Matching Items (32)
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- All Subjects: robotics
- Creators: Artemiadis, Panagiotis
Description
Electromyogram (EMG)-based control interfaces are increasingly used in robot teleoperation, prosthetic devices control and also in controlling robotic exoskeletons. Over the last two decades researchers have come up with a plethora of decoding functions to map myoelectric signals to robot motions. However, this requires a lot of training and validation data sets, while the parameters of the decoding function are specific for each subject. In this thesis we propose a new methodology that doesn't require training and is not user-specific. The main idea is to supplement the decoding functional error with the human ability to learn inverse model of an arbitrary mapping function. We have shown that the subjects gradually learned the control strategy and their learning rates improved. We also worked on identifying an optimized control scheme that would be even more effective and easy to learn for the subjects. Optimization was done by taking into account that muscles act in synergies while performing a motion task. The low-dimensional representation of the neural activity was used to control a two-dimensional task. Results showed that in the case of reduced dimensionality mapping, the subjects were able to learn to control the device in a slower pace, however they were able to reach and retain the same level of controllability. To summarize, we were able to build an EMG-based controller for robot devices that would work for any subject, without any training or decoding function, suggesting human-embedded controllers for robotic devices.
ContributorsAntuvan, Chris Wilson (Author) / Artemiadis, Panagiotis (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
As robotic systems are used in increasingly diverse applications, the interaction of humans and robots has become an important area of research. In many of the applications of physical human robot interaction (pHRI), the robot and the human can be seen as cooperating to complete a task with some object of interest. Often these applications are in unstructured environments where many paths can accomplish the goal. This creates a need for the ability to communicate a preferred direction of motion between both participants in order to move in coordinated way. This communication method should be bidirectional to be able to fully utilize both the robot and human capabilities. Moreover, often in cooperative tasks between two humans, one human will operate as the leader of the task and the other as the follower. These roles may switch during the task as needed. The need for communication extends into this area of leader-follower switching. Furthermore, not only is there a need to communicate the desire to switch roles but also to control this switching process. Impedance control has been used as a way of dealing with some of the complexities of pHRI. For this investigation, it was examined if impedance control can be utilized as a way of communicating a preferred direction between humans and robots. The first set of experiments tested to see if a human could detect a preferred direction of a robot by grasping and moving an object coupled to the robot. The second set tested the reverse case if the robot could detect the preferred direction of the human. The ability to detect the preferred direction was shown to be up to 99% effective. Using these results, a control method to allow a human and robot to switch leader and follower roles during a cooperative task was implemented and tested. This method proved successful 84% of the time. This control method was refined using adaptive control resulting in lower interaction forces and a success rate of 95%.
ContributorsWhitsell, Bryan (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Arizona State University (Publisher)
Created2014
Description
As robots are increasingly migrating out of factories and research laboratories and into our everyday lives, they should move and act in environments designed for humans. For this reason, the need of anthropomorphic movements is of utmost importance. The objective of this thesis is to solve the inverse kinematics problem of redundant robot arms that results to anthropomorphic configurations. The swivel angle of the elbow was used as a human arm motion parameter for the robot arm to mimic. The swivel angle is defined as the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Using kinematic data recorded from human subjects during every-day life tasks, the linear sensorimotor transformation model was validated and used to estimate the swivel angle, given the desired end-effector position. Defining the desired swivel angle simplifies the kinematic redundancy of the robot arm. The proposed method was tested with an anthropomorphic redundant robot arm and the computed motion profiles were compared to the ones of the human subjects. This thesis shows that the method computes anthropomorphic configurations for the robot arm, even if the robot arm has different link lengths than the human arm and starts its motion at random configurations.
ContributorsWang, Yuting (Author) / Artemiadis, Panagiotis (Thesis advisor) / Mignolet, Marc (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
As the robotic industry becomes increasingly present in some of the more extreme environments such as the battle field, disaster sites or extraplanetary exploration, it will be necessary to provide locomotive niche strategies that are optimal to each terrain. The hopping gait has been well studied in robotics and proven to be a potential method to fit some of these niche areas. There have been some difficulties in producing terrain following controllers that maintain robust, steady state, which are disturbance resistant.
The following thesis will discuss a controller which has shown the ability to produce these desired properties. A phase angle oscillator controller is shown to work remarkably well, both in simulation and with a one degree of freedom robotic test stand.
Work was also done with an experimental quadruped with less successful results, but which did show potential for stability. Additional work is suggested for the quadruped.
The following thesis will discuss a controller which has shown the ability to produce these desired properties. A phase angle oscillator controller is shown to work remarkably well, both in simulation and with a one degree of freedom robotic test stand.
Work was also done with an experimental quadruped with less successful results, but which did show potential for stability. Additional work is suggested for the quadruped.
ContributorsNew, Philip Wesley (Author) / Sugar, Thomas G. (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2015
Description
Wearable robots including exoskeletons, powered prosthetics, and powered orthotics must add energy to the person at an appropriate time to enhance, augment, or supplement human performance. Adding energy while not being in sync with the user can dramatically hurt performance making it necessary to have correct timing with the user. Many human tasks such as walking, running, and hopping are repeating or cyclic tasks and a robot can add energy in sync with the repeating pattern for assistance. A method has been developed to add energy at the appropriate time to the repeating limit cycle based on a phase oscillator. The phase oscillator eliminates time from the forcing function which is based purely on the motion of the user. This approach has been simulated, implemented and tested in a robotic backpack which facilitates carrying heavy loads. The device oscillates the load of the backpack, based on the motion of the user, in order to add energy at the correct time and thus reduce the amount of energy required for walking with a heavy load. Models were developed in Working Model 2-D, a dynamics simulation software, in conjunction with MATLAB to verify theory and test control methods. The control system developed is robust and has successfully operated on a range of different users, each with their own different and distinct gait. The results of experimental testing validated the corresponding models.
