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Abstract This paper addresses the problem of algorithmic prediction of protein folding pathways, namely, the transient three-dimensional conformations of protein molecules during folding, under constrained rates of entropy change. We formulate the physics-based prediction of folding pathways as a control synthesis problem, where the control inputs guide the protein folding simulations. These folding control inputs are obtained from largescale trust-region subproblems (TRS) utilizing a computationally efficient algorithm with no need for outer iterations. The proposed control synthesis approach, which leverages the solutions obtained from a special generalized eigenvalue problem, avoids potentially cumbersome and unpredictable iterative computations at each protein conformation. Moreover, the TRS-based control inputs align the closed-loop dynamics closely with the kinetostatic compliance method (KCM) reference vector field while satisfying ellipsoidal constraints on the folding control inputs. Finally, we provide conditions for existence and uniqueness of the resulting closed-loop solutions, which are the protein folding pathways under constraints on the rate of entropy change. Numerical simulations utilizing the KCM approach on protein backbones confirm the effectiveness of the proposed framework. 

Effective human-robot interaction is increasingly vital across various domains, including assistive robotics, emotional communication, entertainment, and industrial automation. Visual feedback, a common feature of current interfaces, may not be suitable for all environments. Audio feedback serves as a critical supplementary communication layer in settings where visibility is low or where robotic operations generate extensive data. Sonification, which transforms a robot's trajectory, motion, and environmental signals into sound, enhances users' comprehension of robot behavior. This improvement in understanding fosters more effective, safe, and reliable Human-Robot Interaction (HRI). Demonstrations of auditory data sonification's benefits are evident in real-world applications such as industrial assembly, robot-assisted rehabilitation, and interactive robotic exhibitions, where it promotes cooperation, boosts performance, and heightens engagement. Beyond conventional HRI environments, auditory data sonification shows substantial potential in managing complex robotic systems and intricate structures, such as hyper-redundant robots and robotic teams. These systems often challenge operators with complex joint monitoring, mathematical kinematic modeling, and visual behavior verification. This dissertation explores the sonification of motion in hyper-redundant robots and teams of industrial robots. It delves into the Wave Space Sonification (WSS) framework developed by Hermann, applying it to the motion datasets of protein molecules modeled as hyper-redundant mechanisms with numerous rigid nano-linkages. This research leverages the WSS framework to develop a sonification methodology for protein molecules' dihedral angle folding trajectories. Furthermore, it introduces a novel approach for the systematic sonification of robotic motion across varying configurations. By employing localized wave fields oriented within the robots' configuration space, this methodology generates auditory outputs with specific timbral qualities as robots move through predefined configurations or along certain trajectories. Additionally, the dissertation examines a team of wheeled industrial/service robots whose motion patterns are sonified using sinusoidal vibratory sounds, demonstrating the practical applications and benefits of this innovative approach. 

Sonification is a method to represent data and convey information using sound. Just like the Geiger counter, humans can use sound to better understand complex sets of data that are either unable to be seen or visualized or that are too complex to understand with visual displays. Sonification research and learning have been predominantly conducted at the higher education level. However, as STEM-related programs and activities continue to be increasingly important in secondary school education, it is possible to expose high school students to university-level research through project-based learning (PBL) activities in the classroom. Using a physical snake robot prototype that was built and programmed with low-cost materials, high school students are introduced to the field of sonification and its applications to snake robots. This dissertation demonstrates the feasibility of using project-based learning to teach university level research in secondary school education. Using the sonification of snake robot movement, students learned advanced topics in robotics with the goal of realizing that university level research is accessible and understandable through PBL. This paper will begin by discussing the concept of human-robot interaction, introduce sonification, and give a brief overview of project-based learning. A detailed discussion of how the snake robot prototype was constructed and programmed, an in-depth explanation of the sonification algorithm that was used, and how sonification was taught in a high school classroom using PBL is presented along with student feedback and suggestions for future work. 

The development of Unmanned Aerial Vehicles (UAVs) can be enhanced through the use of sonification, an emerging field within Human-Robot Interaction (HRI). This dissertation introduces UAVSonification, a computational algorithm that maps simulation data to musical notes using the Musical Instrument Digital Interface (MIDI). By integrating UAVSonification with Formation Flight Simulation in Simulink, this study explores the sonification of UAV trajectories under environmental conditions. The function transforms simulation data into auditory signals, allowing users to discern key dynamics through sound. Specifically, the data series for multiple UAVs is mapped to piano notes via MIDI on MATLAB, providing auditory insights into UAV trajectories, environmental conditions, and control errors. A well-controlled flight path and stable heading controller produce harmonious sounds, while disruptions and deviations result in dissonance. UAVSonification offers a unique auditory approach to understanding UAV behavior in relation to control dynamics and environmental conditions. The sonification of UAVs has the potential to aid in the planning and analysis of UAV trajectories and controllers, as well as in creative endeavors. The effectiveness of the proposed method is shown through MATLAB numerical simulations. 

The Controller Area Network (CAN) is widely used in the automotive industry for its ability to create inexpensive and fast networks. However, it lacks an authentication scheme, making vehicles vulnerable to spoofing attacks. Evidence shows that attackers can remotely control vehicles, posing serious risks to passengers and pedestrians. Several strategies have been proposed to ensure CAN data integrity by identifying senders based on physical layer characteristics, but high computational costs limit their practical use. This paper presents a framework to efficiently identify CAN bus system senders by fingerprinting them. By modeling the CAN sender identification problem as an image classification task, the need for expensive handcrafted feature engineering is eliminated, improving accuracy using deep neural networks. Experimental results show the proposed methodology achieves a maximum identification accuracy of 98.34%, surpassing the state-of-the-art method’s 97.13%. The approach also significantly reduces computational costs, cutting data processing time by a factor of 27, making it feasible for real-time application in vehicles. When tested on an actual vehicle, the proposed methodology achieved a no-attack detection rate of 97.78% and an attack detection rate of 100%, resulting in a combined accuracy of 98.89%. These results highlight the framework’s potential to enhance vehicle cybersecurity by reliably and efficiently identifying CAN bus senders. 

The kinetostatic compliance method (KCM) models protein molecules as nanomechanisms consisting of numerous rigid peptide plane linkages. These linkages articulate with respect to each other through changes in the molecule dihedral angles, resulting in a kinematic mechanism with hyper degrees of freedom. Within the KCM framework, nonlinear interatomic forces drive protein folding by guiding the molecule’s dihedral angle vector towards its lowest energy state in a kinetostatic manner. This paper proposes a numerical integrator that is well suited to KCM-based protein folding and overcomes the limitations of traditional explicit Euler methods with fixed step size. Our proposed integration scheme is based on pseudo-transient continuation with an adaptive step size updating rule that can efficiently compute protein folding pathways, namely, the transient three-dimensional configurations of protein molecules during folding. Numerical simulations utilizing the KCM approach on protein backbones confirm the effectiveness of the proposed integrator. 

ABSTRACT Industrial robotic systems in the era of Industry 4.0 play a pivotal role in modern manufacturing. These systems, which belong to the larger class of cyber‐physical systems (CPSs), rely heavily on advanced sensing capabilities to execute complex and delicate tasks with high precision and efficiency. It is of no surprise that the integration of sensors with Industry 4.0 robotic systems exposes them to potential cyber‐physical risks/threats. This paper addresses a critical gap in the literature of industrial robotics cybersecurity by presenting a comprehensive analysis of vulnerabilities in the sensing systems of industrial robots. In particular, we systematically explore how sensor performance limits, faults and biases can be exploited by attackers who can then turn these inherent weaknesses into security threats. Our investigation relies on a detailed literature review of a multitude of commonly used sensors in industrial robotic systems through the lens of their physics‐based operating principles, classifications, performance limits, potential faults and associated vulnerabilities against disturbances such as temperature fluctuations, electromagnetic and acoustic interference, and ambient light variations. The result of this systematic investigation is a ring chart illustrating the overlaps and entanglements of sensor faults and performance limits, which can be exploited by cyber‐physical adversaries. Additionally, we investigate the cascading effects of compromised sensor data on the operation of industrial robotic systems through a cause‐and‐effect analysis, where the sensor vulnerabilities can cause malfunction and lead to cyber‐physical damage. The result of this analysis is a sensor cyber‐physical threat cause‐and‐effect diagram, which can be employed for design of robust and effective cyber‐physical defence measures. By providing insights into sensor‐related cyber‐risks, our cyber‐physical threat analysis paves the path for enhanced industrial robotics security. 

Despite the inherent need for enhancing human-robot interaction (HRI) by non-visually communicating robotic movements and intentions, the application of sonification (the translation of data into audible information) within the field of robotics remains underexplored. This paper investigates the problem of designing sonification algorithms that translate the motion of teams of industrial mobile robots to non-speech sounds. Our proposed solution leverages the wave space sonification (WSS) framework and utilizes localized wave fields with specific orientations within the system configuration space. This WSS-based algorithm generates sounds from the motion data of mobile robots so that the resulting audio exhibits a chosen timbre when the robots pass near designated configurations or move along desired directions. To demonstrate its versatility, the WSS-based sonification algorithm is applied to a team of OMRON LD series autonomous mobile robots, sonifying their motion patterns with pure tonal sounds. 

Considering the non-affine-in-control system governing the motion of a spherical particle trapped inside an optical tweezer, this paper investigates the problem of stabilization of the particle position at the origin through a control Lyapunov function (CLF) framework. The proposed CLF framework enables nonlinear optimization-based closed-loop control of position of tiny beads using optical tweezers and serves as a first step towards design of effective control algorithms for nanomanipulation of biomolecules. After deriving necessary and sufficient conditions for having smooth uniform CLFs for the optical tweezer control system under study, we present a static nonlinear programming problem (NLP) for generation of robustly stabilizing feedback control inputs. Furthermore, the NLP can be solved in real-time with no need for running computationally demanding algorithms. Numerical simulations demonstrate the effectiveness of the proposed control framework in the presence of external disturbances and initial bead positions that are located far away from the laser beam. 

Considering the non-affine-in-control system governing the motion of a spherical particle trapped inside an optical tweezer, this paper investigates the problem of stabilization of the particle position at the origin through a control Lyapunov function (CLF) framework. The proposed CLF framework enables nonlinear optimization-based closed-loop control of position of tiny beads using optical tweezers and serves as a first step towards design of effective control algorithms for nanomanipulation of biomolecules. After deriving necessary and sufficient conditions for having smooth uniform CLFs for the optical tweezer control system under study, we present a static nonlinear programming problem (NLP) for generation of robustly stabilizing feedback control inputs. Furthermore, the NLP can be solved in real-time with no need for running computationally demanding algorithms. Numerical simulations demonstrate the effectiveness of the proposed control framework in the presence of external disturbances and initial bead positions that are located far away from the laser beam.