Autonomous robots are commonly operated on rough roads. Thus, it is essential to predict their dynamic characteristics. Even though it is possible to use real hardware to acquire a robot’s dynamic characteristics, this requires a significant amount of time and cost. Therefore, a real-time remote driving simulator must be developed to reduce these risks. Most real-time simulators employ physics engines, which are calculated using simple functional expressions based on particles. However, in this case, there is a limit to reflecting the dynamic characteristics of actual robots. In this study, a multi-body dynamic model of a robot was established. MATLAB Simulink was used to connect the vehicle model with the joystick and calculate user input signals. The PID control system determines the driving torque of the robot to satisfy the calculated signal. Gain value optimization is performed to enable real-time control. This study can be available to analyze the traversability.

The objective of this study was to present a rotary manipulating system driven by a rotary actuator based on twisted shape memory alloy (SMA) wires. The rotary actuator was composed of two oppositely twisted SMA wires connecting a rotor and a stator through a shaft. Two oppositely twisted SMA wires could generate bidirectional rotary motions upon actuation of each twisted SMA wire corresponding to the direction against the twist direction of each SMA wire. A manipulator was designed and fabricated by integrating manipulating arms, the rotary actuator, and a Hall effect magnetic rotary encoder which could measure the angular position of the rotary motion. We modeled and characterized the manipulator upon application of a ramp current input to each twisted SMA wire. A proportional-integral-derivative (PID) controller was designed and implemented to control the proposed rotary manipulator. Reference angular position tracking performances of the manipulator were evaluated with a series of experiments.

The need for automated material handling inside the factory has been steadily increasing, especially due to implementation of intelligent manufacturing for better productivity and product quality. Automated material handling devices include logistics robots, automated guided vehicles, industrial robots, collaborative robots, and pick-and-place devices. This study focuses on the development of a low-cost logistics robot that works effectively within a simulated smart factory environment. A nominal PID controller is implemented to guide the robot to follow the line painted on the factory floor. The tracking error information is generated by four down-facing infrared sensors and is fed into the controller. The line-following performance is significantly improved with augmentation of a model-based friction compensator. Optimization of battery power depending on the remaining charge status enhances the reliability. All hardware/software development is supported by the Arduino platform. The step-by-step movement and performance of the logistics robot is verified inside the simulated smart factory environment that includes a robot arm, three conveyors, and two processing stations.

The goal of this study is to develop a fast, controllable PZT-driven depth adjustment device with a flexure hinge. The device can be used to trace rapidly a flat or curved surface with several hundreds of micrometers’ variance in height. The lever type flexure hinge designed for a magnification ratio of 10 and no other axes motion has been confirmed through FEM analysis; the actual performance has been verified through static/dynamic experiments. A micro-depth control system, which is comprised of a DAQ with a LabVIEW, PZT amplifier, PZT actuator, flexure hinge, and laser displacement sensor, is implemented, and its static/dynamic characteristics of depth control is investigated with a PID gain tuned control algorithm on LabVIEW. It has been verified that the developed device can trace a micro-depth command as fast as 0.5 s to get an accurate position of 0.1 μm, even under a load of 1 N.