Microfluidic biochips are being utilized for clinical diagnostics, including COVID-19 testing, because of they provide sample-to-result turnaround at low cost. Recently, microelectrode-dot-array (MEDA) biochips have been proposed to advance microfluidics technology. A MEDA biochip manipulates droplets of nano/picoliter volumes to automatically execute biochemical protocols. During bioassay execution, droplets are transported in parallel to achieve high-throughput outcomes. However, a major concern associated with the use of MEDA biochips is microelectrode degradation over time. Recent work has shown that formulating droplet transportation as a reinforcement-learning (RL) problem enables the training of policies to capture the underlying health conditions of microelectrodes and ensure reliable fluidic operations. However, the above RL-based approach suffers from two key limitations: 1) it cannot be used for concurrent transportation of multiple droplets; 2) it requires the availability of CCD cameras for monitoring droplet movement. To overcome these problems, we present a multi-agent reinforcement learning (MARL) droplet-routing solution that can be used for various sizes of MEDA biochips with integrated sensors, and we demonstrate the reliable execution of a serial-dilution bioassay with the MARL droplet router on a fabricated MEDA biochip. To facilitate further research, we also present a simulation environment based on the PettingZoo Gym Interface for MARL-guided droplet-routing problems on MEDA biochips.

Unmanned Aerial Vehicles (UAVs), also known as drones, have extensive applications in civilian rescue and military surveillance realms. A common drone control scheme among such applications is human supervisory control, in which human operators remotely navigate drones and direct them to conduct high-level tasks. However, different levels of autonomy in the control system and different operator training processes may affect operators’ performance in task success rate and efficiency. An experiment was designed and conducted to investigate such potential impacts. The results showed us that a dedicated supervisory drone control interface tended toward increased operator successful task completion as compared to an enhanced teleoperation control interface, although this difference was not statistically significant. In addition, using Hidden Markov Models, operator behavior models were developed to further study the impact of operators’ drone control strategies as a function of differing levels of autonomy. These models revealed that people with both supervisory and enhanced teleoperation control training were not able to determine the right control action at the right time to the same degree that people with just training in the supervisory control mode. Future work is needed to determine how trust plays a role in such settings.