ContributorsWheeler, Chase (Author) / Sugar, Thomas G. (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2014
Description
This work presents the integration of user intent detection and control in the development of the fluid-driven, wearable, and continuum, Soft Poly-Limb (SPL). The SPL utilizes the numerous traits of soft robotics to enable a novel approach to provide safe and compliant mobile manipulation assistance to healthy and impaired users. This wearable system equips the user with an additional limb made of soft materials that can be controlled to produce complex three-dimensional motion in space, like its biological counterparts with hydrostatic muscles. Similar to the elephant trunk, the SPL is able to manipulate objects using various end effectors, such as suction adhesion or a soft grasper, and can also wrap its entire length around objects for manipulation. User control of the limb is demonstrated using multiple user intent detection modalities. Further, the performance of the SPL studied by testing its capability to interact safely and closely around a user through a spatial mobility test. Finally, the limb’s ability to assist the user is explored through multitasking scenarios and pick and place tests with varying mounting locations of the arm around the user’s body. The results of these assessments demonstrate the SPL’s ability to safely interact with the user while exhibiting promising performance in assisting the user with a wide variety of tasks, in both work and general living scenarios.
ContributorsVale, Nicholas Marshall (Author) / Polygerinos, Panagiotis (Thesis advisor) / Zhang, Wenlong (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
Human locomotion is an essential function that enables individuals to lead healthy, independent lives. One important feature of natural walking is the capacity to transition across varying surfaces, enabling an individual to traverse complex terrains while maintaining balance. There has been extensive work regarding improving prostheses' performance in changing walking conditions, but there is still a need to address the transition from rigid to compliant or dynamic surfaces, such as the transition from pavement to long grass or soft sand. This research aims to investigate the mechanisms involved such transitions and identify potential indicators of the anticipated change that can be applied to the control of a powered ankle prosthetic to reduce falls and improve stability in lower-limb amputees in a wider range of walking environments. A series of human subject experiments were conducted using the Variable Stiffness Treadmill (VST) to control walking surface compliance while gait kinematics and muscular activation data were collected from three healthy, nondisabled subjects. Specifically, the kinematics and electromyography (EMG) profiles of the gait cycles immediately preceding and following an expected change in surface compliance were compared to that of normal, rigid surface walking. While the results do not indicate statistical differences in the EMG profiles between the two modes of walking, the muscle activation appears to be qualitatively different from inspection of the data. Additionally, there were promising statistically significant changes in joint angles, especially in observed increases in hip flexion during the swing phases both before and during an expected change in surface. Decreases in ankle flexion immediately before heel strike on the perturbed leg were also observed to occur simultaneously with decreases in tibialis anterior (TA) muscle activation, which encourages additional research investigating potential changes in EMG profiles. Ultimately, more work should be done to make strong conclusions about potential indicators of walking surface transitions, but this research demonstrates the potential of EMG and kinematic data to be used in the control of a powered ankle prosthetic.
ContributorsFou, Linda (Author) / Artemiadis, Panagiotis (Thesis advisor) / Lee, Hyunglae (Committee member) / Polygerinos, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
This work considers the design of separating input signals in order to discriminate among a finite number of uncertain nonlinear models. Each nonlinear model corresponds to a system operating mode, unobserved intents of other drivers or robots, or to fault types or attack strategies, etc., and the separating inputs are designed such that the output trajectories of all the nonlinear models are guaranteed to be distinguishable from each other under any realization of uncertainties in the initial condition, model discrepancies or noise. I propose a two-step approach. First, using an optimization-based approach, we over-approximate nonlinear dynamics by uncertain affine models, as abstractions that preserve all its system behaviors such that any discrimination guarantees for the affine abstraction also hold for the original nonlinear system. Then, I propose a novel solution in the form of a mixed-integer linear program (MILP) to the active model discrimination problem for uncertain affine models, which includes the affine abstraction and thus, the nonlinear models. Finally, I demonstrate the effectiveness of our approach for identifying the intention of other vehicles in a highway lane changing scenario. For the abstraction, I explore two approaches. In the first approach, I construct the bounding planes using a Mixed-Integer Nonlinear Problem (MINLP) formulation of the given system with appropriately designed constraints. For the second approach, I solve a linear programming (LP) problem that over-approximates the nonlinear function at only the grid points of a mesh with a given resolution and then accounting for the entire domain via an appropriate correction term. To achieve a desired approximation accuracy, we also iteratively subdivide the domain into subregions. This method applies to nonlinear functions with different degrees of smoothness, including Lipschitz continuous functions, and improves on existing approaches by enabling the use of tighter bounds. Finally, we compare the effectiveness of this approach with the existing optimization-based methods in simulation and illustrate its applicability for estimator design.
ContributorsSingh, Kanishka Raj (Author) / Yong, Sze Zheng (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2018
Description
For a conventional quadcopter system with 4 planar rotors, flight times vary between 10 to 20 minutes depending on the weight of the quadcopter and the size of the battery used. In order to increase the flight time, either the weight of the quadcopter should be reduced or the battery size should be increased. Another way is to increase the efficiency of the propellers. Previous research shows that ducting a propeller can cause an increase of up to 94 % in the thrust produced by the rotor-duct system. This research focused on developing and testing a quadcopter having a centrally ducted rotor which produces 60 % of the total system thrust and 3 other peripheral rotors. This quadcopter will provide longer flight times while having the same maneuvering flexibility in planar movements.
ContributorsLal, Harsh (Author) / Artemiadis, Panagiotis (Thesis advisor) / Lee, Hyunglae (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019