Reconfigurable multi-legs robot for pipe inspection: Design and gait movement

1132-1144This paper focuses on studies on reconfigurable multi-legs robotic system. The aim of this paper is to identify and acquire findings on how multi-legs robot can walk, climb vertical pipe and walk along the horizontal pipe after climbing. Three degrees of freedom (3DOF) multi-legs robot is designed and built to replace human involvement either at hazardous pipeline or to check on vertical and horizontal pipes. The robot system is tested to climb the vertical pipe and then move along horizontal pipe for inspection or other purposes. This can reduce the cost and percentage of human risk exposure during inspection on outer pipe. This multi-legs robot has more movement gaits compared to wheeled robot, but in terms of speed, wheeled robot possesses greater advantages. Therefore, this system design has combination of both wheel and multiple legs ensure that the to system has higher stability, more gait movement, and higher speed manoeuvrability. The gaits analysis for the system movement includes angle of the legs to move and selection of certain legs to perform a given operation, either walking, climbing or hanging. The target result is the system able to climb 500 mm height with 85 mm radius pipe. The potential applications for the system are: (i) to move along either on surface or underwater pipe and (ii) to be equipped with ultrasonic sensor to inspect the pipe.</em

Unified Behavior Framework in an Embedded Robot Controller

Robots of varying autonomy have been used to take the place of humans in dangerous tasks. While robots are considered more expendable than human beings, they are complex to develop and expensive to replace if lost. Recent technological advances produce small, inexpensive hardware platforms that are powerful enough to match robots from just a few years ago. There are many types of autonomous control architecture that can be used to control these hardware platforms. One in particular, the Unified Behavior Framework, is a flexible, responsive control architecture that is designed to simplify the control system’s design process through behavior module reuse, and provides a means to speed software development. However, it has not been applied on embedded systems in robots. This thesis presents a development of the Unified Behavior Framework on the Mini-WHEGS™, a biologically inspired, embedded robotic platform. The Mini-WHEGS™ is a small robot that utilize wheel- legs to emulate cockroach walking patterns. Wheel-legs combine wheels and legs for high mobility without the complex control system required for legs. A color camera and a rotary encoder completes the robot, enabling the Mini-WHEGS™ to identify color objects and track its position. A hardware abstraction layer designed for the Mini-WHEGS™ in this configuration decouples the control system from the hardware and provide the interface between the software and the hardware. The result is a highly mobile embedded robot system capable of exchanging behavior modules with much larger robots while requiring little or no change to the modules

Muscular Activity and Physical Interaction Forces during Lower Limb Exoskeleton Use

Spinal cord injury (SCI) typically manifests with a loss of sensorimotor control of the lower limbs. In order to overcome som e of the disadvantages of chronic wheelchair use by such patients, robotic exoskeletons are an emerging technology that has the pot ential to transform the lives of patients. However, there are a number of points of contact between the robot and the user, which lead to interaction forces. In a recent study, we have shown that peak interaction forces are particularly prominent at the an terior aspect of the right leg. This study uses a similar experimental protocol with additional EMG (electromyography) analysis to examine whether such interaction forces are due to t he muscular activity of the participant or the movement of the exoskeleto n itself. Interestingly, we found that that peak forces preceded peak EMG activity. This study did not find a significant correlation between EMG activity and force data, which would indicate that the interaction f orces can largely be attributed to the mov ement of the exoskeleton itself. However, we also report significantly higher correlation coefficients in muscle/force pairs located at the anterior aspect of the right leg. In our previous research, we have shown peak interaction forces at the same locati ons, which suggests that muscular activity of the participant makes a more significant contribution to the interaction forces at these locations. The findings of this study are of significance for incomplete SCI patients, for whom EMG activity may provide an important input to an intuitive control schema

Controlling a robotic hip exoskeleton with noncontact capacitive sensors

For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separate from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this study a robotic hip orthosis was controlled using capacitive sensors placed in orthopedic cuffs on the shanks. The capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The capacitive sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the capacitive sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean ± SEM, -3.2% ± 1.1)

Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research

Training Physics-based Controllers for Articulated Characters with Deep Reinforcement Learning

In this thesis, two different applications are discussed for using machine learning techniques to train coordinated motion controllers in arbitrary characters in absence of motion capture data. The methods highlight the resourcefulness of physical simulations to generate synthetic and generic motion data that can be used to learn various targeted skills. First, we present an unsupervised method for learning loco-motion skills in virtual characters from a low dimensional latent space which captures the coordination between multiple joints. We use a technique called motor babble, wherein a character interacts with its environment by actuating its joints through uncoordinated, low-level (motor) excitation, resulting in a corpus of motion data from which a manifold latent space can be extracted. Using reinforcement learning, we then train the character to learn locomotion (such as walking or running) in the low-dimensional latent space instead of the full-dimensional joint action space. The thesis also presents an end-to-end automated framework for training physics-based characters to rhythmically dance to user-input songs. A generative adversarial network (GAN) architecture is proposed that learns to generate physically stable dance moves through repeated interactions with the environment. These moves are then used to construct a dance network that can be used for choreography. Using DRL, the character is then trained to perform these moves, without losing balance and rhythm, in the presence of physical forces such as gravity and friction

A passive upper-limb exoskeleton for industrial application based on pneumatic artificial muscles

In recent years, exoskeletons are increasingly spreading into the industrial manufacturing sector to improve productivity and to reduce the incidence of work-related musculoskeletal diseases. The aim of this paper is to present a 2 degrees of freedom (DoF) passive upper-limb exoskeleton, consisting of two McKibben pneumatic artificial muscles (PAMs), and used for assisting workers during activities that require them to keep their hands in a sustained position over the head for a long time. Simulations are performed to test two different commercial PAMs and two different designs of the transmission system used to convey the traction force exerted by the pneumatic muscles to the limb; then the results are discussed. A preliminary assembly of the exoskeleton is also presented. The study confirms that PAMs can be used to realize a passive upper-limb exoskeleton for industrial application and that appropriate working space can be obtained with an accurate design of the transmission system

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness