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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. 

The safety-critical nature of vehicle steering is one of the main motivations for exploring the space of possible cyber-physical attacks against the steering systems of modern vehicles. This paper investigates the adversarial capabilities for destabilizing the interaction dynamics between human drivers and vehicle haptic shared control (HSC) steering systems. In contrast to the conventional robotics literature, where the main objective is to render the human-automation interaction dynamics stable by ensuring passivity, this paper takes the exact opposite route. In particular, to investigate the damaging capabilities of a successful cyber-physical attack, this paper demonstrates that an attacker who targets the HSC steering system can destabilize the interaction dynamics between the human driver and the vehicle HSC steering system through synthesis of time-varying impedance profiles. Specifically, it is shown that the adversary can utilize a properly designed non-passive and time-varying adversarial impedance target dynamics, which are fed with a linear combination of the human driver and the steering column torques. Using these target dynamics, it is possible for the adversary to generate in realtime a reference angular command for the driver input device and the directional control steering assembly of the vehicle. Furthermore, it is shown that the adversary can make the steering wheel and the vehicle steering column angular positions to follow the reference command generated by the time-varying impedance target dynamics using proper adaptive control strategies. Numerical simulations demonstrate the effectiveness of such time-varying impedance attacks, which result in a non-passive and inherently unstable interaction between the driver and the HSC steering system. 

This paper investigates development of an efficient numerical integrator for forward dynamics simulation of the protein folding process, where protein molecules are modeled as robotic mechanisms consisting of rigid nano-linkages with many degrees-of-freedom. To address the computational burden associated with fixed step-size explicit Euler methods, we develop a fast numerical scheme with an adaptive step-size strategy for computing the folding pathway of protein molecules. 

There are a variety of ways, such as reflashing of targeted electronic control units (ECUs) to hijacking the control of a fleet of wheeled mobile robots, through which adversaries can execute attacks on the actuators of mobile robots and autonomous vehicles. Independent of the source of cyber-physical infiltration, assessing the physical capabilities of an adversary who has made it to the last stage and is directly controlling the cyber-physical system actuators is of crucial importance. This paper investigates the potentials of an adversary who can directly manipulate the traction dynamics of wheeled mobile robots and autonomous vehicles but has a very limited knowledge of the physical parameters of the traction dynamics. It is shown that the adversary can exploit a new class of closed-loop attack policies that can be executed against the traction dynamics leading to wheel lock conditions. In comparison with a previously proposed wheel lock closed-loop attack policy, the attack policy in this paper relies on less computations and knowledge of the traction dynamics. Furthermore, the proposed attack policy generates smooth actuator input signals and is thus harder to detect. Simulation results using various tire-ground interaction conditions demonstrate the effectiveness of the proposed wheel lock attack policy. 

Increasingly, instructors are challenged by growing complexity in knowledge domains and the need to prepare students with specific skills relevant to an uncertain future. The speed of technological advance and shifting societal conditions make this ever more arduous. One of the promises of project-based learning (PBL) is to cultivate many of the most important student qualities for facing such an uncertain world by exposing them to cross disciplinary problems. Indeed, providing the students with a plethora of perspectives from seemingly unrelated fields enhances their creative problem solving skills and enables them to better adapt to complex scenarios. This paper describes a multidisciplinary effort between faculty from the Electrical and Computer Engineering department at the University of Michigan-Dearborn and the Department of Chemistry and Biochemistry at the Worcester Polytechnic Institute (WPI). The project involved students modeling protein folding as a robotic mechanism and studying the problems associated with this complex system from multiple perspectives. After providing a brief technical background about the robotics-based approaches to the problem of protein folding/unfolding, this paper elaborates on the pedagogical elements of the project. Assessment results highlight the student learning outcomes and perspectives on this interdisciplinary, and intercollegiate project-based learning endeavor. The authors comment on challenges and opportunities associated with such PBL efforts and provide suggestions for disseminating these types of impactful PBL initiatives. 

This paper investigates the problem of prediction of protein molecule folding pathways under entropy-loss constraints by formulating a control synthesis problem whose solutions are obtained by solving large-scale quadratic programming (QP) optimizations with nonlinear constraints. The utilized non-iterative and computationally efficient algorithm, which is based on solving generalized eigenvalue problems, prevents an unpredictable and potentially large number of iterations at each protein conformation for computing the folding control inputs. The synthesized control inputs remain close to the renowned kinetostatic compliance method (KCM) reference vector field while satisfying proper quadratic inequality constraints that limit the rate of molecule entropy-loss during folding. 

This paper investigates the problem of prediction of protein molecule folding pathways under entropy-loss constraints by formulating a control synthesis problem whose solutions are obtained by solving large-scale quadratic programming (QP) optimizations with nonlinear constraints. The utilized non-iterative and computationally efficient algorithm, which is based on solving generalized eigenvalue problems, prevents an unpredictable and potentially large number of iterations at each protein conformation for computing the folding control inputs. The synthesized control inputs remain close to the renowned kinetostatic compliance method (KCM) reference vector field while satisfying proper quadratic inequality constraints that limit the rate of molecule entropy-loss during folding.