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- All Subjects: robotics
Description
Visual navigation is a useful and important task for a variety of applications. As the prevalence of robots increase, there is an increasing need for energy-efficient navigation methods as well. Many aspects of efficient visual navigation algorithms have been implemented in the literature, but there is a lack of work on evaluation of the efficiency of the image sensors. In this thesis, two methods are evaluated: adaptive image sensor quantization for traditional camera pipelines as well as new event-based sensors for low-power computer vision.The first contribution in this thesis is an evaluation of performing varying levels of sensor linear and logarithmic quantization with the task of visual simultaneous localization and mapping (SLAM). This unconventional method can provide efficiency benefits with a trade off between accuracy of the task and energy-efficiency. A new sensor quantization method, gradient-based quantization, is introduced to improve the accuracy of the task. This method only lowers the bit level of parts of the image that are less likely to be important in the SLAM algorithm since lower bit levels signify better energy-efficiency, but worse task accuracy. The third contribution is an evaluation of the efficiency and accuracy of event-based camera intensity representations for the task of optical flow. The results of performing a learning based optical flow are provided for each of five different reconstruction methods along with ablation studies. Lastly, the challenges of an event feature-based SLAM system are presented with results demonstrating the necessity for high quality and high resolution event data. The work in this thesis provides studies useful for examining tradeoffs for an efficient visual navigation system with traditional and event vision sensors. The results of this thesis also provide multiple directions for future work.
ContributorsChristie, Olivia Catherine (Author) / Jayasuriya, Suren (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
Mechanical impedance is a concept that is used to model biomechanical propertiesof human joints. These models can then be utilized to provide insight into the inner
workings of the human neuromuscular system or to provide insight into how to best
design controllers for robotic applications that either attempt to mimic capabilities of
the human neuromuscular system or physically interact with it. To further elucidate
patterns and properties of how the human neuromuscular system modulates mechanical
impedance at the human ankle joint, multiple studies were conducted. The first
study was to assess the ability of linear regression models to characterize the change
in stiffness - a component of mechanical impedance - seen at the human ankle during
the stance phase of walking in the Dorsiflexion-Plantarflexion (DP) direction. A
collection of biomechanical variables were used as input variables. The R^2 value of
the best performing model was 0.71. The second and third studies were performed to
showcase the ability of a newly developed twin dual-axis platform, which goes beyond
the limits of a single dual-axis platform, to quantify bilateral stiffness properties. The
second study quantified the bilateral mechanical stiffness of the human ankle joint
for healthy able-bodied subjects during the stance phase of walking and during quiet
standing in both the DP and inversion-eversion directions. Subjects showed a high
level of subject specific symmetry. Lastly, a similar bilateral ankle characterization
study was conducted on a set of subjects with multiple sclerosis, but only during
quiet standing and in the DP direction. Results showed a high level of discrepancy
between the subject’s most-affected and least-affected limbs with a larger range and
variance than in the healthy population.
ContributorsRussell, Joshua (Author) / Lee, Hyunglae (Thesis advisor) / Honeycutt, Claire (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2022
Description
Simultaneous localization and mapping (SLAM) has traditionally relied on low-level geometric or optical features. However, these features-based SLAM methods often struggle with feature-less or repetitive scenes. Additionally, low-level features may not provide sufficient information for robot navigation and manipulation, leaving robots without a complete understanding of the 3D spatial world. Advanced information is necessary to address these limitations. Fortunately, recent developments in learning-based 3D reconstruction allow robots to not only detect semantic meanings, but also recognize the 3D structure of objects from a few images. By combining this 3D structural information, SLAM can be improved from a low-level approach to a structure-aware approach. This work propose a novel approach for multi-view 3D reconstruction using recurrent transformer. This approach allows robots to accumulate information from multiple views and encode them into a compact latent space. The resulting latent representations are then decoded to produce 3D structural landmarks, which can be used to improve robot localization and mapping.
ContributorsHuang, Chi-Yao (Author) / Yang, Yezhou (Thesis advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
According to the Center for Disease Control and Prevention report around 29,668 United States residents aged greater than 65 years had died as a result of a fall in 2016. Other injuries like wrist fractures, hip fractures, and head injuries occur as a result of a fall. Certain groups of people are more prone to experience falls than others, one of which being individuals with stroke. The two most common issues with individuals with strokes are ankle weakness and foot drop, both of which contribute to falls. To mitigate this issue, the most popular clinical remedy given to these users is thermoplastic Ankle Foot Orthosis. These AFO's help improving gait velocity, stride length, and cadence. However, studies have shown that a continuous restraint on the ankle harms the compensatory stepping response and forward propulsion. It has been shown in previous studies that compensatory stepping and forward propulsion are crucial for the user's ability to recover from postural perturbations. Hence, there is a need for active devices that can supply a plantarflexion during the push-off and dorsiflexion during the swing phase of gait. Although advancements in the orthotic research have shown major improvements in supporting the ankle joint for rehabilitation, there is a lack of available active devices that can help impaired users in daily activities. In this study, our primary focus is to build an unobtrusive, cost-effective, and easy to wear active device for gait rehabilitation and fall prevention in individuals who are at risk. The device will be using a double-acting cylinder that can be easily incorporated into the user's footwear using a novel custom-designed powered ankle brace. The device will use Inertial Measurement Units to measure kinematic parameters of the lower body and a custom control algorithm to actuate the device based on the measurements. The study can be used to advance the field of gait assistance, rehabilitation, and potentially fall prevention of individuals with lower-limb impairments through the use of Active Ankle Foot Orthosis.
ContributorsRay, Sambarta (Author) / Honeycutt, Claire (Thesis advisor) / Dasarathy, Gautam (Thesis advisor) / Redkar, Sangram (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020
Description
This research seeks to present the design and testing of exoskeletons capable of assisting with walking gait, squatting, and fall prevention activities. The dissertation introduces wearable
robotics and exoskeletons and then progresses into specific applications and developments in the
targeted field. Following the introduction, chapters present and discuss different wearable
exoskeletons built to address known issues with workers and individuals with increased risk of fall.
The presentation is concluded by an overall analysis of the resulting developments and identifying
future work in the field.
ContributorsOlson, Jason Stewart (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Honeycutt, Claire (Committee member) / Arizona State University (Publisher)
Created2021