Switching adaptive control of a bioassistive exoskeleton

The effectiveness of existing control designs for bioassistive, exoskeletal devices, especially in highly uncertain working environments, depends on the degree of certainty associated with the overall system model. Of particular concern is the robustness of a control design to large-bandwidth exogenous disturbances, time delays in the sensor and actuator loops, and kinematic and inertial variability across the population of likely users. In this study, we propose an adaptive control framework for robotic exoskeletons that uses a low-pass filter structure in the feedback channel to decouple the estimation loop from the control loop. The design facilitates a significant increase in the rate of estimation and adaptation, without a corresponding loss of robustness. In particular, the control implementation is tolerant of time delays in the control loop and maintains clean control channels even in the presence of measurement noise. Tuning of the filter also allows for shaping the nominal response and enhancing the time-delay margin. Importantly, the proposed formulation is independent of detailed model information. The performance of the proposed architecture is demonstrated in simulation for two basic control scenarios, namely, (i) static positioning, for which the predefined desired joint motions are constant; and (ii) command following, where the desired motions are not known a priori and instead inferred using interaction measurements. We consider, in addition, an operating modality in which the control scheme switches between static positioning and command following to facilitate flexible integration of a human operator in the loop. Here, the transition from static positioning to command following is triggered when either the human–machine interaction force at the wrist or the end-effector velocity exceeds the corresponding critical value. The controller switches from command following back to static positioning when both the interaction force and the velocity fall below the corresponding thresholds. This strategy allows for smooth transition between two phases of operation and provides an alternative to an implementation relying on wearable electromyographic sensors

Stability and robustness of adaptive controllers for underactuated Lagrangian systems and robotic networks

This dissertation studies the stability and robustness of an adaptive control framework for underactuated Lagrangian systems and robotic networks. In particular, an adaptive control framework is designed for a manipulator, which operates on an underactuated dynamic platform. The framework promotes the use of a filter in the control input to improve the system robustness. The characteristics of the controller are represented by two decoupled indicators. First, the adaptation gain determines the rate of adaptation, as well as the deviation between the adaptive control system and a nonadaptive reference system governing the ideal response. Second, the filter bandwidth determines the tracking performance, as well as the system robustness. The ability of the control scheme to tolerate time delay in the control loop, which is an indicator of robustness, is explored using numerical simulations, estimation of the time-delay margin of an equivalent linear, time-invariant system, and parameter continuation for Hopf bifurcation analysis. This dissertation also performs theoretical study of the delay robustness of the control framework. The analysis shows that the controller has a positive lower bound for the time-delay margin by exploring a number of properties of delay systems, especially the continuity of their solutions in the delay, uniformly in time. In particular, if the input delay is below the lower bound, then the state and control input of the closed-loop system follow those of a nonadaptive, robust reference system closely. A method for computing the lower bound for the delay robustness using a Pad\'{e} approximant is proposed. The results show that the minimum delay that destabilizes the system, which may also be estimated by forward simulation, is always larger than the value computed by the proposed method. The control framework is extended to the synchronization and consensus of networked manipulators operating on an underactuated dynamic platform in the presence of communication delays. The theoretical analysis based on input-output maps of functional differential equations shows that the adaptive control system's behavior matches closely that of a nonadaptive reference system. The tracking-synchronization objective is achieved despite the effects of communication delays and unknown dynamics of the platform. When there is no desired trajectory common to the networked manipulators, a modified controller drives all robots to a consensus configuration. A further modification is proposed that allows for the control of the constant and time-varying consensus values using a leader-follower scheme. Simulation results illustrate the performance of the proposed control algorithms

SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation

Automated medical image segmentation is becoming increasingly crucial to modern clinical practice, driven by the growing demand for precise diagnosis, the push towards personalized treatment plans, and the advancements in machine learning algorithms, especially the incorporation of deep learning methods. While convolutional neural networks (CNN) have been prevalent among these methods, the remarkable potential of Transformer-based models for computer vision tasks is gaining more acknowledgment. To harness the advantages of both CNN-based and Transformer-based models, we propose a simple yet effective UNet-Transformer (seUNet-Trans) model for medical image segmentation. In our approach, the UNet model is designed as a feature extractor to generate multiple feature maps from the input images, then the maps are propagated into a bridge layer, which is introduced to sequentially connect the UNet and the Transformer. In this stage, we approach the pixel-level embedding technique without position embedding vectors, aiming to make the model more efficient. Moreover, we apply spatial-reduction attention in the Transformer to reduce the computational/memory overhead. By leveraging the UNet architecture and the self-attention mechanism, our model not only retains the preservation of both local and global context information but also is capable of capturing long-range dependencies between input elements. The proposed model is extensively experimented on seven medical image segmentation datasets including polyp segmentation to demonstrate its efficacy. Comparison with several state-of-the-art segmentation models on these datasets shows the superior performance of our proposed seUNet-Trans network

