An external electromagnet plus moving PM (permanent magnet) FPCB (flexible printed circuit board) micromirror is proposed in this paper that can overcome two limitations associated with the previous FPCB micromirror with a configuration of an external PM plus moving coil, i.e., (1) it reduces the overall width beyond the mirror plate, and (2) increases the maximum rotation angle. The micromirror has two external electromagnets underneath an FPCB structure (two torsion beams and a middle seat) with two moving PM discs attached to the back and a metal-coated mirror plate bonded to the front of the FPCB middle seat. Modeling and simulation were introduced, and the prototype was fabricated and tested to verify the design. The achieved performance was better than that of the previous design: a maximum resonant rotation angle of 62° (optical) at a driving voltage of ±3 V with a frequency of 191 Hz, the required extra width beyond the mirror plate was 6 mm, and an aperture of 8 mm × 5.5 mm with a roughness of <10 nm and a flatness of >10 m (ROC, radius of curvature). The previous FPCB micromirror’s performance was: strain limited maximum rotation angle was 40° (optical), the extra width beyond the mirror plate was 14.7 mm, and had an aperture of 4 mm × 4 mm with a similar roughness and flatness.

No abstract

The COVID-19 pandemic has demonstrated the need for a more effective and efficient disinfection approach to combat infectious diseases. Ultraviolet germicidal irradiation (UVGI) is a proven mean for disinfection and sterilization and has been integrated into handheld devices and autonomous mobile robots. Existing UVGI robots which are commonly equipped with uncovered lamps that emit intense ultraviolet radiation suffer from: inability to be used in human presence, shadowing of objects, and long disinfection time. These robots also have a high operational cost. This paper introduces a cost effective germicidal system that utilizes UVGI to disinfect pathogens, such as viruses, bacteria, and fungi, on high contact surfaces (e.g. doors and tables). This system is composed of a team of 5-DOF mobile manipulators with end-effectors that are equipped with far-UVC excimer lamps. The design of the system is discussed with emphasis on path planning, coverage planning, and scene understanding. Evaluations of the UVGI system using simulations and irradiance models are also included. Please see the project's website for videos and simulations of the robot.

The objective of pose SLAM or pose-graph optimization (PGO) is to estimate the trajectory of a robot given odometric and loop closing constraints. State-of-the-art iterative approaches typically involve the linearization of a non-convex objective function and then repeatedly solve a set of normal equations. Furthermore, these methods may converge to a local minima yielding sub-optimal results. In this work, we present to the best of our knowledge the first Deep Reinforcement Learning (DRL) based environment and proposed agent for 2D pose-graph optimization. We demonstrate that the pose-graph optimization problem can be modeled as a partially observable Markov Decision Process and evaluate performance on realworld and synthetic datasets. The proposed agent outperforms state-of-the-art solver g 2 o on challenging instances where traditional nonlinear least-squares techniques may fail or converge to unsatisfactory solutions. Experimental results indicate that iterative-based solvers bootstrapped with the proposed approach allow for significantly higher quality estimations. We believe that reinforcement learning-based PGO is a promising avenue to further accelerate research towards globally optimal algorithms. Thus, our work paves the way to new optimization strategies in the 2D pose SLAM domain.