specific gait pattern prediction model. The model generates gait patterns that resemble natural gait patterns of the targeted subjects. The features of NaTUre-gaits have been demonstrated by walking trials with several subjects. The trials have been evaluated by therapists and doctors. The results show that 10-m walking trial with a reduction in manpower. The task-specific repetitive training approach and natural walking gait patterns were also successfully achieved.

A subject-based motion generation model with adjustable walking pattern for a gait robotic trainer: NaTUre-gaits

A gait trainer, NaTUre-gaits (natural and tunable rehabilitation gait system), has been developed to provide assistance for the gait rehabilitation. The gait rehabilitation system can provide mobility for an overground locomotion in forward walking and turning by a mobile platform. The exoskeleton modules are mounted on the mobile platform and attached to the lower limbs and pelvis in parallel. The synchronized motion generation for the exoskeleton modules is provided by virtue of the inverse kinematic model analysis. The pelvic trajectory is predefined and ten points are specified within one gait cycle to obtain the foot trajectory from the designated step length and height. The trajectory is obtained via curve fitting based on these specified points. On the other hand, in order to keep the desired foot trajectory, the pelvic motion is compensated to accommodate hip and knee joint angles. The proposed generation method is tested on the gait system and it has demonstrated the effectiveness and smoothness of the system.

An individual-specific gait pattern prediction model based on generalized regression neural networks

Robotics is gaining its popularity in gait rehabilitation. Gait pattern planning is important to ensure that the gait patterns induced by robotic systems are tailored to each individual and varying walking speed. Most research groups planned gait patterns for their robotics systems based on Clinical Gait Analysis (CGA) data. The major problem with the method using the CGA data is that it cannot accommodate inter-subject differences. In addition, CGA data is limited to only one walking speed as per the published data. The objective of this work was to develop an individual-specific gait pattern prediction model for gait pattern planning in the robotic gait rehabilitation systems. The waveforms of lower limb joint angles in the sagittal plane during walking were obtained with a motion capture system. Each waveform was represented and reconstructed by a Fourier coefficient vector which consisted of eleven elements. Generalized regression neural networks (GRNNs) were designed to predict Fourier coefficient vectors from given gait parameters and lower limb anthropometric data. The generated waveforms from the predicted Fourier coefficient vectors were compared to the actual waveforms and CGA waveforms by using the assessment parameters of correlation coefficients, mean absolute deviation (MAD) and threshold absolute deviation (TAD). The results showed that lower limb joint angle waveforms generated by the gait pattern prediction model were closer to the actual waveforms compared to the CGA waveforms.