Deep-Learning Framework for Optimal Selection of Soil Sampling Sites

This work leverages the recent advancements of deep learning in image processing to find optimal locations that present the important characteristics of a field. The data for training are collected at different fields in local farms with five features: aspect, flow accumulation, slope, NDVI (normalized difference vegetation index), and yield. The soil sampling dataset is challenging because the ground truth is highly imbalanced binary images. Therefore, we approached the problem with two methods, the first approach involves utilizing a state-of-the-art model with the convolutional neural network (CNN) backbone, while the second is to innovate a deep-learning design grounded in the concepts of transformer and self-attention. Our framework is constructed with an encoder-decoder architecture with the self-attention mechanism as the backbone. In the encoder, the self-attention mechanism is the key feature extractor, which produces feature maps. In the decoder, we introduce atrous convolution networks to concatenate, fuse the extracted features, and then export the optimal locations for soil sampling. Currently, the model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%, while the performance metrics of the state-of-the-art CNN-based model are 66.08%, 3.85%, and 1.98%, respectively. This indicates that our proposed model outperforms the CNN-based method on the soil-sampling dataset. To the best of our knowledge, our work is the first to provide a soil-sampling dataset with multiple attributes and leverage deep learning techniques to enable the automatic selection of soil-sampling sites. This work lays a foundation for novel applications of data science and machine-learning technologies to solve other emerging agricultural problems.Comment: This paper is the full version of a poster presented at the AI in Agriculture Conference 2023 in Orlando, FL, US

Embedded and visual programming for SmartSuit motion capture system

This thesis presents a novel sensing technology using optical linear encoders (OLE) to capture human motion. A CAN bus architecture is proposed to connect the OLE-based sensor nodes, each of which consists of a tri-axis accelerometer and an OLE. The network of three SmartSuit sensing modules is able to capture full motion of human arm with a 7-DOF kinematic model. Firmware programs are developed and embedded into wearable sensor nodes to implement the architecture. In addition, the programming framework for motion capture and processing is introduced. Based on the framework, the motion capture software is developed with all necessary features. The software interfaces with the wearable sensor hardware via serial communication. The motion data are processed and stored in hierarchical models. The software’s graphics display unit regenerates the body motion using either OpenGL rendering method or modeling softwares. Moreover, the software can import and export standard motion data formats, facilitating our OLE-based motion capture system to communicate with various platforms. Experiments were carried out to compare the performance of the OLE sensing module with BIOPAC Goniometer. The results show that the OLE’s performance is comparable to that of those expensive systems, and also validate the sensor network architecture, firmware and the SmartSuit software. Furthermore, a statistical study is conducted to confirm the repeatability and reliability of the new OLE sensing module and the wearable sensor network. The results demonstrate that the new sensor system has strong potential to be used as a low-cost tool for motion capture, and arm function evaluation for short-term as well as long-term monitoring.MASTER OF ENGINEERING (MAE

Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach

Nozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which indicates how much of the spray adheres to the crop and how much becomes chemical runoff that pollutes the environment. There is a critical need to measure these droplet properties to improve the performance of crop spraying systems. This paper establishes a deep learning methodology to detect droplets moving across a camera frame to measure their size. This framework is compatible with embedded systems that have limited onboard resources and can operate in real time. The method leverages a combination of techniques including resizing, normalization, pruning, detection head, unified feature map extraction via a feature pyramid network, non-maximum suppression, and optimization-based training. The approach is designed with the capability of detecting droplets of various sizes, shapes, and orientations. The experimental results demonstrate that the model designed in this study, coupled with the right combination of dataset and augmentation, achieved a 97% precision and 96.8% recall in droplet detection. The proposed methodology outperformed previous models, marking a significant advancement in droplet detection for precision agriculture applications

A deep-learning framework for spray pattern segmentation and estimation in agricultural spraying systems

Abstract This work focuses on leveraging deep learning for agricultural applications, especially for spray pattern segmentation and spray cone angle estimation. These two characteristics are important to understanding the sprayer system such as nozzles used in agriculture. The core of this work includes three deep-learning convolution-based models. These models are trained and their performances are compared. After the best model is selected based on its performance, it is used for spray region segmentation and spray cone angle estimation. The output from the selected model provides a binary image representing the spray region. This binary image is further processed using image processing to estimate the spray cone angle. The validation process is designed to compare results obtained from this work with manual measurements

On Algorithms for Planning S-curve Motion Profiles

Although numerous researches on s-curve motion profiles have been carried out, up to date, no systematic investigation on the general model of polynomial s-curve motion profiles is considered. In this paper, the model of polynomial s-curve motion profiles is generalized in a recursive form. Based on that, a general algorithm to design s-curve trajectory with time-optimal consideration is proposed. In addition, a special strategy for planning s-curve motion profiles using a trigonometric model is also presented. The algorithms are implemented on a linear motor system. Experimental results show the effectiveness and promising application ability of the algorithms in s-curve motion profiling