In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via computational simulations, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and disinfectants.Note to Practitioners—This paper is motivated by the problem of COVID-19 infection spread in enclosed spaces but it also applies to other airborne pathogens. Airborne disease contagion often takes place in indoor environments; however, ventilation systems are almost never designed to take this into account so as to contain the spread of the pathogens. This is mainly because airflow design requires solving high-dimensional nonlinear partial differential equations known as Navier Stokes equations in fluid dynamics. In this paper, we propose a data-driven approach for solving the control problem of pathogen containment without solving the fluid dynamics equations. To this end, we first mathematically formulate the problem as an optimal control problem and then cast it as a reinforcement learning (RL) task. Reinforcement learning is the data-driven science of sequential decision-making and control in which the controller finds an optimal solution by systematic trial and error and without access to the system dynamics, i.e. fluid and pathogen dynamics in this paper. We employ an state-of-the-art RL algorithm, called PPO, to solve for optimal airflow in a room so as to minimize the exposure risk of occupants. Once it is calculated, the optimal airflow could be realized, via reverse engineering, by proper placement of the ventilation equipment, e.g. inlets, outlets, and fans. As an alternative to the airflow design, we use the same proposed data-driven techniques to find an optimal placement for pathogen disinfectants if there exists one, such as, hydrogen peroxide for COVID-19. Our results show the efficacy of our data-driven approach in designing an steady-state controller with full access to the system states. In future research, we will address the controller design with sparse measurements of the system states.

This Research Work in Progress Paper analyzes the process by which students start solving mechanical engineering problems. Participants of this study were given a problem and asked “what is the first step that you would take?” The participants' first steps to problems were categorized into bins, such as “simple technique” and “complex technique.” Problems that had a large starting problem space were chosen, so participants were not confined to one or two solution paths. The performance of undergraduate students, graduate students, and university faculty were compared. Information used from participants responses was used to analyze the relationship between technique used and participant expertise level.

In order to grasp and hold uneven or dusty objects, a robust adhesion mechanism is required. This paper evaluates controllable adhesion using magneto-rheological fluid (MRF) as a technique to stick to non-magnetic materials and rough/dusty surfaces. This technique is both simple to use and robust to uncertain surface conditions, as it involves applying MRF on a surface and activating it with a magnetic field. In this paper, we experimentally evaluate yield stresses in both normal and shear directions with respect to MRF layer thickness, magnetic flux density and surface type. Based on these results, a four-legged climbing robot is designed to demonstrate scaling vertical walls and shows effectiveness of the controllable adhesion using MRF for rough surface.

Hyper-redundant manipulators can be fragile, expensive, and limited in their flexibility due to the distributed and bulky actuators that are typically used to achieve the precision and degrees of freedom (DOFs) required. Here, a manipulator is proposed that is robust, high-force, low-cost, and highly articulated without employing traditional actuators mounted at the manipulator joints. Rather, local tunable stiffness is coupled with off-board spooler motors and tension cables to achieve complex manipulator configurations. Tunable stiffness is achieved by reversible jamming of granular media, which—by applying a vacuum to enclosed grains—causes the grains to transition between solid-like states and liquid-like ones. Experimental studies were conducted to identify grains with high strength-to-weight performance. A prototype of the manipulator is presented with performance analysis, with emphasis on speed, strength, and articulation. This novel design for a manipulator—and use of jamming for robotic applications in general—could greatly benefit applications such as human-safe robotics and systems in which robots need to exhibit high flexibility to conform to their environments.

A mobile system's maneuverability describes the scale and span of the velocities with which it can move. In this paper, we present a new geometric framework for describing the maneuverability of kinematic locomoting systems, inspired by the manipulability analysis of robotic arms. This framework describes both the local maneuverability in the neighborhood of each shape the system can assume and the cyclic maneuverability achieved by executing gaits from a library. Additionally, the gait-level analysis includes tools that direct the search for gaits whose inclusion into the library will usefully improve the maneuverability. Throughout, we provide examples based on a swimming system operating at low Reynolds number.

Razor clams (Ensis directus) are one of nature's most adept burrowing organisms, able to dig to 70cm at nearly 1cm/s using only 0.21J/cm. We discovered that Ensis reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. We have developed RoboClam, a robot that digs using the same mechanisms as Ensis, to explore how localized fluidization burrowing can be extended to engineering applications. In this work we present burrowing performance results of RoboClam in Ensis' habitat. Using a genetic algorithm to optimize RoboClam's kinematics, the machine was able to burrow at speeds comparable to Ensis, with a power law relationship between digging energy and depth of n = 1.17, close to the n = 1 achieved by the animal. Pushing through static soil has a theoretical energy-depth power law of n = 2, which means that Ensis-inspired digging motions can provide exponential energetic savings over existing burrowing methods.